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This dissertation addresses the critical role of perception in autonomous navigation and driving systems, proposing a paradigm shift by focusing on radar as the primary sensor. Traditional light-based sensors, such as cameras and lidars, face challenges in adverse weather conditions, impacting the reliability of autonomous systems. The research explores four key aspects of radar-based sensing: high-quality perception, semantic understanding, contextual information, and scalable deployment.The first aspect introduces a multi-radar system that mitigates specularity and sparsity issues in radar point clouds, enhancing perception quality. Spatial averaging techniques filter out noise, and a deep learning system accurately converts radar data into bounding boxes for objects. Semantic understanding is tackled by a novel sensor fusion system, integrating radar point clouds with missing texture and color information from cameras. A unique fusion philosophy breaks feature dependence, ensuring robust performance even in adverse conditions.Recognizing the importance of environmental context, the dissertation addresses challenges in identifying traffic infrastructure using radar alone. Specialized millimeter-wave backscatter tags, affixed to infrastructure, enhance radar visibility. The Van-Atta array amplifies reflections, RF switches modulate backscattered signals, and unique gold codes enable precise localization without modifying radar hardware.To facilitate deployment, an open-source radar simulation framework is introduced, simulating high-fidelity Multiple Input Multiple Output (MIMO) radar data using lidar point clouds and camera images. This framework allows researchers to efficiently evaluate algorithms with effectiveness comparable to real-world data, enabling rapid iterations across the radar parameter space.The comprehensive exploration of multi-radar systems, radar-camera fusion, smart infrastructure augmentation, and the innovative radar simulation framework contributes to advancing autonomous systems, particularly in challenging environmental conditions. The dissertation aims to overcome current limitations and chart a path for future breakthroughs in autonomous perception technologies.

Vibration measurement is a crucial task in industrial systems, where vibration characteristics reflect health conditions and indicate anomalies of the devices. Previous approaches either work in an intrusive manner or fail to capture the micrometer-level vibrations. In this work, we propose mmVib, a practical approach to measure micrometer-level vibrations with mmWave radar. First, we derive a metric called Vibration Signal-to-Noise Ratio (VSNR) that highlights the directions of reducing measurement errors of tiny vibrations. Then, we introduce the design of mmVib based on the concept of Multi-Signal Consolidation (MSC) for the error reduction and multi-object measurement. We implement a prototype of mmVib, and the experiments show that it achieves 3.946% relative amplitude error and 0.02487% relative frequency error in median. Typically, the average amplitude error is only 3.174um when measuring the 100um-amplitude vibration at around 5 meters. Compared to two existing mmWave-based approaches, mmVib reduces the 80th-percentile amplitude error by 69.21% and 97.99% respectively.

Gesture Recognition which is an inevitable part of human computer interaction is an ever evolving active research area. This has been in use under varied applications like smart home systems, sign language recognition, augment reality and in device controls. The acquisition of hand gestures are usually through optical sensors, in this work a radar based hand gesture data set is used for classifying the gestures. Micro doppler signatures were used as the input to the model. Radar dataset has fewer data samples compared to optical based data sets. The proposed work uses separable convolutional neural networks model which does depth wise convolution followed by point wise convolution to reduce overfitting effect of the training data. The proposed model was built in such a way that the model is capable of classifying any unseen data without exactly mimicking the training samples. The proposed model has achieved 94.56% as the testing accuracy which is certainly better than the previous work on this Dop Net data set. Moreover the model has also minimized the computational hours of the model using separable convolutions.

Nyquist Folding Receiver (NYFR) is a perceptron structure that realizes a low probability of intercept (LPI) signal analog to information. Aiming at the problem of LPI radar signal receiving, the time domain, frequency domain, and time-frequency domain problems of signals intercepted by NYFR structure are studied. Combined with the time-frequency analysis (TFA) method, a radar recognition scheme based on deep learning (DL) is introduced, which can reliably classify common LPI radar signals. First, the structure of NYFR and its characteristics in the time domain, frequency domain, and time and frequency domain are analyzed. Then, the received signal is then converted into a time-frequency image (TFI). Finally, four kinds of DL algorithms are used to classify LPI radar signals. Simulation results demonstrate the correctness of the NYFR structure, and the effectiveness of the proposed recognition method is verified by comparison experiments.

Stealth aircraft use Stealth Technology in order to evade recognition by utilizing mix of highlights with the goal that Aircraft can invade with RADAR and furthermore decrease the deceivability in “radio frequency (RF) range”, sound and infrared visual. During the most recent decades, stealth technology has demonstrated to be one of the best methodologies to the extent the undertaking to escape radar systems is concerned. Stealth intends to evade the recognition or attempt to cover up, for planes, avoiding radar implied stealth. The idea utilized in the stealth innovation isn’t so difficult. It utilizes the guideline to retain and mirror the radar waves. Airplane redirects the radar waves in different ways and limits the radar waves which return back to radar. Another marvel which permits the air ship maintain completely the incoming radar wavelengths to inhabit the electromagnetic reincorporated energy in various way. The structure and material used to manufacture air ship choose the degree of stealth accomplish via air ship. The thought for the radar radio wire to release Radio energy that is then returned through any item that occurs to recognize. The time taken for the reflection to come is measure by the radar radio wire and it can tell how far off an object is.

Roadside sensors, such as camera and millimeter-wave (MMW) radar, provide traffic information beyond the visual range of intelligent vehicles in cooperative vehicle-infrastructure systems. Unlike onboard equipment, roadside sensors are affiliated with different systems and lack synchronization in both space and time. In this paper, we propose a novel spatio-temporal synchronization method of asynchronous roadside MMW radar-camera for sensor fusion, which utilizes features of the scenario to extract lane line corner points to pre-calibrate the camera. Based on the consistent time flow rate of the separate sensors, multiple virtual detection lines are set up to match the time headway of successive vehicles and conduct objective matching to track data. Finally, a synchronization optimization model is formulated and a constrained nonlinear minimization solver is applied to tune the parameters. Measure data from Donghai Bridge in Shanghai is applied to verify the feasibility and effectiveness of the method. The results determine that there are 33 frames (33*40 ms) of temporal deviation between the camera and the radar in this case. After the synchronization, the average spatial deviation is reduced from 2.47 m to 0.42 m in the X-direction and 64.06 m to 2.34 m in the Y-direction, respectively. This study provides an economical and effective way to solve the problem of spatio-temporal synchronization of roadside sensors.

Multiple-input multiple-output (MIMO) radar design for extended targets in a spectrally crowded environment is a challenge owing to the high sensitivity of the target impulse response (TIR) and the increasing requests for spectrum. Assuming unknown TIR, this paper proposes a joint design method to optimize the transmit waveforms and receive filter bank in MIMO structure ensuring spectral compatibility with the overlayed radiators. A priori information is used to impose a spectral constraint on the waveforms, which is the result of a non-convex optimization problem aimed at enhancing the average signal-to-interference-plus-noise-ratio (SINR) over a finite uncertainty set for the TIR. The new method realizes an improved spectral cohabitation with the surrounding radiators through an appropriate modulation of the transmitted energy. In addition, we develop an iterative optimization algorithm which successively enhances the average SINR. Each iteration of the algorithm involves a hidden convex problem, which can be solved resorting to the rank-one decomposition procedure. Finally, the performance is assessed by studying the trade-off among the achieved SINR and spectral shape. The reported results are presented to analyze the performance of the devised method against several counterparts in terms of the SINR value and robustness.

Recently, radar frequency bands have attracted attention as candidates for cognitive radio owing to their wide bandwidth but low utilization. When spectrum sensing is performed in radar bands, sliding-window-based detection can be used to exploit the sparsity of the radar pulse signal in the time domain and obtain a sufficient number of samples. The detection performance and sensing time depend on the configuration of the windows. The detection performance was optimized when the window size was equal to the pulsewidth. However, the pulsewidth is generally unknown. Another way to increase the detection performance is to obtain more samples of the window. However, this causes large computation overhead owing to the sparsity of radar signals. Therefore, we propose an adaptive-sliding-window-based detection scheme to address these problems. First, the window is adaptively applied. Second, a pulsewidth estimation algorithm is proposed to approximate the window size to the pulsewidth for each detection. Additionally, we demonstrate the performance improvement of the proposed scheme and evaluate the estimation error of the proposed algorithm via simulations.

Reasonable route planning and resource allocation strategy in the airborne radar systems (ARS), can sufficiently utilize the limited resources and promote the multitarget tracking (MTT) performance. However, using separately the route planning and resource optimization method cannot take full advantage of the airborne platform. Considering this issue, we propose a joint online route planning and resource optimization strategy in the ARS to improve the system capability for MTT. First, a kinematic model of the ARS, including the mathematical expression between the radar states and the system control parameters, is introduced, which integrates the route planning to the radar scheduling scheme. Next, the posterior Cramer–Rao lower bound about route planning and resource optimization variables for the tracking targets is derived. Then, a scaled-based utility function is established to quantify the MTT performance. Hereafter, a nonconvex problem is formulated by minimizing the utility function with route and resource constraints, and then a efficient three-stage partition-based solution is proposed. Finally, simulation experiments demonstrate the effectiveness of the proposed algorithm. Furthermore, over the traditional benchmark algorithm, the tracking performance of the proposed approach improves 30.44%.

In this article, we propose a framework for contactless human-computer interaction (HCI) using novel tracking techniques based on deep learning-based super-resolution and tracking algorithms. Our system offers unprecedented high-resolution tracking of hand position and motion characteristics by leveraging spatial and temporal features embedded in the reflected radar waveform. Rather than classifying samples from a predefined set of hand gestures, as common in existing work on deep learning with mmWave radar, our proposed imager employs a regressive full convolutional neural network (FCNN) approach to improve localization accuracy by spatial super-resolution. While the proposed techniques are suitable for a host of tracking applications, this article focuses on their application as a musical interface to demonstrate the robustness of the gesture sensing pipeline and deep learning signal processing chain. The user can control the instrument by varying the position and velocity of their hand above the vertically-facing sensor. By employing a commercially available multiple-input-multiple-output (MIMO) radar rather than a traditional optical sensor, our framework demonstrates the efficacy of the mmWave sensing modality for fine motion tracking and offers an elegant solution to a host of HCI tasks. Additionally, we provide a freely available software package and user interface for controlling the device, streaming the data to MATLAB in real-time, and increasing accessibility to the signal processing and device interface functionality utilized in this article., Accepted to IEEE Transactions on Multimedia

The world is entering a new era with ubiquitous connectivity among billions of humans and machines, i.e., the 6G massive Internet of Things (IoT). Radar and communication need to be integrated into this system to achieve target detection and massive connectivity. In this work, we first develop a shape-adaptive antenna array composed of multiple sub-arrays spaced at regular intervals. The shape-adaptive antenna array uses flexible material that enables changes in the physical structure to achieve adaptive gain. Subsequently, we investigate the micro-movement of IoT targets, such as the micro-motion characteristics and micro-Doppler effects of rotary-wing drones. The simulation results demonstrate the effectiveness and efficiency of the proposed schemes for detecting UAVs in an IoT scenario.

Speeding is one of the biggest threatens to road safety. However, facilities like radar detector and speed camera are not deployed everywhere, as roads in some areas like campus and residential areas often lack these facilities. Several solutions either depend on pre-deployed infrastructures, or require additional devices, which motivate us to explore the practicability of using smartphones' acoustic sensors to detect vehicle speed. In this paper, we propose a Hybrid Detection system for vehicle Speed (HDSpeed). We first investigate the relationship between acoustic pattern and vehicle speed. According to our findings on typical patterns of both Electric Vehicles (EVs) and Gasoline Vehicles (GVs), we separately extract different features from the acoustic signals of EVs and GVs. A CNN and an LSTMN are designed for training EV and GV models, respectively. Considering that applying neural networks obtains coarse-grained information like a speed section, we propose a detection method based on active acoustic sensing, in which method HDSpeed calculates the fine-grained speed by detecting the distance change between the smartphone and the passing vehicle. In addition, the previously detected speed section can eliminate interferences of surrounding moving objects. Through extensive experiments in real driving environments, HDSpeed achieves an average error of 2.17km/h.

Autonomous Vehicles (AVs) rely on a set of radar sensors operated on frequency modulated waveforms. Due to the large bandwidth requirement of frequency modulated radars, only a limited number of proximate vehicles can be allowed for a concurrent transmission within the available spectrum. However, with the explosive growth of AVs, the available spectrum may soon reach its capacity. As a results, the coexistence of multiple AVs working on same resource, may lead to the problem of Radar-to-Radar (R2R) interference, also known as radar blindness. In this paper, we propose the notion of coded waveforms to minimize the R2R interference among the vehicles operating on the same resource. Specifically, the spread spectrum codes have been used as another degree of freedom (i.e., along with time-frequency resources) to spread the inter-vehicular radar interference over the wider spectrum, which enable to orthogonalize more number of AVs over the available range of spectrum. In addition, we have formulated a Spread Spectrum-based Radar Transmission Scheme (SS-RTS), and described the transmission and reception through SS-RTS. Also, the SS-RTS has been compared with the existing Graph-based Resource Allocation (GRA) scheme. Further, simulation results verified that SS-RTS significantly reduces the inter-vehicular R2R interference and outperforms GRA in terms of blind probability.

Intelligent vehicle testing and validation technology is a hotspot in the field of intelligent vehicles. Millimeter-wave radar has been widely used in intelligent vehicles and advanced driving assistant systems (ADASs). Owing to low efficiency, high maintenance cost and limited scenario types, the traditional real-vehicle-testing can make it difficult to meet the requirements of intelligent vehicle testing. Also, the degree of reality of millimeter-wave radar sensor models in the virtual test platform is not convincing enough. To solve the mentioned problems, the millimeter-wave radar in-the-loop (mmRil) testing for intelligent vehicles is proposed in this study. First, the radar echo simulation method is developed. Next, based on the millimeter-wave flight time, Doppler frequency shift and the radar power attenuation equation, the mmRil test system is built. A geometric model of a radar target detection and a power attenuation model under bad weather conditions are established. Then, the accuracy of the testbench is verified. Finally, a typical intelligent vehicle control strategy is used for testing under various working conditions and bad weather using the mmRil testbench. The test results show that the mmRil testing has higher authenticity and accuracy than the simulation testing and can be used to test the intelligent vehicle algorithms under severe weather conditions. Compared with the real vehicle testing, the mmRil testing has higher efficiency, repeatability and safety. Therefore, the mmRil testing represents an indispensable testing method in the field of intelligent vehicle testing.

As an increasingly recognized optical interferometric technique for nondestructive testing and evaluation, shearography has attracted extensive interest in various industrial applications. However, it suffers from the ignorance of the whole process of dynamic surface deformation and the difficulty in determining the depth-resolved information of inhomogeneities. In this regard, a novel speckle pattern interferometric modality named photothermal radar shearography is proposed. Unlike the differential mode of conventional shearography, the present work focuses on the dynamic surface displacement field channel technique with more comprehensive understanding of the subsurface structural information. With the utilization of frequency modulated photothermal excitation, the depth-distributed information of subsurface structures and inhomogeneities is encoded into the induced dynamic surface deformation. Using Hilbert transform and least-square method solved by discrete cosine transform, the time-domain interference signal of each pixel from the recorded speckle patterns sequence is demodulated and unwrapped to obtain the transient full-field shearographic phase distribution which indicates the dynamic surface deformation. In the meanwhile, incrementally delayed cross-correlation matched filtering allows for the localization of axial energy distribution to generate depth-selective structural display for tomography. This proposed transient-based interferometric methodology thus enables depth-tomographic profiles and three-dimensional visualization of subsurface anomalies for the first time, which significantly improves the superiority and attractiveness of shearography in providing insight into the status, performance and reliability of industry.

Pedestrians represent acutely complex and agile targets whose accident prevention has the highest priority concerning highly automated vehicles in urban traffic. Consequently, detection and tracking methods are necessary to determine motion behavior quickly and with high precision. With advancing microelectronics, powerful radar sensors and novel antenna structures are available for pedestrian detection systems, which can utilize unique features such as micro-Doppler signatures in several dimensions and high resolution. This paper presents a micro-Doppler based leg tracking framework that enables behavioral indications in a few sensor cycles. An adaptive motion model representing the kinematic locomotion of a human is derived to propagate the foot motion. Joint probabilistic data association assigns the detections to the respective leg and allows its temporal filtering of the position, micro-Doppler velocity, and kinematic parameters using an extended Kalman filter yielding real-time implementation capability. The presented approach provides the entire signal chain from raw radar data processing to extended multi-object state estimation based on highly relevant safety-critical motion maneuvers measured with radar parameterization corresponding to current serial short-range sensors.

Radar and camera information fusion sensing methods are used to solve the inherent shortcomings of the single sensor in severe weather. Our fusion scheme uses radar as the main hardware and camera as the auxiliary hardware framework. At the same time, the Mahalanobis distance is used to match the observed values of the target sequence. Data fusion based on the joint probability function method. Moreover, the algorithm was tested using actual sensor data collected from a vehicle, performing real-time environment perception. The test results show that radar and camera fusion algorithms perform better than single sensor environmental perception in severe weather, which can effectively reduce the missed detection rate of autonomous vehicle environment perception in severe weather. The fusion algorithm improves the robustness of the environment perception system and provides accurate environment perception information for the decision-making system and control system of autonomous vehicles.

Large-scale deployment of connected vehicles with cooperative sensing technologies increases the demand on the vehicular communication spectrum in 5.9 GHz allocated for the exchange of safety messages. To support the high data rates needed by such applications, the millimeter-wave (mmWave) automotive radar spectrum at 76-81 GHz can be utilized for wideband communication as well. For this purpose, various joint automotive radar-communication (JARC) systems have been proposed in the literature to perform both functions using the same wideband waveform. However, the wideband joint waveforms encounter frequency-selectivity in both radar and communication channels due to multi-path propagation. In this paper, we address the optimal joint waveform design problem to exploit the frequency-selectivity for wideband JARC operations via orthogonal frequency-division multiplexing (OFDM) wherein subcarrier coefficients are designed for optimal power allocation and phase coding. We show that the problem is a non-convex quadratically constrained quadratic programming (QCQP) problem which is known to be NP-hard. Existing approaches to solve QCQP include semidefinite relaxation (SDR) which incurs high time complexity. Instead, we propose approximation methods to solve QCQP more efficiently by leveraging structured matrices and using convex approximations. Finally, we demonstrate the efficacy of the proposed approaches through numerical simulations.

So far, numerous learned models have been pressed to use in microwave imaging problems. These models however, are oblivious to the imaging geometry. It has always been hard to bake the physical setup of the imaging array into the structure of the network, resulting in a data-intensive models that are not practical. This work put forward a graph formulation of the microwave imaging array. The architectures proposed is made cognizant of the physical setup, allowing it to incorporate the symmetries, resulting in a less data requirements. Graph convolution and attention mechanism is deployed to handle the cases of fully-connected graphs corresponding to multi-static arrays. The model works with a modular fashion at node level to strike a trade-off between flexibility and efficient capture of mutual information present in measured signals. Additionally, the modular working fashion endows the model with immunity to overfitting. The graph-treatment of the problem is evaluated on experimental setup in context of anomaly localization with imaging array and has shown higher performance as compared to the popular radar technique. The thin model was realized with a feasibly procured reasonably-sized dataset in the order of few hundreds, thus eliminating the need to resort to simulations for augmentation.

In-band full-duplex (IBFD) technology has the potential to not only double the communication throughput but also enable additional capabilities. The fusion of radar and communication subsystems is an excellent example of how IBFD facilitates integration of radar functionality into a communication system. Developing and analysis of a joint IBFD radar-communication (RadCom) system is at the core of this article. This system derives the range-Doppler image from the state of an adaptive filter, which already exists in a typical IBFD transceiver. Our approach is waveform-independent and reuses the radio frequency (RF) front-end while it requires little additional digital logic. The proposed system is prototyped to assess the modem-radar coexistence in a real-world IBFD communication link budget. Employing the prototyped system, we quantify the additional required logic resources and investigate whether such a RadCom approach dictates a tradeoff between the two intended functionalities. The experimental results show that the proposed solution enables a communication system to detect targets within 20 m while maintaining an IBFD link with another communication node, info:eu-repo/semantics/published

This paper proposes a joint uplink massive multiple-input-multiple-output (MIMO) communication and orthogonal frequency-division multiplexing (OFDM) radar sensing architecture. Specifically, uplink communication and short-range radar sensing are considered, where the user equipments (UEs) transmit data to the base-station (BS), which simultaneously receives radar returns from the targets over the same subcarriers. Hence, the signals received at each BS antenna include radar returns and communication signals to be processed for extracting the sensing and communication data. The separation and detection of such signals are achieved by utilizing the channel diversity between the UEs and the targets. To this end, the UEs’ signals are first detected and demodulated, and then subtracted from the received signal to acquire the radar returns. Symbol-based radar processing is then employed, as it provides substantial processing gains, and its detection performance is independent of the transmitted radar waveform. Furthermore, self-interference—due to the simultaneous operation of transmit and receive antennas—is taken into account. The communication rate and normalized error of the radar-target channel estimation are mathematically analyzed, and the trade-off between the achievable rate and radar detection performance is demonstrated in terms of the power output of the communication and radar sub-systems and the signal-to-noise ratio.

Miniature radar has demonstrated its great potential in smart homes to understand the wellness of the residents and provide ubiquitous interactions. While it has many promising applications, it also results in congested RF (radio frequency) environments as there is an unprecedented amount of traffic in a smart home. To ease the strain on the limited spectrum, we ask the question that, can we reuse the sensing signals for data communication With such a capability, we can improve the spectrum utilization by sharing the spectrum between sensing and communication systems. However, radar signals are customized for the sensing purpose and are incompatible with legacy communication standards. To address this challenge, we have an observation that, non-linearity effect in RF circuits can convert wideband radar signals into a LoRa signal. Based on this observation, in this paper, we present LoRadar, which enables an FMCW (Frequency-Modulated Continuous Wave) radar to carry LoRa signals in sensing waves. We present both the downlink and uplink design, enabling a LoRadar device to communicate with LoRa nodes in a bi-directional way. We implement LoRadar and evaluation results show that LoRadar can achieve home-level coverage with 3:4kbps data rate while it preserves the sensing resolution of the radar.

Model uncertainty is a serious challenge for robustness of tracking algorithms in radar systems. The smooth variable structure filter (SVSF) achieves error-bounded estimations for target state by scaling the magnitude of kinematic modeling error and accordingly performing a flexible switching strategy for the correction gain. However, the SVSF, without any smoothing functions, suffers from undesired chattering phenomenon since the measurement noise causes random disturbance to the identification of actual level of uncertainties, leading to obvious deterioration of tracking accuracy. In this paper, we present a new switching function for SVSF, i.e. the hyperbolic tangent function, for effective chattering suppression. Then we propose a new algorithm named as the Tanh-SVSF, which reformulates the correction gain with the new switching function, to improve the estimation accuracy for target state. A mathematical definition of SVSF chattering is proposed to quantify the chattering amplitude. It is demonstrated that the new switching function exerts a nonlinear compressing effect on the likelihood of measurement innovation and substantially reduces the disturbance of measurement noise, leading to elimination of the chattering problem. The stability of the Tanh-SVSF is analyzed, based on a proposed stability theorem and the numerical exhaustion strategy. Finally, the proposed method is tested on a simulated vehicle tracking scenario and real-world radar data from the Oxford Radar RobotCar Dataset, and shows superior performance over existing SVSF formulations and the Kalman filter, in view of tracking accuracy, track continuity and the proposed chattering indicator.

Deinterleaving mixtures of radar pulse sequences is the first and the most vital step for modern electronic reconnaissance systems to intercept and analyze the intentions of non-cooperative radars. Currently, these systems are facing great challenges due to the emerging complexity of radar modulations. This paper proposes a novel method for deinterleaving mixtures of radar pulse sequences based on the time series characteristics of each source (component pulse sequences). Firstly, the mathematical representations are established to describe the structural characteristics of each source. Then, the deinterleaving problem is formulated as a minimization of a maximum-likelihood cost function that can be solved efficiently through a supervised neural machine translation (NMT) network, i.e. to translate each received pulse in the interleaved pulse sequence to the corresponding source label. A sequence-to-sequence NMT model is proposed to capture the structural relationships among the non-adjacent pulses originated from the same source in the mixtures and assign the corresponding label to each pulse. The proposed method does not require the exact knowledge of each component pulse sequence. The experimental results based on the time of arrival sequence of mixed pulse sequences show that the proposed method outperforms the state-of-the-art deinterleaving methods and achieves satisfactory performance under highly non-ideal situations with measuring noise and lost pulse conditions. The proposed method can be directly applied to other fields involving deinterleaving problems and multi-variate input conditions.

This contribution presents an electronic travel aid for blind and visually-impaired people based on a millimeter wave radar and a traditional white-cane. It not only represents an advanced system able to warn the user of potential obstacles, but also discerns between human and nonhuman targets. Since real scenarios would likely have moving targets, a new range alignment technique has been proposed with the aim of detecting the tiny chest displacement due to the physiological activity as the key sign of the human presence. The proposed system is able to properly recognize humans in complex environments with multiple moving targets, thus providing to the user a complete set of information, namely presence, position and nature of the available targets. The operating principle and the effectiveness of the system are shown both by simulated case studies and by employing a 122 GHz radar board for carrying out suitable measurements.

Among all the road accidents, speeding is the most deadly factor. To reduce speeding, it is essential to devise efficient schemes for ubiquitous speed monitoring. Traditional approaches either suffers from using special equipment or special deployment. In this paper, we propose SpeedTalker, a mobile phone-based approach to perform speed detection on automobiles. SpeedTalker estimates the automobile speed by passively sensing the acoustic and image signals. Specifically, we use the time difference of arrivals (TDOA) model based on acoustic signals to figure out the candidate trajectories of automobile, and use the pin-hole model based on image frames to figure out the vertical distance between the user's position and the automobile's trajectory, thus to estimate the unique trajectory. Besides, we propose a method to effectively mitigate the influence of the movement jitters of mobile phone. We implemented a system prototype for SpeedTalker and estimated the automobile speed with high accuracy. Experiment results show that in the scenario of single automobile, SpeedTalker can achieve an average estimation error of 6.1% compared to radar speed guns. In the scenario of multiple automobiles, SpeedTalker can achieve an average estimation error of 9.8%, which is acceptable for usage.

As an essential part of the emerging Internet of Things, connected and autonomous vehicles (CAVs) have the potential to reshape future transportation systems and change the commute style in people's everyday life. Among many vehicular on-board devices, radar system and vehicle-to-vehicle (V2V) communication system are two important pillars for the realization of CAVs. In this paper, the concept of coordinated CRC (C-CRC) is proposed to serve as a cognitive mediator for bridging vehicular radar and communication systems. By establishing a mutual-assistance relationship, C-CRC provides a new safety mechanism that allows one system to learn from and react to the risks that the other system has encountered. Simulation results have shown that the proposed method has desirable performance in face of motion perturbation and/or jamming attack under various scenarios.

Accurate train positioning is crucial for train safety. In this paper, we propose a train positioning method which fuses vison and millimeter-wave radar data. The proposed method contains two parts: loop closure detection (LCD) and radar-based odometry. The loop closure detection part fuses the convolutional neural network (CNN) features and the line features to achieve accurate key location detection. The radar-based odometry part proposes a train speed measurement algorithm using millimeter-wave radar, and combines the results of loop closure detection to further realize train positioning. Experiments conducted on the Hong Kong metro Tsuen Wan line show that our proposed loop closure detection can achieve an efficient key location detection with 98.57% precision and 99.37% recall; the speed detection method fulfills the ETCS requirements; and the relative error of the proposed train positioning method is 0.45%. Besides, the proposed method has been applied on the Hong Kong Metro TSUEN WAN line.

With the continuous use of various new radar systems and complex radar systems, the electromagnetic environment is extremely deteriorated. The traditional emitter recognition methods have been difficult to meet the requirements of recognition performance in the rapidly changing battlefield environment. To solve this problem, a radar electronic signal recognition algorithm based on wavelet transform and deep learning is proposed in this paper. Starting from the radar reconnaissance system, the causes of signal preprocessing are analyzed, and the methods of signal denoising, signal normalization, signal intra pulse modulation recognition, multipath signal detection and suppression are deeply studied. In particular, the denoising algorithm based on threshold wavelet transform is proposed, which significantly improves the reliability of the algorithm. Aiming at the individual feature extraction of emitter signal, the extraction methods of emitter signal time domain feature, frequency domain feature, fuzzy function slice feature and cyclic spectrum feature based on wavelet transform are studied and analyzed, which provides stable and reliable classification features for emitter signal recognition. According to the characteristics of radar emitter signal, an optimized convolution neural network is designed, and the feature fusion processing is carried out at the decision-making level, which greatly improves the recognition effect and enhances the robustness of the recognition system. Experiments on radar data show that the fusion recognition rate is higher than any single feature and has strong robustness. In addition, compared with the traditional SVM and elm networks, the CNN network proposed in this paper can extract detailed features more effectively and improve the recognition rate of electronic radar.

In order to improve the speed of passenger screening while preserving the effective capability to detect more sophisticated threats, airport security imaging systems must be able to accurately characterize concealed body-worn objects. In addition to improving the passenger experience, this system capability will enhance airport security for the traveling public. This paper presents a real-time, fully-automatic algorithm for the wideband millimeter-wave (mm-wave) radar reconstruction of the nominal human body contours, even in the presence of an affixed weak dielectric object or when a portion of the body cross-section is not captured by the imaging scanner. The algorithm extracts the main contours from a noisy collection of 3D reconstructed reflectivity and approximates the nominal human body cross-section via fitting a low order angular Fourier series. This important step is essential for precise characterization of concealed body-worn explosives. A ranking algorithm is developed as a metric for the nominal body reconstruction accuracy. We verify the developed algorithm by applying it to the actual images of the High Definition-Advanced Imaging Technology (HD-AIT) system, a laboratory prototype mm-wave scanning system developed recently by the US Department of Homeland Security (DHS). The reconstructed body contours may be used to estimate the electric permittivity of the concealed person-worn objects.

Autonomous vehicles have been widely equipped with radars, camera, and LiDARs due to their complementary capabilities of environment perception. However, it becomes a critical task to accurately track the trajectory of the preceding vehicle when the host vehicle suffers a LiDAR failure under challenging lighting conditions. This paper proposes an integrated learning-based solution to address this critical issue. It is composed of a Q-learning-based Gaussian mixture model (QL-GMM) for clustering dense radar data, a weight-scheduled method for radar data association, and a switchable recurrent neural network (RNN) with dual-level long short-term memory (DL-LSTM) cells for trajectory tracking under LiDAR failure and various illumination levels. Specifically, the QL-GMM is first introduced to improve the conventional GMM-EM algorithm with the cluster number determined by a Q-learning approach. Then, the weight-scheduled method is presented to associate the data from multiple radars. Furthermore, the switched DL-LSTM RNN is developed to adaptively fuse the trajectories from the radar and camera streams based on three lighting modes. The training data and testing data were acquired on a fully-instrumented autonomous vehicle. Experimental verification results demonstrate that the proposed method can achieve a promising improvement for LiDAR-fault-tolerant trajectory tracking under different lighting conditions.

The sharing waveform based integrated radar and communication (IRC) has great potential to apply in Internet of Things (IoT) devices due to its equipment miniaturizing and high efficiency of spectrum and energy, etc. However, the sharing waveform scheme demands a trade-off between radar and communication performances. In this paper, we focus on the design of multiple phase shift keying (MPSK) based linear frequency modulation (LFM) sharing waveform by exploring the trade-off among energy leakage, peak-to-side lobe ratio (PSLR) and symbol rate. First, we derive the energy leakage and PSLR with respect to the symbol rate. Next, a novel LFM-MPSK waveform design is carried out by formulating a communication frame based optimization, where the symbol rate is maximized with the constraints of the energy leakage and PSLR. Then, a branch and reduce algorithm is proposed to solve the optimization. Compared with the conventional one, the proposed LFM-MPSK waveform has a higher symbol rate with the same energy leakage and PSLR. Besides, the proposed waveform design alleviates the difficulty of the trade-off among the energy leakage, PSLR and symbol rate due to more Pareto solutions. Finally, all the analytical results are validated by simulations.

A sequential decision process in which an adaptive radar system repeatedly interacts with a finite-state target channel is studied. The radar is capable of passively sensing the spectrum at regular intervals, which provides side information for the waveform selection process. The radar transmitter uses the sequence of spectrum observations as well as feedback from a collocated receiver to select waveforms which accurately estimate target parameters. It is shown that the waveform selection problem can be effectively addressed using a linear contextual bandit formulation in a manner that is both computationally feasible and sample efficient. Stochastic and adversarial linear contextual bandit models are introduced, allowing the radar to achieve effective performance in broad classes of physical environments. Simulations in a radar-communication coexistence scenario, as well as in an adversarial radar-jammer scenario, demonstrate that the proposed formulation provides a substantial improvement in target detection performance when Thompson Sampling and EXP3 algorithms are used to drive the waveform selection process. Further, it is shown that the harmful impacts of pulse-agile behavior on coherently processed radar data can be mitigated by adopting a time-varying constraint on the radar's waveform catalog., Comment: 16 pages, 9 figures. arXiv admin note: text overlap with arXiv:2010.15698

This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar (Video-SAR) as a new mode of radar imaging for real-time remote sensing and surveillance. This research also considers new suggestions in the systematic design, research taxonomy, and future trends of radar data processing. Despite the conventional modes of SAR imaging, Video-SAR can generate video sequences to obtain online monitoring and green surveillance throughout the day and night (regardless of light sources) in all weathers. First, an introduction to Video-SAR is presented. Then, some specific properties of this imaging mode are reviewed. Particularly, this research covers one of the most important aspects of the Video-SAR systems, namely, the systematic design requirements, and also some new types of visual distortions which are different from the distortions, artifacts and noises observed in the conventional imaging radar. In addition, some topics on the general features and high-performance computing of Video-SAR towards radar communications through Unmanned Aerial Vehicle (UAV) platforms, Internet of Multimedia Things (IoMT), Video-SAR data processing issues, and real-world applications are investigated.

Range-dependent clutter suppression is a challenging problem in non-sidelooking (NSL), bistatic, and conformal antenna array airborne radar, especially in the presence of range ambiguity. Superimposed stepped frequency (SSF) radar employs a small frequency increment across the stepped signals in each pulse, which induces a dimension related to slant range. In this paper, an airborne radar framework is established with SSF signal as the transmit waveform. Thus, a two-stage adaptive clutter suppression method is proposed which utilizes the degrees of freedom in range, space and time domains provided by airborne SSF radar. In the first stage, the secondary range dependence compensation (SRDC) approach is adopted to distinguish the clutter of each range region in the carrier frequency domain. Then, a target-free covariance matrix is estimated by the compensated data, and the covariance matrix is used for the range-ambiguous clutter separation in the carrier frequency domain. Thus, the space-time snapshot of each range region can be extracted. In the second stage, a clutter segmentation processing method is devised for residual clutter suppression.

The joint radar-communication (RadCom) concept has been continuously gaining interest due to the possibility of integrating radar sensing and communication functionalities in the same radio frequency hardware platform. Besides a number of challenges in terms of hardware design and signal processing, the choice of suitable modulation schemes plays a significant role in driving the performance of RadCom systems. In this sense, this article presents an overview of state-of-the-art modulation schemes for RadCom systems, namely, chirp sequence, phase-modulated continuous wave, orthogonal frequency-division multiplexing, and orthogonal chirp-division multiplexing. For each of them, a detailed system model is outlined, and parameters for quantifying both radar and communication performances are presented. Finally, a comparative analysis of the aforementioned RadCom modulation schemes is carried out to illustrate the presented discussion.

Automotive radar and vehicular communication are the two primary means of establishing an intelligent transportation system. However, both the systems are susceptible to interference. The inter-vehicular interference significantly affects the radar and communication performance, especially in the dense traffic scenarios. In this work, we have shown that lowering down radar range in dense traffic scenario provides twofold advantages; (a) it reduces radar-to-radar interference and, (b) it provides resources to support the vehicular communication. We propose a joint Radar-Communication (R-Comm) algorithm which enables connected vehicles to use a fraction of radar resources for vehicular communication based on the traffic density. Also, two different schemes have been proposed for R-Comm transmission in the sparse and dense traffic scenarios. Further, through simulation results, it is shown that R-Comm benefits both the radar and communication systems.

Airborne Distributed Coherent Aperture Radar (ADCAR) is one of the most promising next-generation radars to significantly improve target detection and discrimination abilities. However, time and phase synchronization among unit radars should be done before an ADCAR is intended to cohere on a potential target. To address this problem, a time and phase synchronization technique using clutter observations is proposed in this paper. Clutter returns from different azimuths and elevations on the surface of the earth are employed to calibrate system uncertainties. Two stages are mainly considered: a scene registration among range-Doppler units from different transmit/receive pairs is performed to enhance the clutter coherence in the first stage, followed by a joint estimation of those synchronization errors in the second stage. To relieve the computational burden, a novel Separable and Sequential Estimation (SSE) method is provided to separate the unknowns at the sacrifice of a range-Doppler unit. Moreover, performance analyses including the clutter coherence ability, estimation lower bound, and signal coherence loss are also performed. Finally, simulation results indicate that ADCAR time and phase synchronization is realized by using our methods.

Dual-function radar and communication (DRC) system has been recently recognized as a promising approach to solve the spectrum scarcity problem. However, when the target exists within a crowded area where pathloss dominating, the performance of radar may be severely degraded. To tackle this issue, this article proposes for the first time the deployment of an intelligent reflecting surface (IRS) to help the DRC system to enhance the radar detection performance. The IRS can configure the environment around the radar by adaptively adjusting the phases of its reflecting units to strengthen the signal quality toward specific directions, mostly the target direction, and completely null-out transmissions in other directions, mostly the directions toward the communication system. Specifically, in this article, we investigate the joint optimization of the IRS passive phase-shift matrix (PSM) and precoding matrix of the radar-aided basestation for the DRC system. The optimization is carried-out through maximizing the signal-to-noise ratio (SNR) at the radar receiver under both sensing and communication constraints, which turns out to be a nonconvex problem. In order to circumvent this challenging problem, an alternation optimization approach is employed to decouple the optimization variables and split this intractable problem into two subproblems. However, it is still challenging to obtain the optimal PSM due to the high power of the objective function and the unit-modulus constraints. To solve this problem, a majorization–minimization algorithm is conceived to transform the nonconvex problem to an easy to solve quadratic constraint quadratic programming problem. Simulation results demonstrate that the IRS can help improving the performance of the DRC system in terms of the received SNR, and the proposed algorithm shows fast convergence.

Remote non-contact monitoring of human vital signs has recently received lots of attention due to the advancement and availability of millimeter wave (mmWave) radars. These sensors are significantly reduced in size, but still face serious electromagnetic (EM) propagation loss and signal obstructions resulting in lower signal-to-noise ratios (SNR). As mmWave received signals also have higher sensitivity to body motions, these effects typically degrade the accuracy of heart rate (HR) detection. To overcome this challenge, MIMO configuration can be used to improve the SNR level by taking advantage of its channel diversity. We use here a Frequency Modulated Continuous Wave (FMCW) radar from Texas Instruments (TI) at 77 GHz to collect data from 192 channels. Additionally, vital sign information is extracted using Arctangent Demodulation (AD) and Maximal Ratio Combining (MRC) combined with an adapted-wavelet Continuous Wavelet Transform (CWT) are utilized to demonstrate improvement of HR estimation accuracy.

In low signal-to-noise ratio (SNR) environments, the traditional radar emitter recognition (RER) method struggles to recognize multiple radar emitter signals in parallel. This paper proposes a multi-label classification and recognition method for multiple radar-emitter modulation types based on a residual network. This method can quickly perform parallel classification and recognition of multi-modulation radar time-domain aliasing signals under low SNRs. First, we perform time-frequency analysis on the received signal to extract the normalized time-frequency image through the short-time Fourier transform (STFT). The time-frequency distribution image is then denoised using a deep normalized convolutional neural network (DNCNN). Secondly, the multi-label classification and recognition model for multi-modulation radar emitter time-domain aliasing signals is established, and learning the characteristics of radar signal time-frequency distribution image dataset to achieve the purpose of training model. Finally, time-frequency image is recognized and classified through the model, thus completing the automatic classification and recognition of the time-domain aliasing signal. Simulation results show that the proposed method can classify and recognize radar emitter signals of different modulation types in parallel under low SNRs.

Millimeter wave (mmWave) sensing promises to enable contactless and high-precision “in air" gesture-based human-computer interaction (HCI). While previous works have demonstrated its feasibility, they require tedious gesture collecting for person-independent recognition and they operate in an off-line mode without considering practical issues like segmenting gesture and recognition latency. In this work, we propose , a person-independent real-time mmWave gesture recognition solution. We first build a compact gesture model with a custom-designed neural network to distill the unique features underlying each gesture, while suppressing personalized discrepancy across different users without extra collection and re-training. Furthermore, we design a system status transition to decide when a gesture begins and ends, which enables automatic gesture segmentation and hence real-time recognition. We prototype on a commodity mmWave sensor and demonstrate its advantages using two practical applications: a contactless music player and camera. Extensive experiments and user studies show that has an accuracy of 99% and a short response latency within 25ms. Moreover, we also collect and release a comprehensive mmWave gesture dataset consisting of 54,620 instances from 144 persons, which may have an independent value of facilitating future research.

Wideband two‐dimensional multiple input multiple output (MIMO) radar can be used for target three‐dimensional (3‐D) imaging. As the echo of wideband MIMO radar is derived from both time and spatial sampling, the time‐frequency coupling is contained, resulting in imaging defocussing. To solve this problem, a time‐frequency decoupling algorithm based on matrix enhancement and matrix pencil (MEMP) is proposed to achieve high‐resolution 3‐D imaging. By combining the enhanced matrix constructed via MEMP with the modern spectral estimation theory, the time‐frequency coupling term is regarded as a space rotation factor and is eliminated in the process of target position estimation. Simulation results show that the proposed algorithm can reduce the negative influence of time‐frequency coupling and improve the imaging accuracy of wideband two‐dimensional MIMO radar.

Radar Beam-Park Experiments (BPEs) are regularly conducted in order to get insights into the statistical distribution of the space debris environment, especially at low altitudes. Signal processing techniques are necessary to process the data collected during BPEs. An improvement in these techniques may result in a corresponding improvement in terms of radar detection performance and space object parameter estimation. To this end, a transition from incoherent to coherent pulse integration could be of help, thanks to the resulting improvement in terms of Signal to Noise Ratio (SNR). Before assessing the potential of new signal processing schemes, the expected variations of the radar observables over the acquisition time have to be investigated. Indeed, deriving a priori bounds on the possible values the observables can assume, would allow adapting the BPE settings (e.g., the radar waveform or the boundaries of the matched filter) in order to optimize the processing speed and the overall detection performance. To carry out this investigation, two BPEs were simulated and the resulting beam crossing lists were exploited to understand how the satellite parameter space maps into the radar parameter subspace. This paper presents and discusses the results of the two simulations.

The main‐lobe jamming causes serious degradation of radar system performance in practical applications. In recent years, the Blind Source Separation (BSS) method has been applied to suppress the main‐lobe jamming. However, the received echoes of target and jamming cannot be effectively identified while the robustness of the conventional BSS method is poor due to the influence of system noise. To tackle these problems, a radar main‐lobe jamming suppression and identification method is proposed based on robust whitening BSS and Convolutional Neural Networks methods. First, the target echoes and jamming are separated by the proposed robust whitening BSS method. An iterative optimisation method is proposed to obtain the robust whitening matrix, and sufficient conditions are provided for solvability. Then, a Target echo and Jamming Identification Network (TJINet) is proposed to identify the signals after the robust whitening BSS. The proposed TJINet method uses residual blocks and branch structures to improve classification performance and utilises asymmetric convolution blocks to extract subtle features between the target echo and deceptive jamming. Simulation and experimental results show that the proposed method has better jamming suppression performance than the traditional methods under low signal‐to‐noise ratio (SNR) conditions, the algorithm converges faster, and also can stably identify the target echo and jamming signals.