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This work was supported in part by ITEA3 Unleash Potentials in Simulation (UPSIM) by the German Federal Ministry of Education and Research (BMBF) under Project 19006, in part by the Austrian Research Promotion Agency (FFG), in part by the Rijksdienst voor Ondernemend Nederland (Rvo), and in part by the Innovation Fund Denmark (IFD)., Technological advances and scalability are leading Human-Computer Interaction (HCI) to evolve towards intuitive forms, such as through gesture recognition. Among the various interaction strategies, radar-based recognition is emerging as a touchless, privacy-secure, and versatile solution in different environmental conditions. Classical radar-based gesture HCI solutions involve deep learning but require training on large and varied datasets to achieve robust prediction. Innovative self-learning algorithms can help tackling this problem by recognizing patterns and adapt from similar contexts. Yet, such approaches are often computationally expensive and hardly integrable into hardware-constrained solutions. In this paper, we present a gesture recognition algorithm which is easily adaptable to new users and contexts. We exploit an optimization-based meta-learning approach to enable gesture recognition in learning sequences. This method targets at learning the best possible initialization of the model parameters, simplifying training on new contexts when small amounts of data are available. The reduction in computational cost is achieved by processing the radar sensed data of gestures in the form of time maps, to minimize the input data size. This approach enables the adaptation of simple convolutional neural network (CNN) to new hand poses, thus easing the integration of the model into a hardware-constrained platform. Moreover, the use of a Variational Autoencoders (VAE) to reduce the gestures' dimensionality leads to a model size decrease of an order of magnitude and to half of the required adaptation time. The proposed framework, deployed on the Intel(R) Neural Compute Stick 2 (NCS 2), leads to an average accuracy of around 84% for unseen gestures when only one example per class is utilized at training time. The accuracy increases up to 92.6% and 94.2% when three and five samples per class are used., Federal Ministry of Education & Research (BMBF) 19006, Austrian Research Promotion Agency (FFG), Rijksdienst voor Ondernemend Nederland (Rvo), Innovation Fund Denmark (IFD)

Spectrum sharing in radar bands with interference forecasting for enhanced radar protection can help design proactive resource allocation solutions which can achieve high data rates for wireless communication networks on one hand and help protect the incumbent radar systems. We consider radar spectrum sharing in 5.6GHz where a weather radar operates as a primary system and the dominant secondary system is an enterprise network consisting of access points (APs) in a university campus. Our work models transmit the power of the APs as a time series with multinomial distribution based on real collected data. The aggregated interference due to the transmissions from the APs at the radar is forecasted using a long short-term memory (LSTM) based neural network. Monte Carlo dropout is utilized to generate prediction intervals that capture the uncertainties in the interference from the APs. Finally, by using both average and upper limits of predicted interference time series a cloud-assisted efficient sharing and radar protection algorithm is proposed. Tracking the rotating radar is not required in the proposed system. The results show that the proposed efficient sharing and radar protection system ensures better radar protection and increased throughput for wireless communication users.

Polysomnography (PSG) is the gold-standard for sleep apnea and hypopnea syndrome (SAHS) diagnosis. Because the PSG system is not suitable for long-term continuous use owing to the high cost and discomfort caused by attached multi-channel sensors, alternative methods using a non-contact sensor have been investigated. However, the existing methods have limitations in that the radar-person distance is fixed, and the detected apnea hypopnea (AH) event cannot be provided in real-time. In this paper, therefore, we propose a novel approach for real-time AH event detection with impulse-radio ultra-wideband (IR-UWB) radar using a deep learning model. 36 PSG recordings and simultaneously measured IR-UWB radar data were used in the experiments. After the clutter was removed, IR-UWB radar images were segmented by sliding a 20-s window at 1-s shift, and categorized into two classes: AH and N. A hybrid model combining the convolutional neural networks and long short-term memory networks was trained with the data, which consisted of class-balanced segments. Time sequenced classified outputs were then fed to an event detector to identify valid AH events. Therefore, the proposed method showed a Cohen’s kappa coefficient of 0.728, sensitivity of 0.781, specificity of 0.956, and an accuracy of 0.930. According to the apnea-hypopnea index (AHI) estimation analysis, the Pearson’s correlation coefficient between the estimated AHI and reference AHI was 0.97. In addition, the average accuracy and kappa of SAHS diagnosis was 0.98 and 0.96, respectively, for AHI cutoffs of ≥ 5, 15, and 30 events/h. The proposed method achieved the state-of-the-art performance for classifying SAHS severity without any hand-engineered feature regardless of the user’s location. Our approach can be utilized for a cost-effective and reliable SAHS monitoring system in a home environment.

The ground penetrating radar (GPR) data in the complex detection environment is non-stationary, non-Gaussian, and non-uniform, so the traditional noise attenuation methods are difficult to meet the requirements of denoising. Therefore, we introduced the K-singular value decomposition (K-SVD) dictionary learning into the denoising of GPR signals. It uses the orthogonal matching pursuit (OMP) algorithm to sparse decompose different radar data and trains the overcomplete dictionary with sample characteristics. K-SVD makes full use of the prior information and can extract features according to the sample data adaptively, which means it has strong sparse representation competence. Because radar signals can be sparsely represented in the dictionary, whereas the random noise does not have a sparse representation, the K-SVD dictionary can be used to distinguish effective signals from noise in the GPR data. We used the discrete cosine transform (DCT) and K-SVD dictionaries to process the Gaussian noise and stochastic clutter in the GPR profile. The results show that both K-SVD and DCT can effectively suppress the Gaussian noise. But for the clutter generated by the random medium, the DCT dictionary also causes damage to the effective signals while removing the noise; whereas the K-SVD dictionary learning algorithm uses the DCT dictionary as the initial dictionary and carries out adaptive learning on the noisy data, considering the information in the block and the global observation to complete the denoising, with good denoising effect and high fidelity. We finally verified the effectiveness and practicability of the K-SVD method for measured data.

In this paper, a wide-angle frequency-scanning array antenna is presented for three-dimensional (3D) radar. Initially, a modified composite right/left-handed (CRLH) unit cell based on a dual-layer finger-connected interdigital capacitor (FCIDC) is analyzed. Compared with its conventional counterpart, the proposed unit cell eliminates the undesired high-order resonances and has a lower right/left-handed passband. Then, a broadband frequency-scanning feeding network, which has a large scanning angle, is designed using the proposed CRLH unit cell. The responses of the four output ports indicate that the network can offer desired amplitude and phase difference to scan towards both negative and positive elevation angles. To validate its performance, a tightly coupled planar quasi-Yagi antenna array is fed to synthesize an array antenna. The measured results show that the designed array antenna has an impedance bandwidth ($\vert \text{S}_{11}\vert

This research proposes a compact quadri-cluster broadband circularly polarized (CP) sequentially-rotated metasurface-based (MTS) antenna array for the C-band frequency spectrum. One cluster of the quadri-cluster MTS-based antenna array consisted of 4 $\times $ 4 S-shaped periodically-arranged MTS elements. The sequentially-rotated feed network was utilized to realize circular polarization and improve the impedance bandwidth (IBW), 3-dB axial ratio bandwidth (ARBW) and 3-dB boresight gain bandwidth of the quadri-cluster MTS-based antenna array. Simulations were performed and results were compared with experiments. The measured IBW and ARBW were 84.74% (4.0–9.0 GHz) and 57.6% (4.2–7.6 GHz) at the center frequency of 5.9 GHz, rendering the proposed quadri-cluster MTS-based antenna array suitable for satellite communication applications. In addition, the quadri-cluster MTS-based antenna array achieved the measured 3-dB boresight gain bandwidth of 81.3% (3.9–8.7 GHz), the maximum gain of 10.04 dBic at 5.6 GHz, and low radar cross-section. Specifically, the novelty of this research lies in the use of the sequentially-rotated feed network with the S-shaped MTS elements to effectively enhance ARBW of the quadri-cluster MTS-based antenna array for the C-band frequency spectrum.

Synthetic Aperture Radar (SAR) simulation is widely used for system design, processing techniques development, and mission planning. However, there is no readily available and free framework for SAR raw signal simulation, and many teams and organizations struggle with developing their own simulator from scratch, repeating work that others have done earlier. This work’s purpose is to create a simple, open-source SAR simulation framework that can be used for many purposes and is available for free to everyone. It is a ready tool for SAR processing techniques verification, and thanks to its open-source nature, it fosters collaboration between scientists, both on the simulation and the tool development. Moreover, as the simulation’s underlying mathematics is described in detail in this article, it may serve as a handbook for the radar’s simulation. The simulator was prepared as a stand-alone, multiplatform software working in the Matlab/Octave environment. It is available in an online software repository that allows others to contribute to the original code or to create forks of it. The simulator supports monostatic, bistatic, and multistatic configurations with shadowing. The surface model includes roughness using the modified Phong model, transparency, and complex reflectivity. The antenna pattern is defined with a general two-dimensional model. The article is concluded with the simulator’s demonstrations, including antenna pattern, Doppler shift, and various surface parameters. It is expected that this simulator will be widely adopted and improved by many collaborators.

Attention mechanisms have been widely integrated with various neural networks to boost performance. However, when an attention mechanism was applied to a radar ego-velocity estimation network, the importance of carefully handling the amplitude and phase of complex-valued tensor was revealed. Therefore, in this study, we present a self-attention mechanism designed to handle complex-valued tensors in order to capture the rich contextual relationships implied within amplitude and phase. To exploit the advantages of complex-valued attention ( $\mathbb {C}\text{A}$ ), we evaluated its impact while performing ego-velocity estimation tasks based on radar data, whose amplitude and phase are related to the electromagnetic scattering of the target being observed. Radars are suitable sensors for such tasks as they are capable of long-range detection and instantaneous velocity measurement under variable weather and lighting conditions. In particular, we coupled our $\mathbb {C}\text{A}$ module with complex-valued neural networks, known to be particularly powerful for handling wave phenomena. The proposed method exhibits robust estimation performance, regardless of whether the Doppler ambiguity problem occurs and eliminates the dependence on preprocessing stages, including target detection and static target indication. Furthermore, it achieves improved stability during training via geometrical constraint regularization, and implicitly allows velocity conversion between the sensor and vehicle frames, even if the mount information of the sensor was not provided. Finally, ablation experiments conducted on extensive real-world datasets show noticeable improvement in estimation performance.

Adaptive beamsteering cognitive radar (AB-CRr) systems seek to improve detection and tracking performance by formulating a beam placement strategy adapted to their environment. AB-CRr builds a probabilistic model of the target environment that enables it to more efficiently employ its limited resources to locate and track targets. In this work, we investigate methods for adapting the AB-CRr framework to detect and track large target swarms. This is achieved by integrating the properties of correlated-motion swarms into both the radar tracking model and AB-CRr’s underlying dynamic probability model. As a result, a list of newly CRr-integrated contributions are enumerated: a) improved uncertainty function design, b) incorporates Mahalanobis nearest neighbors multi-target association methodology into AB-CRr, c) introduces a novel Kalman-based consolidated swarm tracking methodology with a common velocity state vector that frames targets as a correlated collection of swarm members, d) introduces an improved uncertainty growth model for updating environment probability map, e) introduces a method for incorporating estimated swarm structure and behavior into the uncertainty update model referred to as “track hinting”, and f) introduces new metrics for swarm search/detection and tracking called swarm centroid track error and swarm tracking dwell ratio. The results demonstrate that AB-CRr is capable of adapting its beamsteering strategy to efficiently perform resource balancing between target search and swarm tracking applications, while taking advantage of group structure and intra-swarm target correlation to resist large swarms overloading available resources.

In this paper, we investigate novel data collection and training techniques towards improving classification accuracy of non-moving (static) hand gestures using a convolutional neural network (CNN) and frequency-modulated-continuous-wave (FMCW) millimeter-wave (mmWave) radars. Recently, non-contact hand pose and static gesture recognition have received considerable attention in many applications ranging from human-computer interaction (HCI), augmented/virtual reality (AR/VR), and even therapeutic range of motion for medical applications. While most current solutions rely on optical or depth cameras, these methods require ideal lighting and temperature conditions. mmWave radar devices have recently emerged as a promising alternative offering low-cost system-on-chip sensors whose output signals contain precise spatial information even in non-ideal imaging conditions. Additionally, deep convolutional neural networks have been employed extensively in image recognition by learning both feature extraction and classification simultaneously. However, little work has been done towards static gesture recognition using mmWave radars and CNNs due to the difficulty involved in extracting meaningful features from the radar return signal, and the results are inferior compared with dynamic gesture classification. This article presents an efficient data collection approach and a novel technique for deep CNN training by introducing ``sterile'' images which aid in distinguishing distinct features among the static gestures and subsequently improve the classification accuracy. Applying the proposed data collection and training methods yields an increase in classification rate of static hand gestures from $85\%$ to $93\%$ and $90\%$ to $95\%$ for range and range-angle profiles, respectively., Comment: Accepted to IEEE Access

As an important part of electronic warfare, radar countermeasure determines the trend of war to a large extent. Modern radar jamming technology, especially deception jamming technology, plays an increasingly important role. Therefore, how to identify radar deception jamming is very necessary. In this paper, a feature fusion algorithm based on Bayesian decision theory is used to recognize radar deception jamming signals. Firstly, the real echo signal, deception jamming signal (contains range gate pull-off jamming, velocity gate pull-off jamming and range-velocity gate pull-off jamming) and noise signal received by radar are acquired as signal sources. Then bispectrum transformation is used to extract features in several aspects. Finally, kernel density estimation is used to improve the fusion algorithm, and the feature fusion algorithm based on Bayesian decision theory is used to recognize the received signals from radar. Results of the experiment indicate that the algorithm not only can recognize the radar deception jamming, but also has high accuracy.

This paper proposes a classification algorithm utilizing an open set recognition concept to conservatively detect lane change intention of surrounding vehicles. Conservatively predicting the lane change intention of the surrounding vehicles is needed to improve adaptive cruise control (ACC) performance and avoid possible accidents. However, existing machine learning can make incorrect decisions due to information not included in the training data set or confused data even with probability. To cope with this problem, we present a classification algorithm using a multi-class support vector machine applying an open set recognition concept to detect the surrounding vehicles’ lane change intentions. Feature vectors are constructed from lateral information obtained by a Kalman filter using only radar and in-vehicle sensors. The open set recognition concept is adapted using Meta-Recognition based on binary classifiers scores. Furthermore, we analyze lateral information where an object vehicle changes lanes. From experimental results, we observe that the proposed system conservatively deals with wrong decisions and detects and cancels detecting the closest in-path vehicle (CIPV) earlier with average times of 1.4 sec and 0.4 sec compared with a commercial radar system, respectively.

In this article, we propose a method to monitor traffic on the road using a 24.1 GHz radar system. In our work, we design our own traffic monitoring radar system from radar hardware, such as antenna systems and the transceiver, to radar software, such as detection and tracking algorithms. The radar we developed uses frequency-modulated continuous waveforms with a center frequency of 24.1 GHz, and its range resolution is tens of centimeters, which is enough to distinguish vehicles in multiple lanes. In addition, we can effectively distinguish vehicles by estimating their angles using an array antenna system. To evaluate the performance of our radar, we install it on the pedestrian overpass facing the road. From actual measurement results, our radar system can reliably estimate the distance to the vehicle, the speed of the vehicle, and the angle of the vehicle for a total of four lanes up to 300 m in real time. The traffic monitoring system using a radar sensor can compensate for the disadvantages of the camera-based road monitoring method.

Identifying the vital states of trapped survivors during post-disaster rescue missions can result in improved rescue strategies and provide injury pre-diagnosis information. The most effective rescue method is the use of bio-radar based non-contact measurements. Presently, bio-radar techniques focus on detecting and locating. Herein, a method to identify vital states with an ultra-wideband bio-radar is proposed, while simulating a trapped condition with Beagle dogs. This investigation revealed three vital stages under the trapped condition: normal, transitioning, and agonal stages. Upon entering the transitioning stage, the heartrates were apparently high, and the respiratory rates increased sharply. The temperatures dropped rapidly once passing this stage. In particular, the respiratory waveforms from the bio-radar frequently change from a normal sine like curve to an “M” like curve within the transitioning stage. The accurate beginning and ending of the transitioning stage are defined by a newly proposed indicator of relative occurrence frequency. Pathological observations indicated that the fragmentation of lamellar bodies within type II alveolar cells caused the insufficiency of the lung surfactant, and further resulted in the occurrence of the “M” like curves. This pioneering work realizes the vital states identification only using a non-contact ultra-wideband bio-radar, thereby enables to infer the health conditions, life expectancy, and appropriate subsequent treatment of victims in the trapped condition. Therefore, it has the potential to promote the welfare of post-disaster trapped human victims.

In a multiple-input multiple-output (MIMO) radar system, binary phase-shift keying (BPSK) modulation is widely used to separate signals transmitted from multiple transmit (Tx) antennas. However, when the BPSK modulation is applied to the frequency-modulated continuous-wave (FMCW) radar system, there is an occurrence of Doppler-angle ambiguity due to the properties of the modulation scheme. In other words, the maximum detectable velocity is reduced in proportion to the number of Tx antennas, and the angle estimation performance can be degraded when a moving target induces a Doppler phase shift. Therefore, in this paper, we propose a method for solving the Doppler-angle ambiguity in the BPSK-MIMO FMCW radar system. The proposed method involves comparing and arranging the phases of signals corresponding to the same target in the range-velocity detection results. By arranging the phases to form an arithmetic sequence, the signals from each Tx antenna can be distinguished and the unambiguous velocity and angle of the target can be estimated. In addition, by using a general phase-shift keying (PSK) modulation scheme, the proposed method can be applied to an arbitrary MIMO system regardless of the number of Tx antennas.

This paper investigates the problem of unmanned aerial vehicle (UAV) recognition in the presence of WiFi interference using passive radio frequency (RF) detection. The proposed method relies on machine learning based RF recognition and considers the scenario in the bandwidth of the video signal (VS) and WiFi are identical. Our machine learning strategy involves extracting 31 features from the WiFi signal and the UAV VS, which are then input to the classifier. Among the 31 features, 30 are statistical in the time and frequency domain, while the remaining one involves the effective subcarrier feature. We evaluate four different machine learning (ML) classifier variants and demonstrate through simulation and experiments that the proposed method can accurately recognize UAV VS in the presence of WiFi interference. We also improve the feature-vector compactness and reduce the 31-feature vector to a 6-feature vector composed of the most significant features and demonstrate that the recognition performance of random forest method (RandF) classifier is not compromised. The most appealing performance for the 6-feature vector case is attained by the RandF, managing a recognition rate of 100% in the indoor experiment and 96.26% in the 2 km outdoor experiment. The recognition rate of the four ML classifiers is larger than 95.52% in the 2 km outdoor experiment, which is better than other UAV detection methods such as radar, acoustic and video.

Distinguishing humans from animals using ultra-wideband (UWB) radar is necessary in post-disaster emergency rescues to prioritize and thereby optimize the distribution of labor and material resources. However, current studies are few and have only been implemented in simple laboratory environments, such that the effectiveness of these approaches cannot be guaranteed in rescue situations. This study describes experiments under actual post-disaster emergency rescue scenarios, for which the signal-to-noise ratio of UWB radar is seriously degraded owing to multipath effects and a complicated ruin environment. Four distinguishing features are extracted from aspects of wavelet entropy, correlation coefficient, and energy to classify humans from animals. Analysis of feature effectiveness showed that each feature could identify humans from animals individually. The largest difference between humans and animals was found in a feature which combines advantages of the correlation coefficient and energy simultaneously. There was no overlap between the human and animal values for this feature among the 20 sets of radar data collected. This is the first attempt to distinguish humans from animals in an actual post-disaster trapped condition, and it yielded four features of strong classification ability. We envision this study to advance real-world applicability of UWB radar in post-disaster emergency rescue.

We develop a new method for characterizing the lift force on a baseball. The methodology addresses this task from the novel perspective of considering a large set of radar measurements acquired outside of a laboratory setting. The reduced degree of standardization in the measurements is countered by several elements of the approach. A new optimization method is developed that incorporates domain knowledge and constraints derived from optical measurements. The optimization accounts for the uncertainty in the different data sources while exploiting the size and diversity of the radar measurements to mitigate the effects of systematic biases, outliers, and the lack of geometric information that is typically available in laboratory experiments. Fine-grained weather data is associated with each radar measurement to enable compensation for the local air density. By applying this methodology to a set of over two million trajectory measurements, we achieve unprecedented accuracy in the characterization of the lift force. We show that the lift coefficient is more than six percent greater than measured by previous laboratory experiments. We also demonstrate the ability to predict increases in the lift coefficient in response to changes in seam height on the order of a thousandth of an inch. Previous methods based on smaller sets of laboratory measurements have been unable to discern changes in the lift coefficient in response to changes in seam height of 0.02 inches. We demonstrate the statistical significance of the results. This work benefits several important application areas including the monitoring of sensor calibration systems and the definition of ball specifications that constrain trajectories to acceptable ranges.

Missiles have been playing an important role in modern wars. In order to gain advantage in missile contests, the technology of missile interception have been developed for many years. However, constrained by current technology, it is still very difficult to intercept successfully. Thus, it may need to optimize the allocation of intercept missiles to prevent attack missiles from hitting targets. This paper proposes a method to optimize the missile interception strategy (intercept missile allocation) by simulating the games of missile interception, which jointly considering multiple phases, multiple targets, cost and the risk attitude of the defender. This method is developed based on combinatorial theory and cumulative prospect theory. There are two optimization goals considered: the cost and the prospect value. We studied two cases where the detection of attack missiles is perfect or imperfect respectively. Four illustrative examples are presented to illustrate the proposed method and several directions of research in future are pointed.

This paper considers the problem of estimating the motion parameters of a low-observable high-speed maneuvering target with high-order motions, where the target’s complex motions will result in the range migration (RM) and Doppler frequency migration (DFM) effects within the observation time interval, which will significantly affect the radar performance. Based on the Hough transform (HT) and the polynomial Chirplet transform (PCT), a novel method for the motion parameter estimation, which we coin as HPCT, is proposed in this paper. It first employs the trajectory feature extracted from the image of range profiles by the HT to correct the first-order RM and resolve the Doppler ambiguity. Then, it compensates the residual range curvature and the DFM with the motion parameters estimated from the time-frequency ridge, which is extracted from the highly concentrated time-frequency representation (TFR) generated by the PCT. After that, the energy of radar returns can be coherently integrated along the target’s moving trajectory. In addition, we evaluate the parameter estimation performance, integration performance and computational cost of the proposed method via theoretical analysis or numerical experiments. The results show that in terms of the anti-noise ability, cross-term interference avoidance, measurement accuracy and computational complexity, the HPCT is superior to some common methods for the motion parameter estimation. Finally, we extend it to the scenario of multiple targets with different motion orders.

In this paper, we propose to produce synthesized micro-Doppler signatures from different aspect angles through conditional generative adversarial networks (cGANs). Micro-Doppler signatures of non-rigid human body motions vary considerably as a function of the radar’s aspect angle. Because the direction of the human motion can be arbitrary, a large volume of training data across diverse aspects is needed for practical target activity classification through machine learning. As measurements can require significant monetary and labor costs, the synthesis of micro-Doppler signatures can be an alternate solution. Therefore, we investigate the feasibility of data augmentation through synthesizing micro-Doppler signatures of human activities from diverse radar aspect angles with input data from a single aspect angle. For the training data, the micro-Doppler radar signatures of 12 human activities are generated from different angles ranging from 0 to 315 degrees, at 45-degree increments, through simulations. For each angle, cGANs are trained to synthesize the micro-Doppler signatures for that specific angle given micro-Doppler signatures from another angle. The output of each model is evaluated by calculating mean-square errors and structural similarity indexes between the synthesized micro-Doppler signatures and the ground-truth ones obtained from simulations. We test three different scenarios, and report the respective results.

In this paper, we develop a new algorithm for centralized target detection in passive MIMO radar (PMR) using sparse recovery technique. PMRs use a network of receivers and illuminators of opportunity to detect and localize targets. We consider a widely separated PMR network assuming the availability of reference channels. We first transform the collected information of all receivers to a common space and combine them to attain a unified model. The problem of target detection in the extracted model is equal to a block sparse recovery problem. Since employing the generic sparse recovery tools are impractical due to the ultra-large dimension of the sensing matrix, we exploit the structure of the involving matrices and propose a very efficient distributed algorithm which extracts all scatterers, including targets and clutter simultaneously with a unified procedure. The proposed algorithm is highly efficient, and it does not require a high bandwidth link to transfer raw data from nodes to the fusion center. Moreover, the algorithm inherently benefits from parallel processing and distributes the extensive computations among receivers. Our simulation results demonstrate that the proposed algorithm outperforms the popular PMR detection algorithm, especially in the presence of interfering targets and any strong clutter residue.

Aiming at the target detection problem of MIMO radar under the condition of both spatial and spectral domain interferences, a waveform design method for MIMO radar based on joint optimization of spatial and spectral domain interferences suppression in multi-target scenarios is proposed. Compared with the traditional synthetic waveform method based on the transmitting signal covariance matrix, this method directly performs waveform design in the space-spectral domain in turn, considering target characteristics and spatial-spectral environment comprehensively. Firstly, according to the target characteristics and energy allocation strategy, the fast time transmission pulse set is divided into a group of sub-pulse sets whose number equals to the number of targets. Meanwhile a set of transmit filter weighting factor vectors are introduced and the number of vectors equals to the number of spatial interferences. Furthermore, the spatial waveform optimization model is constructed according to the pattern matching criterion, subjecting to the peak-to-average power ratio of the transmitted signal. The sub-pulse sets are obtained through convex optimization, and the full matrix of the emission waveform is formed by the sub-pulse sets. Then, according to the invariability of radar transmit pattern with changing the initial phase of the sub-pulse, the transmitted waveform with desired beam pattern and spectrum distribution is obtained by optimizing the initial phase matrix through convex optimization. Finally, the optimized transmit waveform of MIMO radar with the ability of simultaneously suppressing space frequency interferences is generated. The simulation results prove the effectiveness of the proposed method.

To tackle the inherent unknown deformation (e.g., translation, rotation and scaling) of the inverse synthetic aperture radar (ISAR) images, a deep polar transformer-circular convolutional neural network, i.e., PT-CCNN, is proposed to achieve deformation robust ISAR image automatic target recognition (ATR) in this article. Inspired by human visual system and canonical coordinate of Lie-groups, we adopt a polar transformer module to transform the deformation ISAR images to the log-polar representations, before which a conventional convolutional neural network (CNN) is utilized to predict the origin of log-polar transformation. The above techniques make the proposed network invariant to translation, and equivariant to rotation and scaling. On this basis, for the log-polar representations with wrap-around structure, a circular convolutional neural network (CCNN) is further applied to extract more effective and highly discriminative features and improve recognition accuracy. The proposed network is end-to-end trainable with a classification loss, and could extract deformation-robust and essential features automatically. For multiple practical ISAR image datasets of six satellites, the performance testing and comparison experiments demonstrate that the techniques utilized in PT-CCNN are effective, and our proposed network achieve higher recognition accuracy than those previous common methods based on deep learning. For instance, our proposed PT-CCNN beats traditional CNN on rotation, scaling and practical deformation datasets by 24.5-49.3%, 9.0-40.8% and 22.3-26.7%. And it also outperforms the polar CNN without using the above techniques on rotation, scaling and practical deformation datasets by 9.2-53.7%, 5.2-54.6% and 9.0-49.9%. Additionally, the presented visualization results show the abilities and advantages of our method in terms of handling image deformation and extracting effective features.

An E-band antenna module and corresponding beam sharpening method is proposed to achieve high azimuth resolution for front-looking real aperture radar application. To achieve high angular resolution with a small antenna aperture size, a solution involving real beam and super-resolution is investigated. A monopulse antenna is developed to produce two coprime channels, which are essential for the subsequent beam sharpening in digital domain. A dual-aperture horn antenna and a monopulse comparator that make up the monopulse antenna are designed respectively, and the experimental prototypes are fabricated and tested. Experimental results show the antenna module can operate in the 75 to 80 GHz band, more than 20 dBi gain and 12° half power azimuth beamwidth can be maintained in the sum-patterns, and the null-depths are lower than −20 dB in the difference-patterns. Based on the measured sum and difference patterns, an azimuth super-resolution processing based on two-channel deconvolution is implemented. The simulation results show the sharpening ratio can achieve 12 dB with 0 dB signal-noise-ratio and truncated half power beamwidth. Theoretically, the azimuth super-resolution of 0.8° can be achieved with 20 mm aperture size. The corresponding achievements are valuable in the follow-up E-band civil front-looking imaging system.

The aim of this paper is to show how compact mmWave radar systems, attached to a person, can take advantage of their movements, in order to significantly improve the image resolution by means of synthetic aperture radar (SAR) techniques. Thus, high resolution imaging becomes possible even with tiny on-chip systems, opening up a horizon of possibilities to assist visually impaired people. The approach is illustrated for movements in terms of arm swings. For this purpose, it is firstly shown that the information from an inertial measurement unit (IMU) can be used to provide a trajectory estimation, which is enough to apply synthetic aperture radar processing. As a consequence, the resolution of the radar is greatly improved when compared with that achieved considering a single position. In order to avoid the continuously growing position error, commonly observed in dead-reckoning systems relying just on IMU, zero velocity updates (ZUPT) will be systematically applied. For doing so, two different zero velocity detectors (ZVD) are considered, one based on acceleration data and other on velocity estimation. Although the positioning information is extracted from the IMU, demonstrating the enhanced obstacle detection capabilities obtained with this approach, other sensors that can be carried by a person and that are capable of providing trajectory estimations to combine measurements acquired with compact mmWave radars could have been used. The system is tested using different targets and a good performance comparable to the one achieved by using a high accuracy reference positioning system is attained. The compactness of the system enables the possibility of using it as a wearable device, e.g., attached to the wrist. This shows that an IMU is enough for tracking relatively simple movements, though more complex positioning systems can be used for more general actions.

Meter wave radar height measurement is one of the most difficult problems in meter wave radar system. At present, the most popular height measurement methods of meter wave radar include beam splitting method, MUSIC algorithm and maximum likelihood estimation method, which can achieve good performance under flat ground. However, the real ground is not ideal condition, and the situation of undulating ground is also very complicated. Under this condition, the performance of the above algorithm dropped sharply. Many researchers solve this problem by improving the robustness of the algorithm. However, the authors think that the key to solve the problem is to establish a refined height measurement signal model. In this paper, the accurate model of reflection coefficient, reflection height and reflection point under the condition of undulating ground are established to provide accurate signal model for meter wave radar height measurement. Then we deduce the application of generalized MUSIC algorithm and maximum likelihood estimation algorithm for MIMO radar in the accurate terrain signal model. In fact, the method proposed in this paper is a terrain matching MIMO radar target height measurement, and the traditional algorithm can be regarded as terrain mismatch. The simulation results show that the accuracy of terrain matching estimation of the precise signal model established in this paper is much better than that of the traditional simplified signal model.

Detection and classification of vulnerable road users (VRUs) such as pedestrians and cyclists is a key requirement for the realization of fully autonomous vehicles. Radar-based classification of VRUs can be achieved by exploiting differences in the micro-Doppler signatures associated with VRUs. Specifically, machine learning (ML) algorithms can be trained to classify VRUs using the spectral content of radar signals. The performance of these models depends on the quality and quantity of the data used during the training process. Currently, data collection is typically done through measurements or low fidelity physics, primitive-based simulations. The feasibility of carrying out measurements to collect training data is typically limited by the vast amounts of data required and practicality issues when using VRUs like animals. In this paper, we present a computationally efficient, high fidelity physics-based simulation workflow that can be used to obtain a large quantity of spectrograms from the micro-Doppler signatures of VRUs. The simulations are conducted on full-scale VRU models with a 77 GHz, frequency-modulated continuous-wave (FMCW) radar sensor model. Here, we collect the spectrograms of 4 targets; car, pedestrian, cyclist and dog at different speeds and angles-of-arrival. This data is then used to train a 5-layer convolutional neural network (CNN) that achieves nearly 100% classification accuracy after 5 epochs. Studies are conducted to investigate the impact of training data size, velocity and observation time window size on the accuracy of the CNN. Results from this study demonstrate how an accuracy of 95% can be realized using spectrograms obtained over a 0.2 s time window.

The outboard corner reflectors are widely used as a countermeasure to radar tracking and detection. Therefore, it is essential to determine the composite time-domain scattering characteristics of the corner reflector and ship against the sea background and the jamming effectiveness of the corner reflector array. In this paper, the composite electromagnetic scattering characteristics of ship target, corner reflector, and the sea surface are studied based on the time-domain bouncing ray method (TDSBR). The radar echo and broadband radar cross section (RCS) results for the time domain pulse incident are also obtained and the high-resolution range profile (HRRP) is calculated. We then devise an evaluation system based on centroid jamming intensity and range profile similarity to determine the jamming characteristics of passive jamming. We further propose an array composing of three sizes of corner reflectors. Finally, the centroid jamming effectiveness is analyzed by comparing the RCS of the corner reflector array and the ship, and the deception jamming effectiveness is measured by calculating the range profile similarity based on the cosine similarity method and the range profile peak envelope overlap. Simulation results show that the reflector array can achieve a good performance both for centroid and range profile deception. The simulation results given and the evaluation method proposed in this paper provides significant practical insights into the design and analysis of the passive jamming systems.

In this paper, novel Dual-Functional Radar-Communication (DFRC) waveforms with peak average power ratio (PAPR) constraint are designed, which are under the multiple-input multiple-output (MIMO) radar-communication system. The DFRC waveforms transmitted by multiple antennas can send communication information to many downlink cellular users and detect radar targets simultaneously. An optimization model is established to minimize the downlink multi-user interference (MUI) under PAPR constraint. The model is non-convex quadratically constrained quadratic programs (QCQP) and can be derived into a convex problem and solved by using the semi-definite relaxation (SDR) technique with rank-one approximation. Numerical simulations demonstrate that our proposed waveforms can achieve a better radar performance in practical scenarios without sacrificing the communication performance.

The direct measurement of distance-dependent information between wireless units represents a challenge for wireless locating systems, because it requires the exact time synchronization of separate wireless units. To avoid these synchronization efforts, many wireless locating systems only evaluate phase difference of arrival (PDOA) measurements. While simple PDOA localization techniques rely on multiangulation, advanced PDOA concepts like the holographic extended Kalman filter (HEKF) directly evaluate the measured phases without non-linear preprocessing. However, these differential phase measurement approaches are less sensitive than systems that can measure absolute phase variations, which allow the tracking of much smaller position changes than the signal’s carrier wavelength. This paper proposes to extend the HEKF by the evaluation of absolute phases in an incoherent measurement setup, which consists of a continuous wave (CW) beacon and several receivers. The developed quasi-coherent holographic extended Kalman filter (QCHEKF) uses the overdetermined PDOA measurements to estimate the phase–frequency relation between each beacon–receiver pair. Then, the established phase–frequency relations allow the evaluation of absolute phase measurements and, thus, the accurate localization and tracking of a simple, unsynchronized, narrowband CW beacon, even under severe multipath conditions. This novel concept is experimentally validated via 3D localization results in a challenging indoor scenario using a 24 GHz CW measurement setup. Here, the QCHEKF improves the achieved localization accuracy in comparison to the HEKF by 35 % from 0.78 cm to 0.51 cm, while the maximum deviation from the trajectory reduces by 68 % from 5 cm to 1.6 cm. Furthermore, the QCHEKF enables the exact tracking of fast changes in direction, which is usually a significant challenge for standard wireless target tracking systems.

Owing to advances in the overall performance and anti-interception capability of radars, the designs of radar waveforms with good correlation properties have been a concern for researchers. In this paper, we propose a novel method based on convolutional neural networks (CNNs) for designing single or multiple unimodular sequences with good auto- and cross-correlation or weighted correlation properties. The framework of the neural networks for sequence optimization is constructed using group convolution and identity mapping, and three different loss functions are presented using different optimization objectives. To illustrate the performance of the proposed method, we present numerous examples, including the design of sequences with low autocorrelation sidelobes in a specified lag interval and a sequence set with good auto- and cross-correlation properties. Moreover, an analysis of the simulations shows that the sequences designed through our method demonstrate better correlation properties than classic algorithms.

This work presents a seat-occupancy detection system based on a PCR (Pulsed Coherent Radar) at the unlicensed 60 GHz ISM band. The radar can measure distances with sub-millimeter resolution. Therefore, the system can detect the presence of people occupying the seats by measuring small movements of the body, such as those produced by breathing. In consequence, the system not only measures seat-occupancy but also breathing rate, which is estimated from the amplitude peaks after filtering the amplitude measured by the radar to remove the noise. The effect of the car vibrations and random body movement is experimentally studied. Measurements performed with adults, as well as with a baby emulator sitting in a child seat, are presented. Comparison of the system with another that measures the breath based on airflow shows good agreement and permits its validation.

Edge computing has a wide range of applications in the Internet of Things (IoT), especially suitable for low latency, high bandwidth, and high reliability. Edge computing enabling IoT can locate the vehicles rapidly with the help of edge computing. The sensors of IoTs can construct the Frequency diverse array (FDA) radar system, which has been widely concerned by scholars in recent years. The monostatic FDA and multiple-input-multiple-output (FDA-MIMO) is a research hotspot in parameter estimation field, but the research based on bistatic FDA-MIMO radar is insufficient. In this paper, we propose a tensor-based target location method in bistatic FDA-MIMO radar, which implements joint direction of arrival (DOA), direction of departure (DOD) and range parameters estimation. First of all, subarrays with different transmission frequency increment are used to construct the transmitting array to overcome the coupling between DOD and range. Then the tensor-based third-order signal model is established, which saves the multidimensional structure characteristics of received signal. And the signal subspace of each subarray is estimated by high-order-singular value decomposition (HOSVD). Furthermore, the phase period ambiguity is eliminated by limiting the range of the target, and the method for DOA, DOD and range parameters matching is provided. Theoretical analysis and numerical simulations demonstrate the effectiveness and superiority of the proposed method.

Autonomous systems require a continuous and dependable environment perception for navigation and decision-making, which is best achieved by combining different sensor types. Radar continues to function robustly in compromised circumstances in which cameras become impaired, guaranteeing a steady inflow of information. Yet, camera images provide a more intuitive and readily applicable impression of the world. This work combines the complementary strengths of both sensor types in a unique self-learning fusion approach for a probabilistic scene reconstruction in adverse surrounding conditions. After reducing the memory requirements of both high-dimensional measurements through a decoupled stochastic self-supervised compression technique, the proposed algorithm exploits similarities and establishes correspondences between both domains at different feature levels during training. Then, at inference time, relying exclusively on radio frequencies, the model successively predicts camera constituents in an autoregressive and self-contained process. These discrete tokens are finally transformed back into an instructive view of the respective surrounding, allowing to visually perceive potential dangers for important tasks downstream., Comment: concurrently submitted to IEEE Access

Deep neural networks are used as effective methods for the Low Probability of Intercept (LPI) radar waveform recognition. However, existing models' performance degrades seriously at low Signal-to-Noise Ratios (SNRs) because the effective features extracted by the networks are insufficient under noise jamming. In this paper, we propose a multi-resolution deep feature fusion method for LPI radar waveform recognition. First, we apply the enhanced Fourier-based Synchrosqueezing Transform (FSST), which shows good performance at low SNRs, to convert radar signals into time-frequency images. Then, we construct a multi-resolution deep convolutional network to extract more deep features from each resolution channel. Next, we explore an interactive feature fusion strategy for deep feature fusion. By some down-sampling or up-sampling blocks, different resolution features are fused to generate new features. Finally, we apply a fusion algorithm to the fully connected layer to achieve classification fusion for better performance. Simulation experiments on twelve kinds of LPI radar waveforms show that the overall recognition accuracy of our method can reach 95.2% at the SNR of -8 dB. It is proved that our approach does indeed improve the recognition accuracy effectively at low SNRs.

Multifrequency continuous-wave–continuous-wave (MFCW–CW) radars simultaneously transmit multifrequency continuous-wave (MFCW) signals sequentially at a constant time step and a continuous-wave (CW) signal constantly. As MFCW signals are transmitted sequentially, only one phase difference exists at each time. All phase differences between the MFCW and CW signals should be exploited to resolve ambiguous ranges. In this article, we propose a method to compensate the phase difference between the sequential MFCW signals and CW signal using the Doppler frequency of the CW signal. To verify the phase difference compensation result, we estimated the range of multiple targets. The simulation results showed that the proposed phase difference compensation method performed well regardless of compensation direction and target motion. Moreover, we proposed applying the maximum likelihood estimation (MLE) and reduced-complexity off-grid sparse Bayesian learning (RC–OGSBL) to estimate ranges and analyzed their performance. The range accuracy of the MLE using a 0.1- m grid search showed superior performance and the running time of the MLE using the simple method is the fastest among the simulated methods. The RC–OGSBL using a 5- m grid showed good results in terms of range accuracy and running time.

This study focuses on the reentry target classification and fuses target features based on the generalized evidence theory. The features are extensively investigated, and the ballistic factor and length of the high-resolution range profile are selected. The evidence theory is advantageous for solving feature fusion, representing uncertainty, and is widely used in defense applications. However, determining the generalized basic probability assignment (GBPA) and dealing with uncertainty is a matter that requires further improvement. In this paper, we propose a new method to determine GBPA using uncertainty with time-series radar data. First, the samples of each known class are encoded as a generalized fuzzy number (GFN), and the power set comprising the frame of discernment (FOD) is calculated from the GFN and each intersection area. Subsequently, the test samples with uncertainty are encoded as triangular fuzzy numbers, reflecting the mean and standard deviation of a Kalman filter. Finally, the firing strength between the model and the input is calculated as the degree of support for the class hypothesis, which is used to determine the GBPA. The proposed algorithm is compared with the existing methods and exhibits high classification accuracy and a short classification time without leakage. In experiments with various input uncertainties, the results demonstrate that our method can effectively reflect the input uncertainty and determine the GBPA.

Eight different driver head movements are measured using a millimeter-wave FMCW radar mounted in the dashboard of a car. The micro-Doppler signatures are converted into a spectrogram image format for analysis and classification purposes. The eight different head motions exhibit unique time-frequency profiles, which can be classified by deep learning algorithms. In this study, a convolutional neural network is used to classify the eight head motions with an optimized window size. Various dataset permutations are considered, such as the effect of window width on classification accuracy and the classification accuracy of head motions in a still car compared to a moving car.

Chaff cloud is widely used in target covering. In this paper, a jamming performance estimation strategy is presented for the elliptical and cubic chaff clouds. The range profiles of the composite models of the plane with the chaff cloud are experimentally accessed in an anechoic chamber using a stepped-frequency waveform measurement system. Then, dimensions, characteristic length, and distributing entropy of the equivalent scattering centers of the plane and the jammer are extracted from the measured range profiles, respectively. Based on these features, support vector machines, naïve Bayesian classification, and decision tree classification methods are employed to determine the recognition rate of the target under the chaff jamming, and then determine the jamming efficiency of the two kinds of chaff clouds. Results show that two types of chaff clouds are able to reduce the target recognition rate. Besides, when the radar uses the Euclidean distance and decision tree classification methods to complete the recognition task, a good jamming efficiency can be achieved by releasing the elliptical or the cubic chaff cloud. Meanwhile, a low level of jamming performance outcomes using the support vector machines classification method. It is also found that the chaff cloud released from the side of the plane achieves a better jamming performance compared with the other releasing locations. Finally, the influence of environmental noise on the recognition rate of the plane is studied. Estimating results show that the recognition rates decrease with the increasing of the power level of the noise. When the transmitting power level of the noise reaches 1.2 times the reflected power level of the target, it is quite hard to estimate the jamming performance of two kinds of chaff clouds.

Autonomous collision detection and avoidance is a crucial requirement for the safe navigation of unmanned surface vehicles (USVs) in maritime traffic situations. Automatic identification system (AIS) is used to obtain the motion information of surrounding ships and their dimensional specifications. With AIS information, appropriate collision risk assessment between two ships can be performed; further, collision avoidance can be achieved by defining a safe radius of avoidance, which can be determined considering the shape parameters of a target ship. However, AIS data are often unreliable and some commercial fishing vessels intentionally turn off the public tracking system to hide their location. Under these circumstances, marine radars are used to detect and estimate the motion information of nearby ships. However, most existing target tracking studies model the target as a point object without any spatial extent, and thus, its physical dimensions cannot be identified. In this paper, a target tracking method that uses a marine radar is proposed to simultaneously estimate the motion states (i.e., position, course, and speed) of a target ship and its geometric parameter (length) in the framework of an extended Kalman filter (EKF). The proposed approach enhances collision avoidance by providing the kinematic and length parameters to evaluate collision risk and generate an appropriate collision-free path. Real-sea experiments using a developed USV system were conducted to verify and demonstrate the feasibility of the proposed algorithm; the results are presented and discussed in this paper.

In this paper, new virtual antenna array (VAA) based synthesis techniques are introduced for side lobe level (SLL) reduction, beam thinning, and number of elements minimization for elliptical cylindrical antenna arrays (ECAA) of radar systems. Thereby, significant improvements in the array gain and directivity are achieved, which enhance the detection range and angular resolution of the radar. Furthermore, the overall implementation cost of the system is highly reduced by saving the number of elements and the corresponding RF chains and simplifying the feeding network. Firstly, the proposed technique decomposes the single transmit/receive ECAA into a separate transmit linear antenna array (LAA) and receive elliptical antenna array (EAA). Secondly, the number of antenna elements, element spacing, and excitations of the created LAA and EAA are optimized using particle swarm optimization (PSO) to produce efficient beamformed patterns. Finally, the Kronker product of the optimized LAA and EAA patterns is performed to form the optimized virtual ECAA (V-ECAA) pattern. We also introduced both the uniform feeding based V-ECAA technique and the non-uniform feeding based V-ECAA synthesis technique for more flexibility and better productivity in antenna arrays design. The simulation results revealed that the uniform feeding based V-ECAA provides an identical pattern to that of the traditional uniform feeding ECAA while saves the number of elements by 66.6%. While in the case of non-uniform feeding based V-ECAA, it provides much lower SLL and narrower HPBW than those of the ECAA while saving the number of elements by 63.8%. Furthermore, the HB is applied to provide additional beam thinning and SLL reduction of the proposed non-uniform V-ECAA that is denoted as (HBV-ECAA). The possibility of practical validations of the synthesized V-ECAAs is verified using the computer simulation technology (CST) microwave studio package, which gives users an integrated design environment and achieves realizable and robust designs.

In order to improve the feasibility of low-elevation height measurement for meter-wave multiple-input multiple-output (MIMO) radar, a virtual sub-array-level beam splitting low-elevation height measurement algorithm is proposed. The algorithm can solve the coherence problems caused by low-angle multipath reflection, target reflection wave and direct wave coupling, and quickly measure the low elevation angle of the target on a flat ground, and then obtain the target height through the geometric relationship. First, establish an error graph in advance according to the radar frequency, antenna height and reflection coefficient; then combine the nine virtual channels of the three-element MIMO radar into three channels; then use the phase difference between adjacent channels to perform low-elevation encoding to confirm the target elevation range; The calculated amplitude error value is compared with the error curve diagram established in advance to obtain the target low elevation angle; finally, the target height data is obtained according to the geometric relationship. The signal model and error formula under complex terrain are finally derived. The simulation verifies the accuracy and superiority of the proposed algorithm on flat ground and complex undulating ground.

Automotive radar is one of the enabling technologies for advanced driver assistance systems (ADAS) and subsequently fully autonomous vehicles. Along with determining the range and velocity of targets with fairly high resolution, autonomous vehicles navigating complex urban environments need radar sensors with high azimuth and elevation resolution. Size and cost constraints limit the physical number of antennas that can be used to achieve high resolution direction-of-arrival (DoA) estimation. Multiple-input/multiple-output (MIMO) schemes achieve larger virtual arrays using fewer physical antennas than would be needed for a single-input/multiple-output (SIMO) system. This paper presents a high-fidelity physics simulation of a 77GHz, frequency-modulated continuous-waveform (FMCW)-based 128 channel (8 transmitters (Tx), 16 receivers (Rx)) MIMO radar sensor. The 77GHz synthetic radar returns from full scale traffic scenes are obtained using a high-fidelity physics, shooting and bouncing ray electromagnetics solver. A fast Fourier transform (FFT) based signal processing scheme is used across slow-time (chirp) and space (channel) to obtain range-Doppler and DoA maps, respectively. Detection and angular separation performance comparisons of 16, 64 and 128 channel MIMO radar sensors are made for two complex driving scenarios.

For the reason of target multipaths ambiguities, localization for hidden moving target due to lack of line of sight (LOS) path in urban environments is a hard task. To handle this problem we propose a localization algorithm based on the internal connection of these multipaths in this paper. First, we establish an electromagnetic (EM) propagation model based on the characters of the measured signal received by an L-band single-input single-output (SISO) ultra-wideband (UWB) radar. After that, the model-based target localization algorithm is proposed based on a times-of-arrival (TOAs) association method, which is able to match the measured TOAs with the corresponding EM propagation paths correctly. The proposed method can cope with the case while some missing detections of TOAs happen. Simulation and experimental results ultimately validate the robustness and the state-of-the-art performance of the proposed algorithm.

Instead of only considering the radar estimation error in the traditional radar system (TRS), for the integrated radar and communication system (IRCS), we investigate the Cramer-Rao bound (CRB) of the localization estimation, which is influenced by both radar estimation error and communication transmission error. The functions of radar and communication are operated simultaneously by embedding the communication symbols into the multicarrier radar waveforms. Firstly, we derive the CRB of time/direction of arrival (TOA/DOA) estimation. To minimize the estimation error, we maximize the signal-to-interference-plus-noise ratio (SINR) of radar by iteratively optimizing the radar transmit and receive beamformers (with the constraint of available transmit power). Then, the CRB of localization estimation is derived using hybrid TOA/DOA measurement. The local CRBs from different IRCSs are fused according to the linear fusion rule at the fusion center (FC). Finally, numerical results demonstrate that the additional estimation errors for the IRCS are mainly determined by the channel conditions of communication and available transmit power; the estimation accuracy for both IRCS and TRS can be improved through the iterative transmit and receive beamforming (ITRB) technique.

In this article, a multiple diffuse coding metasurface (MDCM) of independent polarization is designed to control the propagation direction of diffuse reflections under different polarizations and to improve the monostatic and bistatic RCS (radar cross section) reduction effect. First, a method for controlling the distribution range and propagation direction of the diffuse field is studied, and the diffuse field distribution of the random phase metasurface is optimized by a genetic algorithm to improve the uniformity of the diffuse scattering distribution. Then, the random phase distribution is superimposed on the periodic gradient phase distributions of the linear and hedge types in the orthogonal direction so that the main propagation direction of the diffuse metasurface deviates from the specular reflection region under different polarizations, showing single and two diffuse beams. Finally, the anisotropic unit cell with a rectangle inside and an improved Jerusalem cross on the outside is employed as the basic coding element of the MDCM due to its independent polarization phase response. The numerical and experimental results show that the MDCM features multiple diffuse scattering, independent polarization and angle insensitivity and can efficiently improve the monostatic and bistatic RCS reduction effect simultaneously. Because the scattered energies are redirected away from the specular reflection direction, the specular scattering reduction effect is better than the isotropic diffuse metasurface. The proposed method increases the difficulty of detection by single or netted radar and has the potential for the applications of stealth techniques.

The ocean surface current vector contains velocity and direction information, and its acquisition method has important research value in marine applications. Multi-aperture along-track interferometric (MA-ATI) SAR is a novel method of current vector detection. However, due to the lack of real experimental data validation, complex marine environment and diverse system parameters have an uncertain impact on the current measurement ability of MA-ATI SAR. Therefore it is necessary to analyze the measurement accuracy and error of this method in theory. Based on the current measurement principle of MA-ATI SAR, this article deduces and establishes the accuracy and error simulation model, and analyzes the influence of radar parameters and marine environment on the velocity and direction results. In the simulation results, the airborne MIMO-SAR radar parameters is more suitable to use MA-ATI method for accurate current measurement, and the accuracy of current measurement is higher for the current with high velocity and direction close to azimuth. It is found that the variation of wind direction will lead to obvious errors in MA-ATI current results, and it is pointed out that accurate current results can be obtained by iteratively correcting the velocity error of sub-apertures using M4S model.

Recent developments in the state-of-the-art Intelligent Transportation Systems enable autonomous vehicles to offer significant safety services to take appropriate and prompt actions to avoid any probable unfortunate road-hazard. As the utmost functions of the advanced driving assistance system-equipped autonomous vehicles governed by the equipped radar, therefore, the radar system should have the ability to track multiple-targets accurately with high radar-resolutions. Unlike the microwave-radar, the photonic-radar comes out as an attractive candidate owing to provide wide-spectra to attain improved and precise radar-resolutions at low-power requirements along with extended target-range even under severe atmospheric fluctuations. Therefore, a linear frequency-modulated continuous-wave photonic-radar is developed in this work to carry out a radar cross-section-based tracking of multiple mobile-targets in the presence of fog, cloud, and rain. Besides it, some complex real-time traffic-scenarios consisting of multiple mobile-targets make the target-detection, data-association, and classification processes more complicated. Therefore, this work is tested for different multiple-mobile targets in different complicated traffic-scenarios modeled by using MATLAB™ software. The performance of the demonstrated photonic-radar is assessed through the power spectral density and range-Doppler mapping measurements. Furthermore, a comparison of the developed photonic-radar is also established with conventional microwave-radar to present a comparative analysis.

Many production vehicles are now equipped with both cameras and radar in order to provide various driver-assistance systems (DAS) with position information of surrounding objects. These sensors, however, cannot provide position information accurate enough to realize highly automated driving functions and other advanced driver-assistance systems (ADAS). Sensor fusion methods were proposed to overcome these limitations, but they tend to show limited detection performance gain in terms of accuracy and robustness. In this study, we propose a camera-radar sensor fusion framework for robust vehicle localization based on vehicle part (rear corner) detection and localization. The main idea of the proposed method is to reinforce the azimuth angle accuracy of the radar information by detecting and localizing the rear corner part of the target vehicle from an image. This part-based fusion approach enables accurate vehicle localization as well as robust performance with respect to occlusions. For efficient part detection, several candidate points are generated around the initial radar point. Then, a widely adopted deep learning approach is used to detect and localize the left and right corners of target vehicles. The corner detection network outputs their reliability score based on the localization uncertainty of the center point in corner parts. Using these position reliability scores along with a particle filter, the most probable rear corner positions are estimated. Estimated positions (pixel coordinate) are translated into angular data, and the surrounding vehicle is localized with respect to the ego-vehicle by combining the angular data of the rear corner and the radar's range data in the lateral and longitudinal direction. The experimental test results show that the proposed method provides significantly better localization performance in the lateral direction, with greatly reduced maximum errors (radar: 3.02m, proposed method: 0.66m) and root mean squared errors (radar: 0.57m, proposed method: 0.18m).