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Radar-embedded communication (REC) is a new type of covert communication whereby communication signals are embedded in radar backscattered echoes. Conventional REC waveform design strategies generally involve transforming the radar sampling sequence into a square matrix that can be eigenvalue decomposed; however, it may lose some original characteristics of the radar signal during this process. In this paper, we propose two REC waveform design methods based on singular value decomposition (SVD), namely, SVD-NDP (non-dominant-processing waveform based on SVD) and SVD-SNDP (shaped-non-dominant-processing waveform based on SVD), and derive the similarities between the radar signal and the proposed waveforms. We then simulate the anti-detection performance, anti-interception performance, and communication reliability of the proposed waveforms and analyze the degrees of freedom and computational complexity of their design. The results show that, compared with conventional REC waveforms, the proposed waveforms could improve communication reliability without sacrificing low probability of intercept (LPI) performance and at least double the design degrees of freedom. SVD-NDP and SVD-SNDP algorithms can reduce computational complexity by more than 30% compared with conventional waveform design algorithms based on eigenvalue decomposition when the number of communication waveforms (symbols) is set to 4 (2 bits), and the proportion of the dominant subspace is set to 50%. [ABSTRACT FROM AUTHOR]

By using uniformly distributed phases for each bit, one can generate a waveform with an almost ideal aperiodic ambiguity function (thumbtack). Moreover,a set of such codes will potentially exhibit low cross-correlation, making them a family of codes suitable for multiple and diverse applications, ranging from 1) MIMO radars, 2) addressing the joint radar-communication problems, 3) mitigating the interference issues of coexistence of multiple radar systems operating in the same region, and 4) increasing the robustness of the system to jamming. This article presents the full analysis of the probability density function of the peak sidelobe level of such codes, as well as a full treatment of the ambiguity function to show mathematically what could be obtained by generating random phase codes. A simple and effective greedy algorithm for generating multiple codes with improved performance is devised increasing the practicality of this waveform family, and numerical results are provided to support the theoretical ones. [ABSTRACT FROM AUTHOR]

A photonics-based leakage cancellation and echo signal de-chirping approach for frequency-modulated continuous-wave radar systems is proposed based on a dual-drive Mach–Zehnder modulator (DD-MZM). The de-chirp reference signal and the leakage cancellation reference signal are combined and applied to the upper arm of the DD-MZM, while the received signal, including the leakage signal and echo signals, is applied to the lower arm of the DD-MZM. When the amplitudes and delays of the leakage cancellation reference signal and the leakage signal are precisely matched, the leakage signal is canceled in the optical domain. The de-chirped signal is obtained after the leakage-free optical signal is detected in a photodetector. An experiment is performed. The cancellation depth of the leakage signal after de-chirping is around 23 dB when the center frequency and bandwidth of the linearly frequency-modulated signal are 11.5 and 2 GHz, respectively. When the leakage cancellation is not employed, the leakage will seriously affect the imaging results and distance measurement accuracy. When the leakage cancellation is enabled, the imaging results of multiple targets can be clearly distinguished, the distance measurement error of a moving target is significantly reduced to less than 10 cm, and a dynamic range increase of 21 dB is achieved. [ABSTRACT FROM AUTHOR]

This article proposes an accurate clutter synthesis method for a dynamic radar under various environmental conditions. Previous clutter synthesis methods mostly rely on empirical clutter models fitting the measurement results obtained from the limited conditions of observations. Inherently, these models have difficulties synthesizing clutter for unexplored environments and operational conditions. The method presented in this article overcomes this limitation by creating and summing the clutter patch by patch. The clutters of individual patches are synthesized by the clutter model corresponding to each patch's specific conditions, such as the grazing angle, ground texture, and wind velocity. It enables one to synthesize clutter signals under various observation scenarios accurately. For example, this work demonstrates the synthesis of clutter signals from heterogeneous textures for an aircraft radar on a mission. Also, the clutter signals for a monopulse radar are synthesized and included in the radar simulation to evaluate the performance of a detection algorithm. Potentially, this method can be utilized to design a robust radar algorithm against the clutter environment of interests. For a given condition of measurement, it is shown that the synthesized clutters agree well with the actual measured data in their statistical characteristics. The program is freely available through the open code. [ABSTRACT FROM AUTHOR]

We propose a deep learning-based approach to localizing a small moving object with a single millimeter-wave frequency-modulated continuous-wave (FMCW) radar. The main challenge that foils conventional localization techniques, such as 3-D fast Fourier transform (3-D-FFT), Pisarenko method, multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance technique (ESPRIT), Capon’s method, and Burg’s method, is the low signal-to-noise ratio of the reflected signal from millimeter-sized objects. Our key idea is to combine useful but noisy features from classical transforms [e.g., fast Fourier transform (FFT)] with neural networks that can refine and interpret those features into range and angle estimates by training on a large dataset of examples. Importantly, our networks were designed to be translation-equivariant, which enables accurate predictions of unseen object locations and improves the range and azimuth root mean square error (RMSE) scores by 34%–46% and 41%–60%, respectively, over state-of-the-art approaches. This pilot study establishes a new baseline for small-object tracking using FMCW and can enable tracking of small animals, such as ants inside the colony for behavior studies. Our first FMCW-small-object dataset and the source code are publicly available on https://github.com/shikuzen/RA-CNN. [ABSTRACT FROM AUTHOR]

Target masking is a pervasive problem in radar signal processing: the range sidelobes of the waveform’s matched filter response may cause a strong target to prevent the detection of nearby weaker targets. Common solutions to sidelobe level reduction for frequency-modulated waveforms result in a signal-to-noise ratio (SNR) loss. In this correspondence, we propose a novel method using pulse diversity to reduce the range sidelobes while avoiding an SNR loss. The proposed approach is based on shaping the power spectrum of the summation of a train of constant-amplitude linearly frequency-modulated pulses to resemble a Gaussian function. We present a numerical example demonstrating a drastic sidelobe level reduction. Importantly, our approach avoids any processing SNR loss. [ABSTRACT FROM AUTHOR]

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 article 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 3-D 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 U.S. Department of Homeland Security (DHS). The reconstructed body contours may be used to estimate the electric permittivity of the concealed person-worn objects. [ABSTRACT FROM AUTHOR]

In this work, we derive the exact response of an interferometric correlation radar to an arbitrary scene containing $N$ independent point scatterers. The response of an interferometric radar to multiple targets can be complicated by the nonlinear processing, which generates intermodulation responses in addition to the desired fundamental frequency responses. An accurate signal model is critical for predicting and mitigating these intermodulation products and allows for model-based parameter estimation methods to be used to estimate target angular velocity. We derive a general response to an arbitrary number of independent targets and validate both the exact model and a simpler approximate model via simulation and experimental measurement with a 38-GHz continuous-wave interferometric radar. The experiment contains two targets traveling perpendicular to the radar’s line-of-sight on linear stages at speeds of 0.36 and 0.5 m/s. We use an error metric to describe the similarity between the measured or simulated time–frequency response and the time–frequency response of a signal generated using the frequency estimates of either the exact or approximate models and show that in regimes where the small-angle approximation holds, both models provide an accurate representation of the measured or simulated interferometric response, demonstrating the feasibility of these models. [ABSTRACT FROM AUTHOR]

Aiming at realizing on-chip real-time human activity recognition based on the miniaturized and integrated radar with limited computational resources, hyperdimensional computing (HDC) is a promising method due to its unique advantages including high energy efficiency, excellent robustness, lightweight model, and superfast learning process. This article reports on the first use of HDC in the radar system to recognize human activities. First, we design an image mask method to minimize the impact of interference in original radar micro-Doppler spectrograms and apply a fast envelope extraction method to fully reflect features of different human activities. Subsequently, we select the most applicable representation methods for HDC to encode and recognize these features. To better accelerate the convergence speed, enable subtle adjustments of the HDC model, and improve the recognition accuracy, we propose a new adaptive retraining method, Mask-Retraining. Finally, the accuracy of recognizing six human activities using HDC with Mask-Retraining reaches 92.93%. Experimental results and comparisons between HDC and ten recognition algorithms demonstrate the validity of our methods. In most cases, HDC outperforms other algorithms being compared both in recognition accuracy and computational cost. Especially, HDC becomes the prime and irreplaceable candidate when only small datasets or finite data points are available. [ABSTRACT FROM AUTHOR]

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 Cramér-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. [ABSTRACT FROM AUTHOR]

In this article, the wideband ambiguity function (WBAF) of monostatic radar and that of bistatic radar, both operating with ultrawideband (UWB)-throb signals, are presented and used to calculate Cramér–Rao lower bounds (CRLBs) on the estimation accuracy of time delay and Doppler stretch in the case of monostatic radar and CRLBs on the estimation accuracy of receiver-to-target range and relative-velocity of a moving point target in the case of bistatic radar. Performance analysis is carried out to investigate the effects of the parameters that characterize the UWB-throb signal, i.e., pulse scaling factor, initial pulse repetition frequency (PRF), throb rate, and signal duration, and the parameters defining the bistatic-radar geometry, i.e., receiver-to-target range, baseline distance, and elevation angle, on the estimation accuracy of the monostatic-CRLBs and on that of the bistatic-CRLBs. Computer plots demonstrate that the estimation accuracy of the monostatic-CRLBs and the bistatic-CRLBs is influenced by the aforementioned radar parameters. It is also shown that target relative velocity has no major effects on the estimation performance of monostatic-CRLBs and bistatic-CRLBs. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) real-time imaging is especially important for disaster emergencies and real-time monitoring applications with highly desired real-time requirements. Therefore, the continuous improvement of real-time imaging efficiency is an important development trend. At present, traditional real-time imaging algorithms based on constant pulse repetition frequency (PRF) have low accuracy when processing spaceborne SAR data with nonlinear trajectory. For this problem, the existing methods usually introduce some complex signal processing steps, such as scaling or interpolation processing, to improve the accuracy of the real-time imaging, but this will reduce its efficiency. Therefore, this article proposes a new real-time imaging algorithm based on variable PRF for nonlinear trajectory spaceborne SAR. By introducing the variable PRF, the proposed algorithm is equivalent to complete the complex signal processing steps in the radar signal transmission stage, which can greatly improve the efficiency of real-time imaging. Simulation experiments verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Radar emitter classification (REC) plays an increasingly significant role in the electronic reconnaissance system. Due to there are many unreliable factors in traditional feature-based methods, this article focuses on improving the accuracy and stability of REC based on multipaths of original radar signals. A novel aggregated multipath extreme gradient boosting (AMP-XGBoost) is therefore put forward. Multi-path, including multi-scale, multi-domain transformations and their concatenations, is developed to exploit more information of the original signal, which contributes to providing more distinguishable features. XGBoost is used to automatically extract the features contained in these paths and complete recognition. Finally, multiple paths are sifted and aggregated according to certain weights to obtain a higher accuracy. Experiments are carried out based on signals from 6 different intra-pulse modulation mode radars with the same radio frequency (RF) range. It is demonstrated that the proposed method has higher REC accuracy than the methods only based on a single scale or domain. In the meantime, experimental results under different low SNRs show that the proposed method has higher stability. Finally, experiments based on the measured aviation radar data proves the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Bistatic radar signal processing often assumes a constant velocity for target detection. The bistatic geometry alters, however, the effectively measurable velocity in relation to the target position and motion even if the target has a constant ground speed. Hence, the bistatic geometry induces instantaneous bistatic target acceleration. This is especially the case if a straight-line target trajectory cuts across several closely spaced velocity isolines. The letter tries to quantify this geometry-induced instantaneous bistatic acceleration, in general, for straight-line target trajectories. Thereby, position-dependent acceleration values are derived for a practical use case. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new elegant systematic framework relying on the Kullback-Leibler Information Criterion to approach the design of one-stage adaptive detection architectures for multiple hypothesis testing problems in radar. Specifically, at the design stage, we assume that one out of several alternative hypotheses may be in force and that only one null hypothesis exists. Then, starting from the case where all the parameters are known and proceeding towards the adaptivity with respect to the entire parameter set, we come up with decision schemes for multiple alternative hypotheses consisting of the sum between the compressed log-likelihood ratio based upon the available data and a penalty term accounting for the number of unknown parameters. Such a term arises from suitable approximations of the Kullback-Leibler Divergence between the true and a candidate probability density function. Interestingly, under specific constraints, the proposed decision schemes can share the constant false alarm rate property by virtue of the Invariance Principle. Finally, we also show that the new architectures can be viewed as the result of a suitable regularization of the log-likelihood. [ABSTRACT FROM AUTHOR]

This article presents a novel spoofing device capable of injecting false target information into a frequency-modulated continuous-wave (FMCW) radar. The spoofing device uses a radio frequency (RF) single-sideband (SSB) mixer to introduce a frequency shift to the incoming RF signal transmitted by the victim radar and retransmits the modulated RF signal. The modulated RF signal resembles a false target. Upon down-conversion on the receiver chain of the victim radar, the modulated RF signal creates an illusion of a real target in the radar signal processing system. The frequency shift can be adjusted to vary the range of the spoofed target. The theory of the spoofing mechanism was developed, and a 5.8 GHz prototype was built for experimental validation. Experimental results demonstrate the ability of the proposed spoofing device to inject a false target at any arbitrary range. A hybrid-chirp FMCW approach was proposed and verified as a countermeasure to distinguish a real target from a spoofed target to mitigate the RF-spoofing attack. [ABSTRACT FROM AUTHOR]

Radar imaging using the synthetic aperture radar (SAR) principle is a common method to obtain information about e.g., the surface of a target. However, most image formation algorithms for such systems assume (quasi-)static measurements. This may lead to errors in the processed images if the sensor is moving during the measurement process. This is especially the case for frequency-modulated continuous-wave (FMCW)-based sensors since the signal duration is longer than in a pulsed system and the achievable bandwidth is much larger and introduces additional challenges. Motion compensation in the context of radar imaging is usually related to the correction of deviations from an ideal trajectory. In contrast, this article presents a method to take the sensor movement during a single FMCW ramp into account and therefore addresses the effects caused by a continuous path during the transmit/receive process. Hence, faster movement can be achieved during the scanning of the synthetic aperture without being bound by stop-and-go approximations. In addition, it will be shown that the algorithm is suitable to reduce systematic errors due to aliasing caused by spatial sampling below the Nyquist rate. For this purpose, this article presents simulations and measurement results, obtained by an ultrawideband $D$ -band FMCW radar operating between 122 and 170 GHz. [ABSTRACT FROM AUTHOR]

A novel matrix-pencil (MP)-based interference mitigation approach for frequency-modulated continuous-wave (FMCW) radars is proposed in this article. The interference-contaminated segment of the beat signal is first cut out, and then, the signal samples in the cutout region are reconstructed by modeling the beat signal as a sum of complex exponentials and using the MP method to estimate their parameters. The efficiency of the proposed approach for the interference with different parameters (i.e., interference duration, signal-to-noise ratio (SNR), and different target scenarios) is investigated by means of numerical simulations. The proposed interference mitigation approach is intensively verified on experimental data. Comparisons of the proposed approach with the zeroing and other beat-frequency interpolation techniques are presented. The results indicate the broad applicability and superiority of the proposed approach, especially in low SNR and long interference duration situations. [ABSTRACT FROM AUTHOR]

This article presents a framework to generate 3-D point clouds by very-high-resolution single-pass and single-channel circular synthetic aperture radar (CSAR). The focus is both on the precise 3-D determination of very small, detached objects and on larger buildings in complex urban scenes. Inspired by optical flow methods, our approach evaluates the tracked aspect dependent backscatter energy flow of objects above the focusing reference plane. Besides the derivation of the exact projection geometry in CSAR, we give answers to the question of how accurate 3-D information can be extracted by this approach as a function of aspect interval and the object’s signal-to-clutter ratio (SCR). A further coherent adaption uses considerably larger subapertures while focusing the data on different reference heights. The computed 3-D information from multiple aspect views is then fused to a georeferenced 3-D point cloud. This results in the first demonstration of 3-D point cloud generation from CSAR data collected with a frequency-modulated continuous-wave (FMCW) radar at the W-band. The results were validated with light detection and ranging (LiDAR) data, and the height accuracy was evaluated in relation to the aspect integration interval and the number of aspect views. For small point targets with edge lengths of 3 cm, we could demonstrate that an aspect interval of only 8.5° leads to a height accuracy below 20 cm. Building roofs reach a height accuracy of several tens of centimeters. Besides the spatial information, we can reveal the angular scattering behavior of individual objects and determine over which aspect interval they are even visible. [ABSTRACT FROM AUTHOR]

Radar specific emitter identification (SEI) is the process of uniquely identifying an individual emitter from the same class of radars by their individual properties that arise from hardware imperfections. However, it is challenging or perhaps impossible to generate globally unique emitter identifiers by using SEI techniques alone, due to the increasing number of radars and the subtle differences in their signal properties. We therefore introduce a multitier radar emitter identification concept that includes radar function identification, model identification, and SEI. The combination of function identifiers, model identifiers, and specific emitter identifiers could generate globally unique radar emitter identifiers. In this article, we propose to use the radio frequency (RF) features extracted from time domain transient signals for radar model identification. The RF features include the duration, maximum derivative, skewness, kurtosis, mean, variance, fractal dimension, Shannon entropy, and polynomial coefficients of the normalized energy trajectory of a transient signal, as well as the area under the trajectory curve. We propose three RF fingerprints for radar model identification, each consisting of a predetermined subset of the features. The performance of the RF fingerprints was evaluated by using five classification algorithms with two radar datasets. Our results show attractive performance with respect to hetero-model radar identification. In particular, for the hetero-model radar dataset, the pair of the all-features (AF) fingerprint and gradient boosting algorithm achieved 91.9, 90.25, and 91.1% classification accuracy for three, four, and five emitters, respectively. On this basis, we conclude that the proposed AF fingerprint could be applied directly to radar model identification by training the gradient boosting algorithm using a dataset with many radars per model. [ABSTRACT FROM AUTHOR]

Passive radar signal processing is hindered by the need to sample a reference signal from the chosen illuminator, which is subject to sensor noise and interference from clutter returns. The reference signal may be demodulated and reconstructed to remove artifacts. However, without accounting for the transmitter's clock offset, the remodulated reference signal will be a poor match for the surveillance signal. This article evaluates clock offset estimation methods in terms of the radar ambiguity surface, thereby appraising them specifically for passive radar applications. Existing offset estimation methods are shown to meet variance bounds; however, these bounds worsen with increased clock offset. Ambiguity surfaces constructed from signals reconstructed with poor clock offsets exhibit power leakage. A new clock offset estimator is proposed, which refines coarse estimates made by an existing method. The new estimator is demonstrated to achieve accurate estimation of clock offset. Reference signals reconstructed with these estimates produce ambiguity surfaces with no significant power leakage. [ABSTRACT FROM AUTHOR]

This article presents a short-range cooperative radar system in the 77-GHz band for the estimation of kinematic parameters, such as position, velocity, and acceleration of a target. The proposed system uses two FMCW radar sensors that are operated incoherently and only depend on a common trigger for coarse synchronization. The resulting frequency offset and phase offset in the beat spectrum of the radar sensors are eliminated through calibration with a known active reference target. For easy detection of the reference target in the beat spectrum, a novel interchirp modulation method is proposed. The sampled data of a single receive channel of only one of the sensors of the system allow for a complete estimation of the kinematic target parameters. The theoretical analysis of incoherent FMCW radar systems is carried out, and the constraints and signal processing algorithms are derived from the results. The theory and operating principle of the proposed system are verified through measurements in an anechoic chamber. The accuracies for the position, velocity, and direction of movement (DoM) estimation are better than 10 mm, 0.03 m/s, and 1.4°, respectively. [ABSTRACT FROM AUTHOR]

This article considers three interrelated adversarial inference problems involving cognitive radars. We first discuss inverse tracking of the radar to estimate the adversary's estimate of us based on the radar's actions and calibrate the radar's sensing accuracy. Second, using revealed preference from microeconomics, we formulate a nonparametric test to identify if the cognitive radar is a constrained utility maximizer with signal processing constraints. We consider two radar functionalities, namely, beam allocation and waveform design, with respect to which the cognitive radar is assumed to maximize its utility and construct a set-valued estimator for the radar's utility function. Finally, we discuss how to engineer interference at the physical layer level to confuse the radar that forces it to change its transmit waveform. The levels of abstraction range from smart interference design based on Wiener filters (at the pulse/waveform level), inverse Kalman filters at the tracking level, and revealed preferences for identifying utility maximization at the systems level. [ABSTRACT FROM AUTHOR]

Phased array radar systems are indispensable in many applications requiring robust sensing of the environment. To achieve sensitive target detection and accurate direction-of-arrival (DOA) estimation, a large number of receiving antenna elements are needed. The high dimension of the element-level data inevitably leads to a large computational burden for digital signal processing. This problem can be overcome by transforming the element-level data into a lower dimensional beamspace. In this article, we present a novel parameter-controlled design method to construct this transformation. If the dimension reduction is not too drastic, it jointly achieves optimal detection and DOA estimation performance. Otherwise, it meets predefined performance criteria by exploiting an acceptable tradeoff between detection and DOA estimation performance. We propose a general design tool, which is not limited to a specific array configuration. It comprises a precalculated set of plots, providing the radar designer an overview of possible performance for a given scenario. We describe a straightforward method to construct the corresponding transformation. Numerical studies highlight the superiority of the proposed design method. [ABSTRACT FROM AUTHOR]

This article introduces a precise radar-based direction-of-arrival measurement system using a Viterbi decoder. Based on the frequency-modulated continuous-wave principle, the presented method employs both the frequency difference and the round-trip phase difference of the radio wave signal impinging at two widely spaced receiving antennas, thereby accomplishing highly accurate and absolute measurements of the direction-of-arrival. In order to resolve angular ambiguity, which is inherently caused by the $2 \pi $ ambiguity in phase measurement, the proposed Viterbi decoder determines the most likely sequence of direction-of-arrivals of the impinging radio wave signals. For proof of concept, experiments based on a 77 GHz radar unit equipped with one transmitting and two widely spaced receiving antennas were carried out. The measurement results show that angular ambiguity can be resolved by the presented Viterbi decoder since absolute, accurate, and repeatable direction-of-arrival estimates were obtained, even for receiver antenna spacings significantly larger than the signal wavelength. [ABSTRACT FROM AUTHOR]

We numerically perform the classification of IQ-modulated radiofrequency signals using reservoir computing based on narrowband optoelectronic oscillators (OEOs) driven by a continuous-wave semiconductor laser. In general, the OEOs used for reservoir computing are wideband and are processing analog signals in the baseband. However, their hardware architecture is inherently inadequate to directly process radiotelecom or radar signals, which are modulated carriers. On the other hand, the high- Q OEOs that have been developed for ultra-low phase noise microwave generation have the adequate hardware architecture to process such multi-GHz modulated signals, but they have never been investigated as possible reservoir computing platforms. In this article, we show that these high-Q OEOs are indeed suitable for reservoir computing with modulated carriers. Our dataset (DeepSig RadioML) is composed with 11 analog and digital formats of IQ-modulated radio signals (BPSK, QAM64, WBFM, etc.), and the task of the high-Q OEO reservoir computer is to recognize and classify them. Our numerical simulations show that with a simpler architecture, a smaller training set, fewer nodes and fewer layers than their neural network counterparts, high-Q OEO-based reservoir computers perform this classification task with an accuracy better than the state-of-the-art, for a wide range of parameters. We also investigate in detail the effects of reducing the size of the training sets on the classification performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Multichannel calibration is essential for detecting moving targets and for estimating their positions and velocities accurately. This article presents a fast and efficient calibration algorithm for the along-track multichannel systems, in particular for space-time adaptive processing (STAP) techniques. The proposed algorithm corrects the phase and magnitude offsets of the receive channels and also takes into account the Doppler centroid variation (e.g., caused by atmospheric turbulences) along the slant range and the azimuth time. The knowledge of the Doppler centroid variation is especially important for an accurate clutter covariance matrix estimation, which is required by STAP for efficient clutter suppression. Important calibration parameters and offsets are estimated directly from the range-compressed training data. The proposed algorithm is evaluated based on real multichannel X-band radar data acquired with DLR’s airborne system F-SAR and compared with the state-of-the-art digital channel balancing technique. The experimental results show the potential of the proposed calibration algorithm toward real-time applications. [ABSTRACT FROM AUTHOR]

Fast Fourier transform (FFT) is one of the fundamental signal processing algorithms widely used in radar applications. The Radon–Fourier transform (RFT) can be seen as an FFT generalization that can overcome some of its limitations. This work derives three spectral RFT (SRFT) based approaches to address major challenges of the multiple-input multiple-output automotive radars. First, two SRFT-based approaches are derived to increase maximal target detection range by mitigation of target migration in range and direction of arrival, jointly, and by multidwell integration processing, which increases the radar coherent integration time without compromising its detection update rate. Next, SRFT-based approach is proposed to address the cluster-to-track association problem that arises in multiple distributed target tracking scenarios that characterize automotive radar operation in dense urban environments. [ABSTRACT FROM AUTHOR]

FILtering by COHerence (FILCOH) is a novel technique for mitigating ground clutter echoes of weather radar, particularly those transmitting in multiple pulse repetition time (PRT) schemes. FILCOH takes advantage of the differences in correlation time for separating rain and ground echoes. In short, the longest time-lag coefficients of the autocorrelation function are used to estimate the ground clutter contribution which is then subtracted from the shortest lag coefficients before the extraction of the meteorological parameters using the conventional estimators. The initial results are obtained for uniform PRT data provided by the Degreane Horizon wind profiler radar. The analysis illustrates the behavior of FILCOH filtering and its high performance in such cases. The simulation results are then presented for 2- and 3-PRT pulse schemes using standard PRT ratios. For the 2-PRT scheme, FILCOH filters out the ground clutter echoes up to a clutter to signal ratio (CSR) of 65 dB for the received power and up to 45 and 60 dB for the radial velocity and spectrum width, respectively. For the 3-PRT scheme, ground clutter filtering is effective up to $CSR = 45$ dB for the received power and up to $CSR = 35$ dB for radial velocity and spectrum width. These results are confirmed by 3-PRT real data issued from the French X- and C-band radars. The observed performances are, however, a little less than those of the simulation since we observed a maximum 40-dB attenuation of the ground clutter echoes of the studied rain events. [ABSTRACT FROM AUTHOR]

Many modern phased-array radars (PARs) are multichannel systems that include multiple receivers for data acquisition. Each channel provides a signal from a group of Transmit/Receive modules comprising a section of the antenna. Channels typically consist of a full receive path, often with an independent local oscillator (LO) clock source. Such arrangement provides for beamforming flexibility on receive which can be applied in a digital domain. Consequently, the channel-to-channel phase and magnitude alignment is critical to maximizing the performance of the digital beamforming process and the accuracy of resulting detections and measurements. Herein, a novel method to improve such alignment using weather returns and achieve the improvement in the polarimetric variable estimates is described. [ABSTRACT FROM AUTHOR]

Multistatic radar is a promising option for the low-cost collection of multiple-Doppler weather observations. However, due to the use of low-directivity antennas at the receivers in these systems, they typically suffer from extremely high two-way sidelobe levels compared to monostatic radars. Doppler velocity estimation biases induced by sidelobe contamination have proved to be a significant obstacle to more widespread adoption of this technology. It has been noted in the existing literature that the technique of sidelobe whitening, first developed for use in monostatic systems, has the potential to mitigate this issue. However, the existing sidelobe whitening algorithm is not suitable for use in this application, as it is only capable of achieving whitening in the two-way antenna pattern, whereas an algorithm to be used in the multistatic case must be able to achieve this result in the transmit pattern alone. This article proposes an alternate sidelobe whitening technique based on the method of alternating projections which allows for effective whitening in the one-way antenna pattern. A multistatic weather radar time-series simulator is used in conjunction with numerical weather prediction data to demonstrate the effectiveness of this method in mitigating multiple-Doppler measurement biases. [ABSTRACT FROM AUTHOR]

In order to improve the resolution of imaging radars, electrically large arrays and a high absolute modulation bandwidth are needed. For radar systems with simultaneously high range resolution and very large aperture, the difference in path length at the receiving antennas is a multiple of the range resolution of the radar, in particular for off-boresight angles of the incident wave. Therefore, the radar response of a target measured at the different receiving antennas is distributed over a large number of range cells. This behavior depends on the unknown incident angle of the wave and is, thus, denoted as range-angle coupling. Furthermore, the far-field (FF) condition is no longer fulfilled in short-range applications. Applying conventional signal processing and radar calibration techniques leads to a significant reduction of the resolution capabilities of the array. In this article, the key aspects of radar imaging are discussed when radars with both large aperture size and high absolute bandwidth are employed in short-range applications. Based on an initial mathematical formulation of the physical effects, a correction method and an efficient signal processing chain are proposed, which compensate for errors that occur with conventional beamforming techniques. It is shown by measurements that with an appropriate error correction an improvement of the angular resolution up to a factor of 2.5 is achieved, resulting in an angular resolution below 0.4° with an overall aperture size of nearly 200 $\lambda _{0}$. [ABSTRACT FROM AUTHOR]

High-resolution delay-Doppler estimation is an important requirement for automotive radar systems, especially in multi-target scenarios that require better target separation performance. Orthogonal frequency-division multiplexing (OFDM) is a promising candidate waveform for future intelligent transport networks, since it enables the integration of both radar and communication functionalities. Exploring the dual functionality enabled by OFDM, this paper presents a new cooperative method for high-resolution delay-Doppler estimation. The proposed subspace-based method exploits the combination of both the radar and received communication signals to estimate target parameters. The procedure achieves high-resolution delay-Doppler estimation for both uncorrelated, partially correlated and coherent signals, and enables a significant reduction in the required bandwidth when compared to previous approaches which do not exploit the knowledge of the communication signals. Laboratory measurements at 24 GHz and simulation results demonstrate the efficacy of the proposed method for the estimation of multiple targets. [ABSTRACT FROM AUTHOR]

The trimodal discrete (3MD) radar clutter model is a simple and accurate model for the interference distribution, which facilitates the computation of the probability of false alarm as a function of the detection threshold. Four estimation methods for the 3MD parameters are presented: two method-of-moments estimators—one based on an analytic solution to the moment equations for integer order from zero to five and the other using a nonlinear least-squares (LS) fit to 50 sample moments of fractional order; a nonlinear LS fit to the complementary cumulative distribution; and the maximum likelihood estimator (MLE). The methods were tested on both measured radar sea clutter data and simulated data and compared with the Cramér–Rao lower bound. The accuracy of all four methods is satisfactory, with the MLE the most accurate, but also the most computationally intensive. The analytic solution to the moment equations is the fastest method, two orders of magnitude faster than the MLE, but it is intractable for more than three modes. The other methods can all be used to estimate parameters for more than three modes if required. [ABSTRACT FROM AUTHOR]

The mesosphere–stratosphere–troposphere (MST) radar is a very high frequency (VHF) pulsed coherent Doppler radar used for ground-based remote sensing. In an MST radar, the backscattered pulses due to fluctuations in atmospheric refractive index are observed at the receiver. Echoes from higher altitudes are typically weaker, resulting in a low signal-to-noise ratio (SNR). They could also be contaminated with clutter and interference, making conventional spectral estimation techniques in the Fourier domain unreliable. In this article, we propose an algorithm to estimate the atmospheric wind profile using a dynamic programming approach employing the Viterbi data association (VDA) algorithm. Progressively for each range bin, the proposed algorithm chooses a set of probable wind speeds as nodes in a trellis. A branch metric incorporating the kinematic parameters inclusive of the differential wind shear is then constructed for state transitions within the trellis, from nodes in one range bin to the subsequent ones. A dynamic programming approach then chooses the optimal wind profile across altitudes. The proposed algorithm is experimented on simulated as well as real MST radar data sets to verify its robustness. Our experiments show that the suggested method has improved performance when compared to previously established techniques. [ABSTRACT FROM AUTHOR]

Multiple-input-multiple-output (MIMO)-radar systems employing ultrawideband (UWB) signals promise enhanced imaging performance. In this article, the generalized ambiguity function (GAF) for an MIMO-radar system is derived by using a set of quasi-orthogonal step-frequency UWB-throb signals. The effect of the step-frequency increment on the orthogonality property of the step-frequency UWB-throb signals is first analyzed. The properties of the UWB-GAF are also described. Computer simulation plots are generated for the delay-Doppler resolution function, delay-angle resolution function, Doppler-angle resolution function, delay profile, Doppler profile, and power beampattern for various design parameters of the MIMO-radar system and the employed UWB-throb signals. For the purpose of comparison, the same resolution functions and profiles are also generated for UWB linear-frequency-modulated (LFM) chirp signals. Performance analysis is conducted to demonstrate the resolution and the clutter-suppression capabilities of the GAF of an MIMO-radar system using UWB-throb signals in comparison with that of an MIMO-radar system using UWB-LFM-chirp signals. In principle, the UWB-GAF of an MIMO-radar system offers many degrees of freedom and allows for tradeoffs between the various signal-and-system design parameters. Such tradeoffs are of importance in practice for reducing the system complexity, enhancing the performance, and achieving the cost-effectiveness. [ABSTRACT FROM AUTHOR]

With the advancement of autonomous driving, the number of automotive radar sensors grows dramatically. As a result, interference becomes a major issue that has a vital importance for the future of automotive radar. Parallel to this development, radar concepts based on the digital signal generation and processing such as orthogonal frequency-division multiplexing (OFDM) have been studied over the past few years. The realization of the radar functionality based on digital waveforms provides a large degree of flexibility as well as adaptability. Among others, this can be used for increasing the interference robustness of digital radar systems compared to the traditional automotive radar. In this article, we propose a novel approach to mitigating automotive radar interference based on cognitive interference avoidance, which we demonstrate for the digital OFDM radar. We introduce the main building blocks of such a cognitive radar and focus specifically on waveform adaptation. The proposed waveform adaptation methods enable dynamical adjustment to interference, including adaptation during a single measurement cycle. The feasibility and performance of the proposed concepts are studied in simulations and validated using measurements with an OFDM radar prototype. [ABSTRACT FROM AUTHOR]

Radar micro-Doppler (m-D) signature, which reflects the micromotion dynamic and structural characteristics of radar target with micromotion, has received increasing attention. Most of the existing m-D signature-extraction methods operate in the time domain or the time-frequency domain. Different from these methods, in this paper, an m-D period estimation approach that operates in the ambiguity domain is proposed. Although the ambiguity function (AF) has been widely used in the field of radar signal processing, its application for m-D signal is introduced for the first time. It is proved that the AF of m-D signal exhibits periodicity along the lag axis and has the best concentration when the lag equals to multiples of the m-D period. Based on this, three AF concentration statistics are employed to capture the periodicity and to provide the m-D estimate. The most important property of the AF concentration statistics is that they are (or approximately) invariant to polynomial translations with terms not larger than second order even if the signal is Doppler ambiguous. Numeric simulation and real radar experiments are used to validate the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

In this article, we investigated the arrival times of plane waves incident on a circular dipole array in a borehole (CAB). Our theoretical and experimental findings suggest that the elevation angle of the received wave strongly affects plane wave arrival times in a borehole installation. If a plane wave is incident at the critical elevation angle (CEA), there is little arrival time difference among the signals at the CAB. On the other hand, when the plane wave is incident at an elevation angle steeper than the CEA, the wave arrival order at the CAB becomes opposite to the normal incidence case. In order to clarify the reason why these phenomena occur, we decompose a transmission matrix, which represents the effect of the borehole, into three components. We found that the azimuthal component of the electric field contributes to the observed phenomena significantly, and two of the three decomposed components cancel each other out at the CEA. For verification of the phenomena in a cross-hole borehole radar measurement, we performed a numerical simulation and carried out experimental verification at a field test site. Both data sets confirmed that the arrival time differences among the signals at the CAB were minimized at the elevation angle predicted to be the CEA. In addition, we found the reverse of the incident wave’s arrival order at the CAB. [ABSTRACT FROM AUTHOR]

A multistatic synthetic aperture radar (SAR) system offers the potential of exceeding the capabilities of conventional SARs in various ways, one of which is to realize high-resolution imaging over wide swaths when operated under the Nyquist frequency. Due to the decrease of the operational pulse repetition frequency, the Doppler spectrum of the received data of the single channels appears strongly aliased and needs to be resolved via azimuth reconstruction. Our aim in this article is to suggest an accurate reconstruction strategy that is applicable to multistatic SAR systems also with large along-track baselines and very high resolution on curved orbits. The algorithm, which can be regarded as a generalized reconstruction in the range-Doppler domain, operates in two steps and is capable of correcting most of the polychromatic deviations over large areas, achieving accurate reconstruction in constellations with kilometric baselines, resolutions of about $15\lambda $ over swaths of hundreds of kilometers. The validity of our approach has been tested using point targets in two- and six-receiver multistatic configurations. [ABSTRACT FROM AUTHOR]

Covariance matrix estimation is a crucial task in adaptive signal processing applied to several surveillance systems, including radar and sonar. In this paper we propose a dynamic learning strategy to track both the covariance matrix of data and its structure (class). We assume that, given the class, the posterior distribution of the covariance is described through a mixture of inverse Wishart distributions, while the class evolves according to a Markov chain. Hence, we devise a novel and general filtering strategy, called multi-class inverse Wishart mixture filter, able to capitalize on previous observations so as to accurately track and estimate the covariance. Some case studies are provided to highlight the effectiveness of the proposed technique, which is shown to outperform alternative methods in terms of both covariance estimation accuracy and probability of correct model selection. Specifically, the proposed filter is compared with class-clairvoyant covariance estimators, e.g., the maximum likelihood and the knowledge-based recursive least square filter, and with the model order selection method based on the Bayesian information criterion. [ABSTRACT FROM AUTHOR]

A new architecture of self-injection-locked (SIL) quadrature radar sensor based on a metamaterial (MTM) leaky-wave antenna (LWA) to detect multi-target vital sign and location simultaneously is proposed for the first time. The angular information of targets is obtained by a 1-D MTM LWA with −50° to +30° beam-steering angle when the frequency varies from 1.85 to 2.85 GHz. Furthermore, the distance from the radar sensor to the targets is obtained by imposing the frequency-shift keying (FSK) modulation on the radar waveforms. Meanwhile, the vital sign signals of multiple targets can be extracted from the Doppler frequency spectrum. For the proof of concept, two frequencies, 2.24 and 2.45 GHz, are chosen to detect two targets at different locations, which correspond to the scanning angle at −10° and −40°, respectively. A first-order microwave differentiator followed by a quadrature coupler is employed to convert the radio frequency (RF) signals from the output of the MTM LWA to in-phase (I) and quadrature (Q) RF signals with frequency-dependent amplitudes, thereby eliminating the null point issues for radar detection. Experimental results show that the proposed SIL MTM radar sensor can detect the target location accurately, while the obtained vital sign results agree well with the ground truth. [ABSTRACT FROM AUTHOR]

The estimation of mean matrix for a set of Hermitian positive definite (HPD) matrices can be considered based on a geometric framework. This kind of estimator is closely related to geometric measures, which can be established to measure the dissimilarity between two matrices on the matrix manifold. In this paper, the matrix spectral norm is exploited to derive a new geometric measure on the matrix manifold consisting of all HPD matrices. Correspondingly, the mean matrix associated with the new geometric measure on the matrix manifold is considered via two effective solution techniques. More specifically, the first method transforms the mean matrix estimation into a semidefinite programming problem solved by the interior point method; the second method exploits joint approximate diagonalization to diagonalize the given covariance matrices and then recasts the mean matrix estimation into an optimization problem with linear inequality constraints. Furthermore, two matrix detectors employing the proposed mean matrix estimators are designed for target detection using pulse Doppler radar with a small bunch of pulses. At the analysis stage, we show that the proposed matrix detector can guarantee the bounded constant false alarm rate property in terms of the clutter covariance matrix. Finally, numerical experiments based on simulated clutter data and real sea clutter data are performed to verify the effectiveness and robustness of the proposed matrix detectors. [ABSTRACT FROM AUTHOR]

Digital modulations such as orthogonal frequency-division multiplexing (OFDM) have received increasing interest for radar. However, the required fast analog-to-digital (AD) and digital-to-analog (DA) converters providing the necessary bandwidth have not been feasible for automotive applications so far. In this paper, the first highly integrated, fully coherent OFDM radar capable of multiple-input multiple-output (MIMO) and stepped-carrier OFDM for automotive applications is presented. The stepped-carrier OFDM approach is adopted to reduce AD/DA requirements using an analog local oscillator (LO) frequency step generator. The capability to generate frequency steps with a duration of a few microseconds required for automotive applications is demonstrated. Calibration steps that are required with the new architecture are discussed and measurements are done to validate the functionality of the system. Also, possibilities for adaptive reconfiguration of the radar sensor in the presence of ambiguities are demonstrated. [ABSTRACT FROM AUTHOR]

Target parameter estimation in active sensing, and particularly radar signal processing, is a long-standing problem that has been studied extensively. In this paper, we propose a novel approach for target parameter estimation in cases where one-bit analog-to-digital-converters (ADCs), also known as signal comparators with time-varying thresholds, are employed to sample the received radar signal instead of high-resolution ADCs. The considered problem has potential applications in the design of inexpensive radar and sensing devices in civilian applications, and can likely pave the way for future radar systems employing low-resolution ADCs for faster sampling and high-resolution target determination. We formulate the target estimation as a multivariate weighted-least-squares optimization problem that can be solved in a cyclic manner. Numerical results are provided to exhibit the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose an ergodic interference alignment (IA) method for interference elimination in spectrum sharing multiple-input multiple-output (MIMO) radar and multi-user MIMO communication systems. In existing ergodic IA methods, the channel state information (CSI) is generally assumed to be constant, but time-varying channels should be considered in practice. Differently, our proposed ergodic IA allows to time-varying and/or frequency selective channels for spectrum sharing radar-communication systems. The essence is equivalent to design a simple transmit beamforming, which helps to find a pair of desired complementary channels easily without long-time system delay, therefore, the interferences can be effectively cancelled. The proposed method is evaluated by generalized likelihood ratio test (GLRT) and signal to interference plus noise ratio (SINR) for the radar functionality, while the communication functionality is examined by bit error rate (BER) performance. Both theoretical analysis and simulation results validate that the effectiveness of the ergodic IA method for spectrum sharing radar-communication systems. [ABSTRACT FROM AUTHOR]

The emergence of CubeSats has opened up the possibilities of advanced space missions with lower costs and faster development times. A major factor that limits the functionality of CubeSats is the absence of high-gain antennas (HGAs) that can sustain a high data-rate link for communications or provide the required spatial resolution for remote sensing. In this work, we discuss the development of one of the largest apertures at Ka-band for CubeSats—a 1 m mesh deployable offset reflector antenna, with a stowed volume of 3U ($10\times 10\times 30 \,\,\text {cm}^{3}$)—to enable precipitation radars that can achieve the required spatial resolution while meeting the stringent mechanical constraints posed by the small CubeSat volume. We detail the critical aspects of this antenna design, including RF characterization, antenna fabrication, and measurement. We also describe a novel deployment mechanism that facilitates the packaging of such a large aperture in a small volume. The antenna demonstrated a measured efficiency of 60%, with a half-power beamwidth of 0.60° at 35.75 GHz. The cost reduction afforded by CubeSats makes launching a constellation of such radar based CubeSats practical, allowing higher temporal sampling rates, which is essential for the observation of weather processes with a short-time evolution. [ABSTRACT FROM AUTHOR]

The Cassini RADAR altimeter has provided broad-scale surface topography data for Saturn’s largest moon Titan. Herein, we adapt the delay/Doppler algorithm to take into account Cassini geometries and antenna mispointing usually occurring during hyperbolic Titan flybys. The proposed algorithm allows up to tenfold improvement in the along-track resolution. Preliminary results are provided that show how the improved topography presented herein can advance our understanding of Titan’s surface characteristics. [ABSTRACT FROM AUTHOR]

This paper presents a strategy for urban site monitoring by very high-resolution circular synthetic aperture radar (CSAR) imaging of multiple aspects. We analytically derive the limits of coherent azimuth processing for nonplanar objects in CSAR if no digital surface model (DSM) is available. The result indicates the level of maximum achievable resolution of these objects in this geometry. The difficulty of constantly illuminating a specific scene in full aspect mode (360°) for such small wavelengths is solved by a hardware- and software-side integration of the radar in a mechanical tracking mode. This results in the first demonstration of full aspect airborne subaperture CSAR images collected with an active frequency-modulated continuous wave (FMCW) radar at W-band. We describe the geometry and the implementation of the real-time beam-steering mode and evaluate resulting effects in the CSAR processing chain. The physical properties in W-band allow the use of extremely short subapertures in length while generating high azimuthal bandwidths. We use this feature to generate full aspect image stacks for CSAR video monitoring in very high frame rates. This technique offers the capability of detecting and observing moving objects in single channel data by shadow tracking. Due to the relatively strong echo of roads, the shadows of moving cars are rich in contrast. The image stack is further evaluated to present wide angular anisotropic properties of targets and first results on multiple aspect image fusion. Both topics show huge potential for further investigations in terms of image analysis and scene classification. [ABSTRACT FROM AUTHOR]