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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.

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°.

Recent years have seen a steady convergence of Radar and Communications band Radio Frequency (RF) transceiver systems. Not only have Communications systems colonised large swathes of previously allocated Radar bands but there is also a convergence of technologies driven by the relatively low cost of software‐defined transceivers and solid‐sate RF sources. Thus, where conventional radar transmissions are characterised by short narrowband pulses with high peak power, new classes of ‘pulse‐compression’ radar are being developed to exploit this new technology. The resulting Low Probability of Intercept waveforms are designed to spread energy in both time and frequency, yielding a very low instantaneous power spectral density. Methods to detect, analyse and distinguish such sources require longer acquisition periods to collect more energy from the sources. Here, a novel solution is provided for detection and separation based on direction‐finding utilising polynomial matrix methods in conjunction with sparse array geometries. This approach provides enhanced detection, separation and direction finding while using relatively few antenna elements.

There has been significant research devoted to the development of distributed microwave wireless systems in recent years. The progression from large, single-platform wireless systems to collections of smaller, coordinated systems on separate platforms enables significant benefits for radar, remote sensing, communications, and other applications. The ultimate level of coordination between platforms is at the wavelength level, where separate platforms operate as a coherent distributed system. Wireless coherent distributed systems operate in essence as distributed phased arrays, and the signal gains that can be achieved scale proportionally to the number of transmitters squared multiplied by the number of receivers, providing potentially dramatic increases in wireless system capabilities enabled by increasing the number of nodes in the array. Coordinating the operations of nodes in a distributed array requires accurate control of the relative electrical states of the nodes. The basic challenge is the synchronization and stability of the relative phases of the signals transmitted or received. Generally, such control requires wireless frequency synchronization, phase calibration, and time alignment. For radar operations, phase control also requires high-accuracy knowledge of the relative positions of the nodes in the array to support beamforming. Various technologies have been developed in recent years to address the coordination challenges for closed-loop applications, such as distributed communications, and more recently, there has been growing interest in new technologies for open-loop applications, such as radar and remote sensing. This article presents an overview of distributed phased arrays, the principal challenges involved in their coordination, and recent research progress addressing these challenges.

Dual function radar communications (DFRC) systems are attractive technologies for autonomous vehicles, which utilize electromagnetic waves to constantly sense the environment while simultaneously communicating with neighbouring devices. An emerging approach to implement DFRC systems is to embed information in radar waveforms via index modulation (IM). Implementation of DFRC schemes in vehicular systems gives rise to strict constraints in terms of cost, power efficiency, and hardware complexity. In this paper, we extend IM-based DFRC systems to utilize sparse arrays and frequency modulated continuous waveforms (FMCWs), which are popular in automotive radar for their simplicity and low hardware complexity. The proposed FMCW-based radar-communications system (FRaC) operates at reduced cost and complexity by transmitting with a reduced number of radio frequency modules, combined with narrowband FMCW signalling. This is achieved via array sparsification in transmission, formulating a virtual multiple-input multiple-output array by combining the signals in one coherent processing interval, in which the narrowband waveforms are transmitted in a randomized manner. Performance analysis and numerical results show that the proposed radar scheme achieves similar resolution performance compared with a wideband radar system operating with a large receive aperture, while requiring less hardware overhead. For the communications subsystem, FRaC achieves higher rates and improved error rates compared to dual-function signalling based on conventional phase modulation., 16 pages

A multiphase continuous-wave (MPCW) Doppler radar with a multiarc circle fitting (MACF) algorithm is proposed for improving the accuracy of small periodic displacement measurements. In the proposed radar, local oscillator (LO) signals with different phases are incident to multiple I/Q mixers, to simultaneously generate multiarcs for small periodic motions. The multiarcs obtained from the synchronized data are used in the circle fitting method to provide dc offset compensation, for accurate phase demodulation. The proposed MACF algorithm, based on the Levenberg–Marquardt method, can more precisely compensate for the dc offset by combining multiarcs generated from the proposed radar. A 5.8-GHz MPCW Doppler radar module with four-phased LO signals is implemented as a demonstration. The small periodic displacement of a metal plate on a single-axis linear stage is measured using the proposed radar module and algorithm. Compared with conventional CW Doppler radar using a single arc, measurement results using the proposed radar show that the root-mean-square error (RMSE) can be effectively decreased in the periodic motion from 1 to 2 mm, at distances from 30 to 60 cm. The RMSE is reduced by more than 33% for a peak-to-peak displacement of 1 mm at a distance of 60 cm.

With the advances in radar technology, there is an increasing interest in radar-based applications for automated surveillance, tracking, and recognition. Radars capture the Doppler-induced frequency shifts, which can be utilized to recognize the small motions by vibrating or rotating parts of the target. This paper introduces a fast and compact deep Gabor neural network for human motion classification using backscattered signals from a continuous-wave Doppler radar. The joint time-frequency representation produced by the S-method is employed to depict the micro-Doppler signature of a walking person. We introduce a new Gabor layer as a generic feature extractor for designing compact neural architectures. Each Gabor layer consists of several steerable Gabor filters that can be trained to extract the salient micro-Doppler features and improve computation efficiency. For human motion classification from a distance, the Deep Gabor Network (DGN) is trained in an end-to-end manner to process the local patches in the time-frequency representation of the radar signal. A Bayesian optimization technique is employed to select the optimal network hyperparameters. The experimental results on a real radar dataset show that the proposed method achieves competitive classification rates compared to several existing approaches while having a significantly smaller model size and shorter prediction time.

Multiple input and multiple output (MIMO) radar systems have advantages over traditional phased-array radar in resolution, parameter identifiability, and target detection. However, the estimation performance of the direction of arrivals (DOAs) and the direction of departures (DODs) will be significantly degraded for a colocated MIMO radar system with unknown mutual coupling matrix (MCM). Although auxiliary sensors (AS) can be set to solve this problem, the computational cost of two-dimensional multiple signal classification (2D-MUSIC) is still large. In this paper, a new angle estimation method is proposed to reduce the computational complexity. First, a local-search range is defined for each initial angle estimation obtained by the MUSIC with AS method. Second, the new estimation of DOAs and DODs of the targets is estimated via the joint estimation theory of angle and mutual coupling coefficient in the local search area. Simulation results validate that the proposed method can obtain the same precision and have the advantage over the global searching in computational complexity.

This article presents frequency-modulated-continuous-wave (FMCW) radars developed for the detection of vital signs and gestures using two generations of 145-GHz transceivers (TRXs) integrated in 28-nm bulk CMOS. The performance and limitations of high-frequency radars are quantified with a system-level study, and the design and performance of individual circuit blocks are presented in detail. A 145-GHz center frequency and radar operation over an RF bandwidth of 10 GHz yield a displacement responsivity of 2 $\pi $ rad/mm and a windowed range resolution of 30 mm, respectively. Radar operation over a 0.1–7 m range is enabled by an effective-isotropic radiated power of 11.5 dBm and a noise figure of 8 dB. The ICs feature frequency multiplication by 9 in the transmit and receive paths, sub-arrayed dipole antennas, and neutralization of TX–RX leakage via delay control. A single TRX dissipates 500 mW from a 0.9-/1.8-V drive. The use of fast chirps (5–30- $\mu \text{s}$ ) mitigates the effect of 1/ $f$ -noise at the intermediate frequency (IF). Extensive characterization results showcase state-of-the-art performance of the TRXs, while the code-domain multiple-input and multiple-output (MIMO) radars ( $1 \times 4$ and $4 \times 4$ ) built with them demonstrate vital-sign and gesture detections.

Breathing rate monitoring is a must for hospitalized patients with the current coronavirus disease 2019 (COVID-19). We review in this paper recent implementations of breathing monitoring techniques, where both contact and remote approaches are presented. It is known that with non-contact monitoring, the patient is not tied to an instrument, which improves patients' comfort and enhances the accuracy of extracted breathing activity, since the distress generated by a contact device is avoided. Remote breathing monitoring allows screening people infected with COVID-19 by detecting abnormal respiratory patterns. However, non-contact methods show some disadvantages such as the higher set-up complexity compared to contact ones. On the other hand, many reported contact methods are mainly implemented using discrete components. While, numerous integrated solutions have been reported for non-contact techniques, such as continuous wave (CW) Doppler radar and ultrawideband (UWB) pulsed radar. These radar chips are discussed and their measured performances are summarized and compared.

International audience; Radio sources in the Very High Frequency (VHF) band can be seized as opportunity donors in a passive radar configuration such as FM radio stations and VHF omnidirectional range (VOR). A full-wave simulation of three size classes of aeroplanes shows that their bistatic radar cross-section (RCS) are statistically comparable, albeit perform differently in time while the plane is flying. This difference can be exploited to recognize the size of the aeroplanes with respect to these classes. Measurements confirm this possible differentiation between the aeroplanes within the same class. Encouraging initial results were obtained using convolutional or recurrent neural networks to classify aircraft classes, combining simulated bistatic RCS results and real trajectories (collected from automatic dependent surveillance-broadcastdata).

Under radar observation, every moving target contains a unique micro-Doppler (m-D) signature due to its structural component movement, which has been considered as one of the target class character...

In this letter, we propose a novel approach to produce attenuation forecasts for microwave links using a probabilistic approach. It uses ensembles of forecast rainfall fields to easily derive attenuation forecasts for specific frequencies. The proposed approach uses the short-term ensemble prediction system (STEPS) to generate ensembles of high, spatial and temporal, resolution forecast rainfall fields based on observed weather radar fields with lead times of 15–90 min. Attenuation forecasts could eventually be used by telecommunication operators to drive the operation of wireless networks and ensure their maintenance during severe and extreme rainfall events. This study used 109 microwave links ranging from 15 to 40 GHz to verify the results of this probabilistic attenuation forecast. Results suggest that the STEPS-based attenuation forecasts were within the narrow span of the 90% confidence region for all microwave links tested up to 30 min lead time, decreasing for longer lead times. Examples of how the proposed approach can be used to derive a detailed probabilistic attenuation forecast for multiple lead times within a domain of few kilometers, as well as probability of attenuation maps for large areas are shown.

Many literatures regarding the coexisting radar and communications assume that some information is exchanged mutually, i.e., they work in a cooperative or coordinated manner. In this letter, we consider an uncoordinated scenario to examine the influence of the unknown communication interference on the active radar target detection, just provided with a known power of the transmitted communication signal. To this end, a numerical generalized likelihood ratio test (GLRT) in the partially homogeneous clutter is derived to handle this problem, with the maximum likelihood estimations (MLEs) approximately solved. Simulations illustrate the superiority of the addressed detector, principally in the strong interference environment.

The Internet of Things (IoT) is a reality that changes several aspects of our daily life, from smart home monitoring to the management of critical infrastructure. The “Routing Protocol for low power and Lossy networks” (RPL) is the only de-facto standardized routing protocol in IoT networks and is thus deployed in environmental monitoring, healthcare, smart building, and many other IoT applications. In literature, we can find several attacks aiming to affect and disrupt RPL-based networks. Therefore, it is fundamental to develop security mechanisms that detect and mitigate any potential attack in RPL-based networks. Current state-of-the-art security solutions deal with very few attacks while introducing heavy mechanisms at the expense of IoT devices and the overall network performance. In this work, we aim to develop an Intrusion Detection System (IDS) capable of dealing with multiple attacks while avoiding any RPL overhead. The proposed system is called DETONAR - DETector of rOutiNg Attacks in Rpl - and it relies on a packet sniffing approach. DETONAR uses a combination of signature and anomaly-based rules to identify any malicious behavior in the traffic (e.g., application and DIO packets). To the best of our knowledge, there are no exhaustive datasets containing RPL traffic for a vast range of attacks. To overcome this issue and evaluate our IDS, we propose RADAR - Routing Attacks DAtaset for Rpl: the dataset contains five simulations for each of the 14 considered attacks in 16 static-nodes networks. DETONAR’s attack detection exceeds 80% for 10 attacks out of 14, while maintaining false positives close to zero.

A method is developed, analyzed, and tested to adapt low-cost, automotive-grade radar chipsets for long-range sensing. These disruptive chipsets offer impressive performance at low size, weight, power, and cost (SWaP-C) that could benefit applications with tight SWaP-C budgets such as urban air mobility and urban air logistics. The short range of these radars currently prevents their deployment in long-range applications, so this paper employs extended measurement intervals coupled with sophisticated motion modeling and signal processing to significantly extend their range. After deriving the optimal maximum likelihood estimator, the paper presents suboptimal, more efficient techniques for target range estimation that are robust to target motion uncertainty. These techniques are validated in simulation and demonstrated via experiment. The results show that low SWaP-C radar chipsets are capable of operating at low SNR to perform long-range sensing when augmented with this paper's motion modeling and signal processing techniques. This potent combination of low SWaP-C hardware and advanced signal processing will drive innovation in urban air mobility, urban air logistics, and other areas in need of long-range sensing.

We propose a high-resolution imaging radar system to enable high-fidelity four-dimensional (4D) sensing for autonomous driving, i.e., range, Doppler, azimuth, and elevation, through a joint sparsity design in frequency spectrum and array configurations. To accommodate a high number of automotive radars operating at the same frequency band while avoiding mutual interference, random sparse step-frequency waveform (RSSFW) is proposed to synthesize a large effective bandwidth to achieve high range resolution profiles. To mitigate high range sidelobes in RSSFW radars, optimal weights are designed to minimize the peak sidelobe level such that targets with a relatively small radar cross section are detectable without introducing high probability of false alarm. We extend the RSSFW concept to multi-input multi-output (MIMO) radar by applying phase codes along slow time to synthesize a two-dimensional (2D) sparse array with hundreds of virtual array elements to enable high-resolution direction finding in both azimuth and elevation. The 2D sparse array acts as a sub-Nyquist sampler of the corresponding uniform rectangular array (URA) with half-wavelength interelement spacing, and the corresponding URA response is recovered by completing a low-rank block Hankel matrix. Consequently, the high sidelobes in the azimuth and elevation spectra are greatly suppressed so that weak targets can be reliably detected. The proposed imaging radar provides point clouds with a resolution comparable to LiDAR but with a much lower cost. Numerical simulations are conducted to demonstrate the performance of the proposed 4D imaging radar system with joint sparsity in frequency spectrum and antenna arrays.

Emerging 5G millimeter-wave (mm-wave) networks use electronic beamforming and beamsteering and support signal bandwidths on the order of hundreds of MHz. Given these characteristics, opportunities exist to develop 3-D sensing applications that leverage 5G mm-wave communications infrastructure. In this context, this work introduces a signal processing pipeline that can: 1) accurately extract the Time of Flight (ToF) of reflected orthogonal frequency division multiplexing (OFDM) communications signals and 2) enhance range resolution by coherently aggregating the reflection information from separate frequency bands. In combination with precise beamsteering, the proposed signal processing techniques enable high-resolution 3-D radar imaging without affecting communications protocols . An experimental system demonstrating this concept has been implemented and is described. This system consists of two software-defined phased array radios (SDPARs), one configured as a prototype 5G base station TX, and one as an auxiliary prototype 5G RX. Each SDPAR primarily consists of a Si-based 28-GHz, 64-element, phased array transceiver module and software-defined radio. Simulation and benchmark results show that our coherent bandwidth stitching enables accurate OFDM-based ranging with 15-cm resolution. Measurement results show 3-D radar images of indoor scenes with 2° angular and 15-cm ranging resolution, created by processing reflected 5G-like communication waveforms at 28 GHz. The produced 3-D radar images effectively depict the location of objects in the scene, and these locations are in close agreement with the ground truth.

This letter presents a simple, lightweight, and ultrawideband, the very high frequency/ultrahigh frequency (VHF/UHF) open-sleeve dipole antenna array for airborne ice measuring radar. The proposed antenna array achieves a wide bandwidth of 300 MHz via mutual coupling from neighboring excited elements. A hollow aluminum tube is used for both dipole and parasitic elements to minimize the antenna's weight. The simulation results show that to achieve a bandwidth of 300 MHz with a peak realized gain up to 12 dBi in the frequency range from 170 to 470 MHz, the length of the dipole ( L dip) and parasitic elements ( L para) of elements 1 and 4 of the four-element antenna array need to be 670 mm and 200 mm, respectively. At the same time, elements 2 and 3 require an L dip of 640 mm and L para of 180 mm. Based on these dimensions, a four-element antenna array is fabricated and tested. The measured antenna performance results show a good agreement with the simulated results.

We consider the problem of corrupted radar super-resolution, a generalization of compressed radar super-resolution in which one aims to recover the continuous-valued delay-Doppler pairs of moving objects from a collection of corrupted and noisy measurements. The received signal in this type consists of contributions from objects, outlier and noise. While this problem is ill-posed in general, tractable recovery is possible when both the number of objects and corrupted measurements are limited. In this brief, we propose an atomic norm optimization in order to find the delay-Doppler pairs and the outlier signal. The objective function of our optimization problem encourages both sparsity in the continuous delay-Doppler domain and the sparsity of perturbations in the outlier signal. We show that the associated problem can be solved via semidefinite programming (SDP). Moreover, we provide a reasonably fast and efficient algorithm for solving this SDP based on the alternating method of multipliers (ADMM). Simulations results verify the superior performance of our proposed problem and the high accuracy and low computational complexity of our algorithm.

Within the autonomous driving community, millimeter-wave frequency-modulated continuous-wave (FMCW) radars are not used to their fullest potential. Classical, hand-designed target detection algorithms are applied in the signal processing chain and the rich contextual information is discarded. This early discarding of information limits what can be applied in algorithms further downstream. In contrast with object detection in camera images, radar has thus been unable to benefit fully from data-driven methods. This work seeks to bridge this gap by providing the community with a diverse, minimally processed FMCW radar dataset that is not only RGB-D (color and depth) aligned but also synchronized with inertial measurement unit (IMU) measurements in the presence of ego-motion. Moreover, having time-synchronized measurements allow for verification, automated or assisted labelling of the radar data, and opens the door for novel methods of fusing the data from a variety of sensors. We present a system that could be built with accessible, off-the-shelf components within a $1000 budget and an accompanying dataset consisting of diverse scenes spanning indoor, urban and highway driving. Finally, we demonstrated the ability to go beyond classical radar object detection with our dataset with a classification accuracy of 85.1% using the low-level radar signals captured by our system, supporting our argument that there is value in retaining the information discarded by current radar pipelines.

This letter presents a 0.1–4.0-GHz broadband serial direct-sequence spread-spectrum (DS/SS)-based inductor-free ground-penetrating radar (GPR) system-on-chip (SoC). A time-domain digital correlation-based time-of-flight measurement is employed for radar ranging instead of using a frequency chirp or pulse, which allows a higher degree of flexibility. With no conventional RF up/down conversion present, the radar offers complete programmability over frequency and bandwidth (BW) up to 4.0 GHz. In-field GPR test with actual rover prototype is performed to validate the capability and characterize the overall performance of the DS/SS radar system. Implemented in 28-nm CMOS, the demonstrated prototype radar SoC offers a fine resolution of 3.75 cm and consumes 46.2 mW from 1-V supply. This makes it suitable for low-power applications such as planetary exploration.

An optoelectronic millimeter-wave (mm-wave) radar transmitter (TX) integrated circuit (IC) for multiple-input–multiple-output (MIMO) imaging radar in silicon photonics technology is presented. The MIMO radar system architecture in which the ICs shall be used features an optical local oscillator (LO) distributed over fiber to coherently operate mm-wave transceiver frontends. Optical LO distribution enables larger apertures and hence higher angular resolution for MIMO imaging radars. The optoelectronic TX frontend IC integrates all electronic and photonic components needed to receive the optical LO signal and to generate a transmit signal at mm-wave. It operates on both 1310- and 1550-nm LO wavelengths. The chip was designed in a pre-production photonic SiGe BiCMOS technology from IHP. Measured output power is 13 dBm at 67 GHz and 5 dBm at 77 GHz from −5-dBm optical modulation amplitude (OMA). The IC dissipates 900 mW from a single 3.6-V supply voltage.

This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian (HTG) with structural geometry parameters that can be learned from the training data. The HTG measurement model provides an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Moreover, large-scale radar datasets can be leveraged to learn the geometry-related model parameters and offload the computationally demanding model parameter estimation from the state update step. The learned HTG measurement model is further incorporated into a random matrix based EOT approach with two (multi-sensor) measurement updates: one is based on a factorized Gaussian inverse-Wishart density representation and the other is based on a Rao-Blackwellized particle density representation. The effectiveness of the proposed approaches is verified on both synthetic data and real-world nuScenes dataset over 300 trajectories.

Human activity recognition has become an obligatory necessity in day to day life and possible solutions can be provided with the technological advancement of sensing field. Radar based sensing with its unbeatable unique features has been a promising solution for identifying and distinguishing human activities in recent years. The ascent of loss of life among elderly people in care homes during COVID-19 is mainly due to poor monitoring services, that was not able to track their daily life activities. This has even more emphasized the need for savvy activity monitoring and tracking system. In this work, we have used a dataset that has captured six daily life activities of people from different locations during different times under realistic environments, unlike an regular controlled data collection environment. We have proposed a novel tower based convolutional neural network architecture that has employed parallel input layers with individual color channel images sent as inputs to the model. We have concatenated all the unique signature features from each channel to have better and robust feature representation to the model. We have analyzed the proposed model with different color spaces like RGB, LAB, HSV as inputs and found that our chosen input type performs better with the proposed model with significant test accuracy results. We have also compared our proposed model with other existing state of art architectures for radar based human activity recognition.

Automotive imaging radars require high angular resolution which can be achieved by a large antenna aperture. In order to obey Nyquist spatial sampling rate, a large number of array elements and receive channels is required. In practice, this solution results in a prohibitively high cost and complexity. This work proposes a new cognitive receiver configuration, in which a large number of sensor array elements is connected to a small number of receive channels via a switching matrix. The state of the switching matrix is sequentially updated using information from previous observations and prior information. According to the proposed scheme, denoted as cognitive antenna selection (CASE), the state of the switching matrix is obtained by the minimization of conditional Bayesian bounds on the mean-squared-error of the direction-of-arrival estimate. We show that the Bayesian Cramer-Rao bound (BCRB) is an inappropriate optimization criterion since it ignores the effect of ambiguity. This work proposes the Bobrovski-Zakai bound (BZB), which accounts for the effect of ambiguity, as a criterion for cognitive antenna selection. The performance of the proposed CASE-BZB approach is evaluated via simulations in single and multiple target scenarios. It is shown that the CASE-BZB outperforms random and linear switching algorithms both asymptotically and in the threshold region.

This letter reports the measurements and results of tests conducted on the detection of unmanned aircraft systems (UASs) or drones using a millimeter-wave (mmWave) automotive radar sensor. Designed for the detection of automotive targets with radar cross section (RCS) in excess of 20 dBsm, these miniaturized radar sensors have demonstrated detection and ranging performance in detecting a UAS, whose RCS is typically less than $-$ 15 dBsm. The sensor operates in the 76–81 GHz (mmWave) band and employs the frequency-modulated continuous-wave principle with a maximum output power of 16 mW. The detection capability of the sensor in terms of range and radial velocity measurements was investigated using a Type-I micro-UAS with a mean RCS less than $-$ 20 dBsm. In addition to detection, ranging, and velocity measurements, the RCS and the micro-Doppler signature measurements of the UAS in flight are also presented. The measurements and analysis suggest that these mmWave sensors are ideally suited as low-cost sensors for the detection of micro- and mini-classes of UASs at short ranges. The micro-Doppler signature analysis reveals the versatility of mmWave sensors in the extraction of micro-Doppler signatures, which serves as an effective tool for the classification of UASs from other airborne nuisance targets such as birds and moving clutter.

Caused by the platform movement, slowly moving targets on the ground will be hidden by the stationary clutter in the Doppler domain for an airborne passive radar system. In this paper, the signal model of airborne passive radar used for ground moving target indication purpose is established at first. Then, the space–time adaptive processing technique is adopted to suppress the strong clutter. Because of the poorer range resolution than active radar, independently and identically distributed training range cells are more difficult to obtain for passive radar. To address this problem, the sparsity of clutter in the space–time domain is exploited and a jointly sparse matrix recovery model is introduced. Furthermore, a parameter-searched two-dimensional multiple measurement vectors based orthogonal matching pursuit (MOMP) algorithm is proposed to solve the off-grid (basis mismatch) problem and reduce the computational complexity at the same time, and thus to more accurately and effectively estimate the clutter covariance matrix. Simulation results with digital TV signal as the illuminator source are shown to demonstrate the effectiveness of the proposed algorithm to determine the Doppler frequency, spatial frequency, and relative bistatic range of the moving targets on the ground.

Narrowband and broadband indoor radar images significantly deteriorate in the presence of target‐dependent and target‐independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating the wall clutter in indoor radar images. The algorithm relies on the availability of clean images and the corresponding noisy images during training and requires no additional information regarding the wall characteristics. The algorithm is evaluated on simulated Doppler‐time spectrograms and high‐range resolution profiles generated for diverse radar frequencies and wall characteristics in around‐the‐corner radar (ACR) scenarios. Additional experiments are performed on range‐enhanced frontal images generated from measurements gathered from a wideband radio frequency imaging sensor. The results from the experiments show that the StackedSDAE successfully reconstructs images that closely resemble those that would be obtained in free space conditions. Furthermore, the incorporation of sparsity and depth in the hidden layer representations within the autoencoder makes the algorithm more robust to low signal‐to‐noise ratio (SNR) and label mismatch between clean and corrupt data during training than the conventional single‐layer DAE. For example, the denoised ACR signatures show a structural similarity above 0.75 to clean free space images at SNR of −10 dB and label mismatch error of 50%.

High-Resolution Range Profile (HRRP) is effective in various Radar Automatic Target Recognition problems. Multi-scale techniques have been verified in aircraft target HRRP feature enrichment to ach...

A new method for detecting microwave harmonic Doppler signatures of held objects in cluttered environments is presented. Based on a passive harmonic tag combined with a pulsed microwave radar, the motion of tagged objects held by a person can be detected in high-clutter environments. In this letter, we present a new derivation of the dynamic harmonic signal response of a harmonic tag in motion. Based on a truncated Bessel function expansion, the model predicts the time-frequency signatures of sinusoidally moving objects with high accuracy. We present a pulsed harmonic radar transmitting at 2.51 GHz and receiving at 5.02 GHz, which is capable of measuring the harmonic micro-Doppler response of the tag with better signal-to-noise since the clutter response only exists at 2.51 GHz. The pulse waveform provides detection of the harmonic tag at larger distances than continuous-wave systems by increasing the peak power of the signal incident on the tag, while the average power is fixed with a low level. Measurements of a held tag at a distance of 1 m in a highly cluttered environment are presented, showing a strong match to the simulated results.

This article presents a new universal radar target simulator (RTS) principle based on multirate sampling. The proposed concept enables a realistic emulation of moving targets independently of the radar type and signal and is suited for all common modulation types, such as frequency-modulated continuous-wave (FMCW), chirp sequence (CS), orthogonal frequency division multiplexing (OFDM), and pseudorandom impulse sequences. This versatile applicability is achieved by delaying the radar signal with a time-variant delay that is changed continuously over time. Analog systems use mixing to simulate moving targets, which is not precise. Standard digital radio frequency memory (DRFM) systems with discrete programmable time delays, in contrast, typically drop or delay samples to simulate moving targets. This time-delay discretization, however, introduces discontinuities to waveforms and therefore creates spurious signals. These distortions particularly affect processing in the Doppler dimension. To overcome this disadvantage, the proposed multirate system simulates continuous target movement and the Doppler effect of real targets by using different sample rates for an analog-to-digital converter (ADC) and digital-to-analog converter (DAC). The resulting Doppler phase variation equals that of a real moving target exactly. With this multirate RTS, even slow-moving real-world targets can be mimicked accurately. The attractive properties of the proposed RTS concept are verified by real-world measurements with a 77-GHz demonstration system.

Surface movement radar (SMR) is emerging as critical air traffic control to prevent runway incursion. In order to reach, or exceed, the demanding requirements for the International Civil Aviation Organization, this paper aims to develop an SMR signal chain architecture which extracts information about radar objects of interest in airport surveillance video. This research results can be integrated into radar analyzer and compressor data processor to enable the system to quickly detect objects of moving or stationary. It can work normally in the case of pulses are lost in burst transmission or the resources. The state-of-the-art performance of this signal chain architecture is verified by simulations with X-band radar trials data are collected at Kunming Changshui International Airport of China, which ensure the reliability of research. The work presented here is practical and straightforward; it brings an evident engineering application prospect.

This work presents the implementation of a synthetic aperture radar (SAR) at 77 GHz, for automotive applications. This implementation is unique in the sense that it is a radar-only solution for most use-cases. The set-up consists of two radar sensors, one to calculate the ego trajectory and the second for SAR measurements. Thus the need for expensive GNSS-based dead reckoning systems, which are in any case not accurate enough to fulfill the requirements for SAR, is eliminated. The results presented here have been obtained from a SAR implementation which is able to deliver processed images in a matter of seconds from the point where the targets were measured. This has been accomplished using radar sensors which will be commercially available in the near future. Hence the results are easily reproducible since the deployed radars are not special research prototypes. The successful widespread use of SAR in the automotive industry will be a large step forward toward developing automated parking functions which will be far superior to today's systems based on ultrasound sensors and radar (short range) beam-forming algorithms. The same short-range radar can be used for SAR, and the ultrasound sensors can thus be completely omitted from the vehicle.

Object detection using FMCW (Frequency-modulated continuous wave) radars is of massive importance for the advanced driver assistance systems. However, it is exceptionally challenging due to the diversity of the electromagnetic environment and the existence of the class imbalance in the radar data space. In this paper, we propose a cascaded object detection network to achieve accurate object detection using FMCW radars. Consisting of a ROI generation stage and a final detection stage, the proposed cascaded network can tackle the problem of the class imbalance and detect objects from the range-Doppler or range-velocity space effectively. Besides, we propose a range-velocity regression procedure to improve the performance of the range-velocity localization. Extensive simulation experiments demonstrate that our proposed approach can robustly detect objects from noisy electromagnetic environments with a high localization accuracy.

In the presence of Weibull clutter, the development of sliding window detection processes, based on scale and power invariant distributions, has been extensively examined. This involves the selection of two functions labeled as scale-invariant and secondary CRP (clutter range profile) functions. However, due to the presence of outliers, existing CFAR (constant false alarm rate) algorithms show remarkable CFAR losses. We resort in this work to the practice of a suitable choice of these two functions in order to have a new decision rule with immunity against interfering targets. To do this, a quadruple-order statistics-based CFAR detection algorithm with different four-order statistics are proposed in the presence of log-normal and Weibull clutter disturbances. Via Monte Carlo simulations, the analysis of the false alarm regulation of the proposed detector is studied showing its robustness with respect to clutter parameters. Moreover, for comparison purposes with existing CFAR algorithms, simulated results indicate that lowest CFAR losses can be obtained by the proposed quadruple-order statistics named WHWH-CFAR (Weber–Haykin-Weber–Haykin) in the presence of strong interfering targets. IPIX real data are also performed to test the validity of the proposed detector.

This paper introduces a new maritime search and rescue system based on S-band illumination harmonic radar (HR). Passive and active tags have been developed and tested while attached to life jackets and a small boat. In this demonstration test carried out on the Baltic Sea, the system was able to detect and range the active tags up to a distance of 5800 m using an illumination signal transmit-power of 100 W. Special attention is given to the development, performance, and conceptual differences between passive and active tags used in the system. Guidelines for achieving a high HR dynamic range, including a system components description, are given and a comparison with other HR systems is performed. System integration with a commercial maritime X-band navigation radar is shown to demonstrate a solution for rapid search and rescue response and quick localization.

In this paper, a recognition model based on the improved hybrid particle swarm optimisation (HPSO) optimised backpropagation network (BP) is proposed to improve the efficiency of radar working state recognition. First, the model improves the HPSO algorithm through the nonlinear decreasing inertia weight by adding the deceleration factor and asynchronous learning factor. Then, the BP neural network’s initial weights and thresholds are optimised to overcome the shortcomings of slow convergence rate and falling into local optima. In the simulation experiment, improved HPSO-BP recognition models were established based on the datasets for three radar types, and these models were subsequently compared to other recognition models. The results reveal that the improved HPSO-BP recognition model has better prediction accuracy and convergence rate. The recognition accuracy of different radar types exceeded 97%, which demonstrates the feasibility and generalisation of the model applied to radar working state recognition.

In this letter, a simple data augmentation method for micro-Doppler radar-based human activity recognition (HAR) is proposed. The proposed augmentation method can improve the performance of a neural network with insufficient training samples. It is applied directly to the spectrograms of the human activity radar data. The augmentation strategy consists of three operations: 1) time shift, 2) frequency disturbance, and 3) frequency shift. Without destroying this kinematic information in the spectrograms, the three operations are used to change the three attributes, i.e., dynamic-static state, instantaneous speed, and overall speed, of human motion spectrograms. The experimental results show that the proposed augmentation method can significantly improve the recognition accuracy of different classic deep models used in radar-based HAR. Moreover, we performed another experiment that utilizes the different groups of volunteers’ data for training and testing. The results reveal that the generalization ability of the network can be significantly improved by the proposed augmentation method.

Lately, RaD of microwave vacuum electron devices (MVEDs) has played a very important role in the technologies of broadcasting, communication, radar, remote imaging, electronic countermeasures, materials research, deep space exploration, and wave spectroscopy. In the 1970s, this widespread role could not have been predicted, as the development of vacuum electron devices faced a serious challenge from solid-state electron devices. However, in solid-state electron devices, the electron stream is a conduction (ohmic, collisional) current, whereas in vacuum electron devices, the current is a convection (ballistic, collisionless) current. So the vacuum is perfect for high-power devices needing high-power electron currents, especially because vacuum electron devices are intrinsically superior at handling high power or high power density, giving vacuum electronics research an edge in high power and high frequency [1]-[5].

Unlike traditional phased-array-radar, multiple-input multiple-output (MIMO) radar is capable of achieving more degrees-of-freedom (DOF) and larger virtual aperture (VA) for the direction-of-arrival (DOA) estimation. In this letter, a new universal antenna array approach employing a one-level transmitter (Tx) and generalized receiver (Rx) is presented for MIMO radar to enhance the DOA estimation performance. The proposed configuration significantly improves the DOF capacity and expands VA by utilizing difference co-array of sum co-array method. The first-level of the Rx and a Tx with increased inter-element spacing is constructed using the nested-coprime array with displaced sub-arrays structure. The proposed Rx is transformable, generic, and easier to realize. It exploits symmetrical array geometry, such that once the initial-level is designed, the remaining stages will be known. The Rx also reveals an inbuilt Tx. Moreover, it achieves increased DOF and enlarged VA, reflecting the multifaceted advantages of the new Rx approach. The configuration is also benefited with closed-form expressions for VA, sensor locations, and DOF. Simulation results demonstrate the superiority of the proposed MIMO radar.

Radar target range profile possesses advantages in many target recognition problems. Feature extraction techniques could optimize target recognition performance, but conventional methods neglect specific characters of electromagnetic scattering mechanisms in range profiles. This letter introduces a range profile segmentation algorithm, which decomposes target range profile into segments corresponding to particular electromagnetic scattering mechanisms. Segment type identification and weighing algorithms are also developed for ensemble classification strategy. Results indicate that the proposed method could achieve comparable classification accuracy as advanced neural networks but requires far fewer computational resources to guarantee its application flexibility.

Publisher Copyright: IEEE Copyright: Copyright 2021 Elsevier B.V., All rights reserved. The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. Harnessing the power of multilinear algebra through tensor analysis in wireless signal processing, channel modeling, and parametric channel estimation provides greater flexibility in the choice of constraints on data properties and permits extraction of more general latent data components than matrix-based methods.Tensor analysis has also found applications in Multiple-Input Multiple-Output (MIMO) radar because of its ability to exploit the inherent higher-dimensional signal structures therein. In this paper, we provide a broad overview of tensor analysis in wireless communications and MIMO radar. More specifically, we cover topics including basic tensor operations, common tensor decompositions via canonical polyadic and Tucker factorization models, wireless communications applications ranging from blind symbol recovery to channel parameter estimation, and transmit beamspace design and target parameter estimation in MIMO radar.

We introduce the concepts, methodologies, and applications of meteorological electromagnetics with a focus on snow, which currently is the least understood component of the global water cycle. As "no two snowflakes are alike," the intricacies of snowflakes and snowfall are both truly fascinating and extremely challenging to measure, analyze, and predict. We describe a unique approach to the characterization of winter precipitation through the synergistic use of advanced optical instrumentation for in situ microphysical and geometrical measurements of ice and snow particles; image processing techniques to obtain the fall speed, size distribution, 3D shape (mesh), density, and effective dielectric constant of snowflakes; method of moments (MoM) scattering computations of precipitation particles; and state-of-the-art dualpolarization radars for the measurement of polarimetric scattering observables. We discuss the operations, observations, and analyses using this approach during a snow field campaign that took place in Colorado, United States, from 2014 to 2017, and we also introduce an international collaborative field program in association with the 2018 Winter Olympics in South Korea. One goal of this article is to promote meteorological electromagnetics as an interdisciplinary field where nature, science, and technology meet in some of the most fascinating and rewarding ways and where many key areas of interest and endeavors of the antennas and propagation community play an indispensable role.

Multi-point vehicular positioning is an essential operation for autonomous vehicles. However, the state-of-the-art positioning technologies, relying on reflected signals from a target (i.e., RADAR and LIDAR), cannot work without line-of-sight (LoS). Besides, it takes significant time for environment scanning and object recognition with potential detection inaccuracy, especially in complex urban situations. Some recent fatal accidents involving autonomous vehicles further expose such limitations. In this article, we aim at overcoming these limitations by proposing a novel relative positioning approach, called Cooperative Multi-point Positioning (COMPOP). The COMPOP establishes cooperation between a target vehicle (TV) and a sensing vehicle (SV) if a LoS path exists, where a TV explicitly lets an SV to know the TV’s existence by transmitting positioning waveforms. This cooperation makes it possible to remove the time-consuming scanning and target recognizing processes, facilitating real-time positioning. One prerequisite for the cooperation is a clock synchronization between a pair of TV and SV. To this end, we use a phase-differential-of-arrival (PDoA) based approach to remove the TV-SV clock difference from the received signal. With clock difference correction, the TV’s position can be obtained via peak detection over a 3D power spectrum constructed by a Fourier transform (FT) based algorithm. The COMPOP also incorporates nearby vehicles, without knowing their locations, into the above cooperation for the case without a LoS path. Specifically, several strong non-LoS (NLoS) links from the TV to the SV can be generated via mirror-like reflections over the neighboring vehicles’ metal surfaces. Following the same procedures in the LoS case, virtual TVs mirrored by nearby vehicles can be detected. By exploiting the geometric relation between the virtual and actual TVs, COMPOP can be achieved by intelligently combining the virtual TVs to position the actual TV. The effectiveness of the COMPOP is verified by several simulations concerning practical channel parameters.

IEEE In this paper, we investigate the network utility maximization problem in a dual-functional radar-communication multi-unmanned aerial vehicle (multi-UAV) network where multiple UAVs serve a group of communication users and cooperatively sense the target simultaneously. To balance the communication and sensing performance, we formulate a joint UAV location, user association, and UAV transmission power control problem to maximize the total network utility under the constraint of localization accuracy. We then propose a computationally practical method to solve this NP-hard problem by decomposing it into three sub-problems, i.e., UAV location optimization, user association and transmission power control. Three mechanisms are then introduced to solve the three sub-problems based on spectral clustering, coalition game, and successive convex approximation, respectively. The spectral clustering result provides an initial solution for user association. Based on the three mechanisms, an overall algorithm is proposed to iteratively solve the whole problem. We demonstrate that the proposed algorithm improves the minimum user data rate significantly, as well as the fairness of the network. Moreover, the proposed algorithm increases the network utility with a lower power consumption and similar localization accuracy, compared to conventional techniques.

This letter proposes a one-dimensional multiple signal classification (MUSIC) algorithm for the joint angle and range estimation in monostatic multi-input multi-output (MIMO) radar with frequency diverse array (FDA). The proposed algorithm can reduce the computational complexity greatly compared to the two-dimensional MUSIC (2D-MUSIC) algorithm but have the same performance as the 2D-MUSIC algorithm. Simulation results verify the performance of the proposed algorithm.

Traveling waves can be supported by long, smooth surfaces under horizontal polarization, such as the wings or bodies of aircraft. A 3-D scattering center (SC) model for traveling waves is proposed in this work. The equivalent location of the SC and the dependence function of scattering amplitude on aspect angles is derived based on traveling wave antenna theory and modified according to real scattering characteristics. Chamber-measured data and full-wave numerical results of a series of geometries are presented to verify the validity of this model in imaging simulation. We find that the equivalent position of this SC is located along a circular arc. In consideration of its polarization characteristics and inverse synthetic aperture radar (ISAR) image signatures, the proposed SC model is promising for used in the model-based automatic target recognition (ATR) to distinguish traveling waves from other scattering components.

Tapering an active array antenna system on transmit lowers the sidelobe level, but it adversely affects equivalent isotropically radiated power (EIRP) by reducing both power and gain. In this article, we analyze this effect. First, we propose a new taper efficiency coefficient, which allows us to assess the effect of tapering on EIRP (in contrast to the standard taper efficiency that describes the effect of tapering on gain only). Then, we present an optimization method to compute the taper weights that maximize the defined efficiency for an assumed bound on the sidelobe level. The obtained optimal weights can be used not only to taper an antenna array but also as a benchmark for other tapers. Therefore, we analyze several known tapers and compare their transmit efficiencies. As a result, we find that for some combinations of sidelobe level and array length, the proposed optimal taper provides substantially better efficiency than Dolph–Chebyshev or Taylor tapers.

In this article, a hybrid radar configuration for automotive radar applications is proposed, with the system architecture, design, and measured results reported. The hybrid radar system consists of a vertically distributed phased transmitting array (PTA) and a horizontally aligned digital beamforming (DBF) receiving array, thereby enabling 2-D beam scanning with a reduced channel number. In contrast to time-division multiplexing (TDM) multiple-input multiple-output (MIMO) radars, in which only a single transmitter is active at a time in the TDM MIMO radar configuration due to the working principle of switching between different transmitting elements, here, all the phased array transmitters of the proposed radar work simultaneously. The demonstrated system realizes a higher transmitted power and signal-to-noise ratio, provided that the equivalent isotropically radiated power (EIRP) is not limited and the radiated power of each transmitter is equal in these two radar configurations. In addition, only 1-D DBF is required for the proposed hybrid radar because beamforming in another dimension is accomplished by the PTA, thus alleviating the baseband complexity and signal processing burden. As a proof-of-concept example for system validation, a hybrid radar prototype is fabricated and measured at around 24 GHz, without considering any specific EIRP limitations in this band, thus confirming the validity of the proposed concept.