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The integration of wireless sensors into the automotive industry is making vehicles increasingly safe and autonomous. At present, vehicles are equipped with external sensors that facilitate maneuverability and braking and internal sensors that detect presence, seat belt state, and occupant vital signs. Early detection of dysfunctional breathing patterns can reduce the risk of accidents caused by drowsiness or fatigue. In this regard, this work presents an MIMO FMCW Radar System able to distinguish dysfunctional breathing patterns from human torso motion. The torso has been divided into six regions, and the amplitude of the elongation and the frequency of motion of each region have been measured during breathing. An elongation map is then constructed in order to graphically show the dysfunctional breathing patterns. The signal processing includes a peak search algorithm to detect elongation amplitude and a bandpass frequency filter to minimize dc components, random driver motion, and vehicle vibration. A torso elongation emulator phantom has been assembled with materials of skin-like relative permittivity and dc motors for calibration and validation of basic breathing patterns. Finally, the signal of the system is compared with that of a commercial respiration belt, and then the system is tested by performing measurements on people. The MIMO radar system is able to measure, differentiate, and classify patterns associated with dysfunctional breathing such as hyperventilation syndrome and thoracic dominant breathing. This work was supported in part by the Spanish “Comision Interministerial de Ciencia y Tecnologia” (CICYT) under Project PID2019-107885GB-C31 and Project MDM2016- 0600, in part by the Catalan Research Group under Grant 2017 SGR 219, and in part by the “Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación” (SENESCYT) from the Ecuadorian Government

The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named unambiguous Doppler-based forward-looking multiple-input multiple-output (MIMO) radar beam sharpening scan (UDFMBSC) is proposed, based on the combination of MIMO processing and DBS. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data.

Linked to the increasing availability of datasets for radar-based human activity recognition (HAR), in this Student Highlights contribution, we report on a classification project that a group of 23 graduate students performed at TU Delft. The students were asked to work in groups of 2-3 members and to use the publicly available University of Glasgow dataset to develop the best classification pipeline as possible. This involved development and justification of both choices for the preprocessing techniques on the radar data (e.g., time-frequency distributions and cleaning of the signatures), and for the classification algorithms (e.g., the type of the algorithm, the hyperparameters' selection, the training-validation-testing split). While this student activity was performed at a small scale and with educational rather than research aims, we are happy to report it to the AESS readership, as we believe that such initiatives with open datasets sharing and classification algorithm benchmarking are beneficial for the wider radar research community. Furthermore, a list of publicly available datasets for radar-based HAR that can be used for similar initiatives is also reported in this article.

Radar-based gesture recognition can play a vital role in autonomous vehicles’ interaction with vulnerable road users (VRUs). However, in automotive scenarios the same gesture produces strongly differing radar responses owed to the wide range of variations such as position, orientation, or ego-motion. Since including all kinds of modifications in a measured dataset is laborious, gesture simulations alleviate the measurement effort and increase the robustness against edge and corner cases. Hence, this paper presents a flexible geometric human target model allowing the direct introduction of a wide range of modifications, while it facilitates the handling of shadowing effects and multiradar constellations. Using the proposed simulation model, a dataset recorded with a radar sensor network consisting of three chirp sequence (CS) radars is resimulated based on motion data simultaneously captured with a stereo video system. Completely substituting the measured by simulated data for training, a convolutional neural network (CNN) classifier still achieves 80.4% cross-validation accuracy on a challenging gesture set, compared to 89.4% for training on measured data. Moreover, using simulated data the classifier is shown to successfully generalize to new scenarios not observed in measurements., acceptedVersion

This article considers the detection and tracking of weak targets in multiple pulse repetition frequency (multi-PRF) radars using the track-before-detect (TBD) technique. By exploring the measurement independence among different PRFs, we decompose the joint multi-PRF and multiframe optimization problem into several lower dimensional maximizations, each of which corresponds to an intra-PRF multiframe processing. An efficient two-stage TBD procedure with a parallel structure is proposed for the multi-PRF radars. Specifically, in the first stage, a constraint inequality is derived analytically and used to decouple the measurement ambiguities from the nonlinear conversion relationship between polar and Cartesian coordinates. The intra-PRF multiframe integration can then be carried out concurrently using the ambiguous measurements and the target-like measurement plot sequences with different ambiguities are extracted for different PRFs. In the second stage, a covariance combination fusion-based inter-PRF joint disambiguation and estimation algorithm is proposed to solve the ambiguities of the plot sequences and output high-Accuracy target tracks. Simulation experiments show that the proposed algorithm can provide a good detection performance and higher tracking accuracy with much lower complexity. This work was supported in part by the National Natural Science Foundation of China under Grant 61771110, Grant U19B2017, and Grant 61871103, in part by the Fundamental Research Funds of Central Universities under Grant ZYGX2020ZB029, in part by Chang Jiang Scholars Program and the 111 Project under Grant B17008, and in part by the China Scholarship Council.

This article presents and experimentally evaluates a frequency error elimination technique suitable for unsynchronized bistatic Multiple-Input Multiple-Output (MIMO) radar for human-body detection. First, a mathematical expression of human-body localization using bistatic MIMO radar is presented. Then the direct path is used to eliminate the phase error created by the frequency difference between the transmitter and receiver. A new Doppler-shifted component of the MIMO channel without phase error is derived, and the locations of the multiple targets are calculated by the 2-dimensional MUltiple SIgnal Classification (MUSIC) method. Next, the results of simulations that examine frequency error versus power ratios are discussed to illustrate the effectiveness of the proposed method. An experiment is carried out in an indoor multipath-rich environment. To emulate the unsynchronized condition, the transmitter and receiver use independent Signal Generators (SGs). One to six targets are tested. The experiments demonstrate that our unsynchronized radar system can identify the locations of multiple targets with high accuracy.

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%.

A respiratory disorder that attacks COVID-19 patients requires intensive supervision of medical practitioners during the isolation period. A non-contact monitoring device will be a suitable solution for reducing the spread risk of the virus while monitoring the COVID-19 patient. This study uses Frequency-Modulated Continuous Wave (FMCW) radar and Machine Learning (ML) to obtain respiratory information and analyze respiratory signals, respectively. Multiple subjects in a room can be detected simultaneously by calculating the Angle of Arrival (AoA) of the received signal and utilizing the Multiple Input Multiple Output (MIMO) of FMCW radar. Fast Fourier Transform (FFT) and some signal processing are implemented to obtain a breathing waveform. ML helps the system to analyze the respiratory signals automatically. This paper also compares the performance of several ML algorithms such as Multinomial Logistic Regression (MLR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting (CB) Classifier, Multilayer Perceptron (MLP), and three proposed stacked ensemble models, namely Stacked Ensemble Classifier (SEC), Boosting Tree-based Stacked Classifier (BTSC), and Neural Stacked Ensemble Model (NSEM) to obtain the best ML model. The results show that the NSEM algorithm achieves the best performance with 97.1% accuracy. In the real-time implementation, the system could simultaneously detect several objects with different breathing characteristics and classify the respiratory signals into five different classes.

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.

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.

This paper presents a 160 GHz, D-band, low-noise amplifier (LNA) and a D-band power amplifier (PA) implemented in the Global Foundries 22 nm CMOS FDSOI. The two designs are used for the contactless monitoring of vital signs in the D-band. The LNA is based on multiple stages of a cascode amplifier topology with a common source topology adopted as the input and output stages. The input stage of the LNA is designed for simultaneous input and output matching, while the inter-stage-matching networks are designed for maximizing the voltage swing. The LNA achieved a maximum gain of 17 dB at 163 GHz. The input return loss was quite poor in the 157–166 GHz frequency band. The −3 dB gain bandwidth corresponded to 157–166 GHz. The measured noise figure was between 7.6 dB and 8 dB within the −3 dB gain bandwidth. The power amplifier achieved an output 1 dB compression point of 6.8 dBm at 159.75 GHz. The measured power consumptions of the LNA and the PA were 28.8 mW and 10.8 mW, respectively.

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.

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

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.

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.

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.

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.

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 paper presents a high-gain slotted ridge waveguide array antenna (SRWAA) with inductive diaphragms, which can realize a wide-sector beam with a low sidelobe simultaneously. The proposed antenna can cover a wide detection range and avoid interference from other directions. The expected excitation distribution for the antenna array is extracted through a beamforming method. To reduce the influence of the dispersion phenomenon on signal quality, inductive diaphragms are inserted into the sidewall of the ridge waveguide, which is fully analyzed from the point of the equivalent circuit. A cut-off-mode power divider is utilized, which can control the power ratio flexibly. A SRWAA working at 24.125 GHz, including a six-way feeding network, and a 6×24 slot array with the size of 330 mm × 66.8 mm is designed and fabricated. The measured sidelobe level (SLL) and half-power beamwidth (HPBW) in the elevation plane are -19.6 dB and 54.41°, with the counterparts in the azimuth plane -29.8 dB and 3.15°, respectively. The measured peak gain is 22.3 dBi at 24.125 GHz. The measured results are in good agreement with the simulated ones. This work has the potential to be applied in air detection, anti-unmanned aerial vehicles (UAVs), meteorological radar, and imaging radar.

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.

A theoretical sea echo modeling of the floating-platform high-frequency hybrid sky-surface wave radar (HSSWR) is presented. The ionosphere is considered to be a no-tilt reflecting plane moving in the vertical direction. Subsequently, the first-order sea clutter cross section with consideration of the platform sway and yaw motion is derived. Simulations are conducted to explore the influences of the ionospheric movement and the platform oscillation motion on the sea clutter power spectrum. Simulation results indicate that the platform sway motion may induce additional peaks that distribute symmetrically concerning the Bragg peaks, while the platform yaw motion and the ionospheric movement may broaden even split the sea clutter. Experiments are conducted with the newly-developed HSSWR system, of which the receiving arrays are simultaneously deployed on a floating platform and the shoreside. Experimental measurements concerning the frequency shift, Doppler width, and the arrival angle of the E-layer/F-layer ionosphere reflected direct wave are compared and analyzed. The study demonstrates the feasibility of the proposed modeling and provides valuable guidelines for the sea clutter characteristics analysis and the data quality assessments of the floating-platform HSSWR.

In this paper, a method of enhancing gain in a microstrip patch array antenna using two metallic plates for 24 GHz radar applications is presented. A 4 × 1 linear microstrip square patch array antenna covering the 24.0 to 24.25 GHz frequency range and using a shunt-connected series feed network with a tapered power distribution was first designed with a measured maximum gain of 9.8 dBi and dimensions of 30 mm × 12 mm. Two metallic plates were appended along the array axis of the antenna to double the gain in the 4 × 1 array antenna. Effects on performance from varying the tilting angle and length of the metallic plates, such as the input reflection coefficient, the radiation patterns, and gain, were investigated through simulation. Gain enhancement in the 4 × 1 patch array antenna with metallic plates was highest when the tilting angle was around 70∘, and gain kept increasing as the length of the metallic plates increased. A prototype of the 4 × 1 patch array antenna was fabricated with plates at a tilting angle of 70∘, a length of 50 mm, and a measured maximum gain of 16.8 dBi. Therefore, a gain enhancement of about 7 dB was achieved by adding the metallic plates along the array axis of the 4 × 1 patch array antenna.

This paper presents a method for estimating the position of a target under jammed conditions using the Time Difference of Arrival (TDOA) method. The algorithm utilizes a deep neural network to overcome the challenges posed by the jammed conditions. The simulations and results indicate that the presented method is more accurate and efficient than the traditional TDOA methods.

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.

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.

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.

Unimodular waveforms with good correlation properties are desired for multiple‐input‐multiple‐output radar to achieve an increased transmitting/receiving virtual aperture. In this study, a new optimisation framework is introduced to design waveforms with good correlations. It is shown that many existing waveform design problems can be incorporated into this framework. Since the considered problem is in general highly non‐linear and non‐convex, an iterative optimisation method based on majorisation‐minimisation is developed. By carefully devising the surrogate function, the only requirement is to deal with a series of simple problems, which have closed‐form solutions. The proposed algorithm can be implemented via fast Fourier transform and hence is computationally efficient. In addition, the proposed algorithm is extended to deal with spectral constraints. Numerical results demonstrate the efficiency of the proposed algorithm compared with the state‐of‐the‐art techniques.

When range ambiguity and range dependence coexist in forward-looking ground moving target indication (GMTI) radar, the traditional space-time adaptive processing (STAP) approaches may fail to work. To cope with this issue, a range-ambiguous clutter suppression approach based on elevation time diverse array (TDA) radar system and azimuth phase coding (APC) technique is presented in this paper. Due to the advantages of frequency scanning, the elevation TDA radar is able to cover all directions by transmitting a single time-shifted waveform. Therefore, a frequency bandpass filter is devised for each single range-bin to achieve the mainlobe echo extraction procedure. In the sequel, the azimuth phase decoding and Doppler filtering are performed to deal with the residual sidelobe ambiguous energy. With two-dimensional filtering, one can extract the unambiguous signals for each range region independently. Then, the traditional clutter compensation and STAP methods are employed to suppress the ground clutter. Finally, the effectiveness of the proposed framework in resolving range ambiguity are verified by the numerical simulation experiments.

In recent years, machine learning theory has set off a wave of research in the field of radar signal processing. In this study, a novel algorithm for sea‐surface target detection based on a stacked autoencoder (SAE) is proposed, which has already been applied in the authors’ coastal defense radar system. In the proposed algorithm, the sea surface echo data are cut into a large number of two‐dimensional (2‐D) images through a sliding window, mapping the cell under test (CUT) and the 2‐D images one by one and performing classification or detection from the perspective of 2‐D signal processing. Experimental results of simulated data and real radar data show that the proposed algorithm based on the SAE has better target detection performance compared with the traditional cell averaging constant false alarm rate (CA‐CFAR) algorithm. Besides, the proposed algorithm shows certain interference suppression ability in real data processing. As far as it is known, there is no public report on the detection of 2‐D sea surface targets based on the SAE algorithm in coastal defense radar.

Frequency diversity array multiple input multiple output (FDA-MIMO) communication system has the advantages of frequency diversity and waveform diversity, so it has attracted extensive attention, especially the joint estimation of angle and range. The existing researches mainly focus on incoherent signals, and conventional coherent signals, which can be solved by spatial smoothing based technologies. However, meter-wave MIMO radar, the target echo signal in low-elevation area has its uniqueness, and decoherence technologies such as spatial smoothing cannot be used. At present, the field of joint angle and range estimation of low-elevation target for meter-wave FDA-MIMO radar is still a gap. This letter focuses on deriving the signal model and proposes a generalized MUSIC algorithm to fill this gap. Simulation results show the effectiveness of the algorithm.

During the global epidemic, non-contact methods for monitoring the vital signs of several people have become particularly important. Advanced signal processing techniques have recently been demonstrated to separate and track the vital signs of multiple people. In this paper, we further develop the multi-person vital signs identification (VSign-ID) system to make non-contact detection available in public places. VSign-ID not only extracts multi-person vital signs but also states from whom these vital signs are collected. We utilize multiple doppler radars to expand the effective range of the measurement area and propose a space and time matching mechanism for vital signs identification. We use a thermal camera to detect the number of people and their movements. VSign-ID efficiently coordinates these two types of sensors (i.e., the doppler radars and the thermal camera) to track and identify the respiration rates and heartbeat rates of multiple people. A series of experiments and simulations are conducted to measure the efficiency of VSign-ID. In the case of five people sitting closely, the estimation errors for respiration and heartbeat rates are -4.85 dB and -2.36 dB lower than the standard resolution of the system, respectively, despite using only two independent radars.

Limited access to available spectrum motivates new age radio frequency (RF) systems to co-exist and cooperate. In this paper, we consider a joint communication and radar system (labeled Comm-Radar), that is capable of receiving and decoding communication signals while simultaneously enabling radar functionalities. Traditionally, systems that leverage principles of spectrum convergence, deemed each functions to be detrimental to the others. This has led to successive interference cancellations strategies that effectively isolate each of the functionalities. Here, in the context of Comm-Radar processing structure, we show that ambient communication transmissions actually assist in radar estimation. The insight is that communications multipath reflects off radar targets (we denote these comm-target paths) in the environment, and can be processed at the receiver to extract extra information. In this work we characterize the fundamental performance limits of such a joint system.To demonstrate the radar performance gain achievable via the processing of extra comm-target paths, the closed-form Cramer-Rao Bound (CRB) for the estimation of direction, range and velocity of a target is derived, for both the general case, and practical radar and communication signalling schemes. Based on the CRB, joint outer bounds are obtained to characterize the trade off between estimation performance and communication rate. Finally, numerical results verify the advantages of utilizing the target reflections from the communication signal to bolster radar performance.

International audience; A 3-in-1 depolarizing circular scatterer for the polarimetric radar calibration is proposed in this paper. The proposed scatterer is low cost, compact, planar circuit, and well suited for the radar operating in a compact range, for example, chipless RFID technology. By the virtue of its circular shape, the rear side of the proposed scatterer acts as a metallic disk with strong co polarization backscattered signals. The front side is composed of eight resonant dipoles which make it nondepolarizing and depolarizing scatterers at the inclination of 0° and 45°, respectively. The features of proposed circular scatterer are tested as reference calibration objects for single antenna based polarimetric radar calibration techniques. Two test objects are utilized: a dihedral tilted at 45° and a depolarizing multi resonant planar structure commonly called chipless RFID tag. The performance of proposed circular scatterer is also compared with the standard reference calibration objects: a metallic disk and a dihedral tilted at 22.5°. The performance of proposed circular scatterer is comparable to the standard reference calibration objects for the calibration techniques (namely Type 1 and Type 2). The proposed scatterer is potentially tolerant of displacement up to 1 cm and misalignment equals 2°.

In this paper, a joint design for cooperative spectrum sharing framework of multiple-input multiple-output (MIMO) radar and MIMO communication system in radar multi-target optimization is proposed. The interesting design Degrees of Freedom (DOFs) include radar transceiver beamforming vector and communication system codebook, whereby the designed problem is a constrained minimization of total radar effective interference power under power limited conditions and communication rate constraint guaranteeing both the two coexistence systems performance. As to the non-convex multi-variables problem, an alternating iteration approach is developed to solve the three suitably transformed subproblems, the closed form solutions are derived, and the convergence of the proposed design is guaranteed. Finally, numerical results show the spectrum sharing performance of the proposed approaches.

Near-field radar cross-section (NF-RCS) of a target depends on antennas’ radiation and can be maximized by adjusting the radiation patterns. It is justified by the power return ratio and can be computed by a comparison method. A theoretical foundation of operational mechanism for phased arrays of antennas is presented to produce conformal radiations for NF-RCS enhancement. The target’s scattering coefficients are extracted from operating the antenna array’s digital phase shifters to steer the resulting beams in this work. The scattering matrix is then analyzed by the singular value decomposition (SVD) to determine the antennas’ excitations for maximum NF-RCS with estimation stability. It results in orthogonal beams for the two-way radiations of antenna arrays to interpret the scattering mechanisms. The feasibility is validated by HFSS full-wave simulations and experimental measurement data.

A novel multitarget respiration monitoring radar scheme based on time-division multiple tags (TDMTs) is proposed. The key innovation in this letter is the introduction of the TDMT, which leverages the frequency-division characteristic of multiple frequency-tunable tags attached on different human bodies to implement the time division of the reflected beat signals in frequency modulated continuous wave (FMCW) system, which is used to separate respiration signals from two closely spaced human targets. To validate the proposed scheme, a radar prototype and two tunable tags have been developed. The experimental results verify the effectiveness of the proposed method by monitoring two closely spaced targets in indoor environment.

In the exploration of broadband radar absorbing structure (RAS), a new kind of multilayer gradient perforated RAS constituted of magnetic iron metal (Fe) and silicon carbide (SiC) is examined utilizing a top-down fabrication technique and metaheuristic grey wolf optimization (GWO) algorithm. The investigation of proposed structures and magneto-dielectric study of samples is carried out in 8.2 to 12.4 GHz (i.e., X-band). Distinct single and multilayered RASs are optimized using the GWO algorithm under certain restrictive conditions. In addition, the GWO algorithm is reinforced with a fitness function to acquire maximum bandwidth below -10dB threshold (RC≤-10 dB) in various oblique angles of incidence with a minimum coating thickness (t≤2.0 mm). Among all investigated RASs, triple-layer gradient perforated structure (TLGPS) is found to be effective which fulfills the lightweight, broadband, and angular stable behavior of an efficient absorber. TLGPS has considerably exhibited the concept of impedance gradient which assists in triumph broadband radar absorbing materials (RAMs) for low observable applications.

We demonstrate the efficacy of periodic corrugations, fabricated as part of the radar cover to serve the dual purpose of protecting the radar circuitry and maximizing transmission in millimeter-wave automotive radars. A 2-dimensional array of square-base pyramids engraved on the plastic radar cover was optimized to achieve minimum reflection for a wide range of incident angles, ± 40° and across the operating frequency band of 76 to 81 GHz. More importantly, we present an alternative radome texture, namely the inverse-pyramid corrugations that results in improved structural integrity as compared to the pyramidal corrugations. This novel inverse-pyramid radome structure is also much easier to manufacture and achieves similar performance as the more conventional pyramid structure. Both designs are shown to significantly reduce radar cover reflections, which is perhaps the most significant bottleneck in modern millimeter-wave automotive radar sensor performance.