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A significant challenge for dual-beam, codesigned systems, such as for simultaneous radar and communications transmission, has been identified as the spurious beams produced by spatial intermodulation from nonlinearities in the transmitter power amplifier. We demonstrate that tuning the load impedance of the power amplifiers can reverse the elementwise power amplifier nonlinear distortion, reducing the magnitude of the unwanted beam transmissions and restoring the integrity of the dual-beam antenna pattern for application to an active electronically scanned array. A comparison to a recently documented predistortion approach for correcting spurious transmissions is provided, and it is shown that impedance tuning can address both element output power and linearity, whereas predistortion addresses only the linearity issue and causes the amplifier gain in many of the array elements to suffer. For multiple dual-beam angle scenarios, simulation results show that impedance tuning is effective at maximizing beam power in both beam directions, maximizing current gain in the element power amplifiers, and minimizing the size of the unwanted spurious beams. [ABSTRACT FROM AUTHOR]

Multi-input multi-output (MIMO) radar with massive antennas is promising for high resolution applications. However, a big challenge of this system is that the hardware cost and power consumption will increase significantly, if high-resolution quantizers are adopted. In this article, we consider MIMO radar deployed with one-bit digital-to-analog converters, and investigate the problem of designing one-bit transmit sequence with good spatial and spectral properties. Specifically, the one-bit waveform design problem is formulated by minimizing the mean-square error between the desired and designed transmit beampatterns, subject to spectral constraints. The resulting problem, including a nonconvex quartic objective and a nonconvex discrete constraint, is NP-hard, and an alternating optimization (AltOpt) framework with the aid of “almost equivalent” criterion is thereby developed to handle it. Particularly, in the AltOpt framework, a low-complexity algorithm is developed based on the alternating direction method of multipliers approach. Numerical simulations are provided to show the advantages of the proposed method over the state-of-the-art techniques in terms of the spatial and spectral properties as well as computational complexity. [ABSTRACT FROM AUTHOR]

Traditionally, inverse synthetic aperture radar (ISAR) imaging and target tracking are two separate and independent processes. ISAR imaging is usually achieved after data collecting and tracking for a long time, resulting in the problems of low imaging efficiency, information loss, and probing resource allocation difficulty. In this article, we proposed an integration method of tracking and imaging with wideband radar by constructing the relation among signal, data, and image domains, which can realize accurate tracking and high data-rate imaging at the same time. In this integrated imaging and tracking method, a complex-valued reference high-resolution range profile (HRRP) is generated and predicted by exploiting the relation among range-Doppler image, echo in time domain, and target's motion state. Then, a Bayes estimation model is built to extract range phase error between the predicted HRRP and the real-measured HRRP, and the Kalman filter can update the motion state. In addition, in order to increase the efficiency, the decentralized Kalman filter is also introduced to realize the subaperture parallel processing and data fusion in this article. Finally, the sequential motion compensation is carried out to realize accurate motion compensation and sequential imaging. The simulated and real-measured data confirm the proposed algorithm's robust imaging and tracking performance. Experimental results show that the proposed method has excellent efficiency and can simultaneously realize sequential imaging and accurate tracking. [ABSTRACT FROM AUTHOR]

Assume that a modern radar system has a general antenna array configuration consisting of primary channels with high-gain beams and reference channels with low-gain beams. We consider the target detection problem for such array radar systems in Gaussian disturbance with unknown covariance matrix. An adaptive detector is proposed according to the criterion of Wald test. The statistical properties of the proposed detector are provided in the mismatched case, where the nominal target steering vector may not be aligned with the true target steering vector. An analytical expression for the probability of false alarm is derived, which reveals that the proposed detector has a constant false alarm rate property with respect to the disturbance covariance matrix. Moreover, a closed-form expression for the detection probability is obtained in the mismatched case. Simulation results demonstrate that the proposed detector exhibits strong robustness against the target steering vector mismatch. [ABSTRACT FROM AUTHOR]

With the increasing of on-orbit satellites, target dynamic monitoring becomes more and more important in space situation awareness applications. Currently, there are some exploratory methods monitoring attitude-stabilized targets using various high-resolution remote sensing technologies. However, it is still a big challenge to achieve dynamic estimation of spin satellite with single sensor. Inspired by the existing matching based works, this article presents a dynamic estimation method of spin satellite using single-station inverse synthetic aperture radar (ISAR) images. When the angle accumulation is set to be a constant in each ISAR imaging period, the projection change of target structures in the long-term observation sequence is described with the hidden Markov model to build the mathematic expression of target on-orbit state. In this condition, target sequential attitude can be solved with Viterbi decoding algorithm even the azimuth scaling of each image is difficulty due to target spin motion. Then, combined with the radar observation geometry, target sequential attitude parameters are substituted into a spin parameter optimization, which is solved by particle swarm optimization algorithm. In the end, target instantaneous attitude vectors and spin speed are used to express its dynamic during the observation period. Simulation experiments of a typical spin spacecraft, Tiangong-I, confirm the feasibility of the proposed algorithm, and its superiority is also investigated by comparison with the existing work. [ABSTRACT FROM AUTHOR]

One of the few electronic attack techniques that can deceive radars in angle is cross-eye jamming, which mimics the naturally-occurring phenomenon glint. The extreme tolerance requirements of cross-eye jamming mean that a retrodirective implementation is required, but published measurements of cross-eye jamming either ignore the retrodirective implementation or only simulate it. The implementation of a retrodirective cross-eye jammer and its testing against a monopulse radar are described. A procedure for calibrating the jammer is outlined and is shown to be effective by achieving large angular errors. The measured results agree well with the extended analysis of cross-eye jamming and confirm that the implemented jammer is retrodirective. Specifically, the ability of a cross-eye jammer to generate an indicated angle that never becomes zero, thereby potentially breaking a tracking lock, is confirmed. [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]

In this article, we develop a new waveform codesign approach for radar–communications spectral coexistence using a decision-theoretic framework called partially observable Markov decision process (POMDP). The POMDP framework’s natural look-ahead feature allows us to trade off short-term for long-term performance, which is necessary in waveform codesign problems with competing objectives and dynamic user needs. As POMDPs are computationally intractable, we extend two approximation methods called nominal belief-state optimization and random-sampling multipath hypothesis propagation to make the codesign approaches tractable. [ABSTRACT FROM AUTHOR]

Target localization, especially the estimation of target altitude, is a challenging task in over-the-horizon radar (OTHR) because of the narrow signal bandwidth as well as the complexity and uncertainty involved in the ionosphere conditions. This task becomes further complicated when the height of the ionosphere layer varies over time. Therefore, it is important to jointly estimate the instantaneous height of the ionosphere and the target altitude as well as other motion parameters. In this article, we achieve these objectives by analyzing the Doppler frequencies of the target local-multipath signals and the clutter. We reveal that the change of the ionosphere height can either enhance or deteriorate the performance of target parameter estimation depending on its direction of motion relative to the target's motion profile. In addition, it is found that the received target and clutter Doppler signatures follow the chirp signal profile at the OTHR receiver. Based on these observations, we develop a general framework that achieves joint target and ionosphere parameter estimation and accounts for the velocity and accelerations of both target and ionosphere layer. Unlike existing time-frequency-based strategies for target localization and tracking in OTHR, where the Doppler signatures only directly determine target vertical velocity and the target altitude is estimated indirectly, the proposed model enables direct estimation of target altitude and ionosphere parameters. The parameter estimation problem in the proposed strategy is analytically derived and the effectiveness is verified using extensive simulation results. [ABSTRACT FROM AUTHOR]

Automatic waveform recognition of overlapping low probability of intercept (LPI) radar signals is an important and challenging task in electronic reconnaissance of the increasingly complicated spectrum environment. In this article, an overlapping LPI waveform recognition processing framework incorporating residual attention-aided U-net generative adversarial network (GAN) and multiinstance multilabel (MIML) classifier is proposed. This framework includes five cascade modules and can achieve satisfactory recognition performance by training with single type of signals only. First, the training signals are transformed into time–frequency images. Then, a residual attention U-net GAN (RAUGAN) with residual learning is employed to reconstruct signal images from noise-contaminated images and with the supervision of the high-quality ones. After that, an instance generation module with asymmetric convolutions generates instance representations, which are then fed into the subsequent residual attention MIML classifier (RAMIML). Finally, an adaptive threshold calibration module is implemented to obtain appropriate thresholds for the final decision. Besides, two loss functions are elaborately designed for the RAUGAN and RAMIML, respectively. Extensive experimental results validate the recognition performance of the proposed framework with a recognition accuracy of 80$\%$ at signal-to-noise ratio $>-18$ dB and show higher robustness on the power ratios and generative performance compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The advantages of sub-terahertz technology (sub-THz, 200–700 GHz) have already been advanced for imaging and recognition of a space object's state from a space-based sensor using inverse synthetic aperture radar (ISAR). The technology benefits from wide absolute signal bandwidths, giving high range resolution and from enhanced sensitivity to surface texture. An experimental validation of such a system in controlled laboratory conditions is presented. Possible image formation methods are proposed and evaluated experimentally. Images of real parts of satellites have been produced at sub-THz frequencies and compared to lower frequency images. Other results include sub-THz bistatic ISAR, and sub-THz cross-polarized images which highlight the scattering from rough surfaces while attenuating the very bright scatterers seen from conventional monostatic images. [ABSTRACT FROM AUTHOR]

Asan important means in low-altitude airspace surveillance, passive radar has been widely studied. With the increasing complexity of the low-altitude environment, the urgent need for radar's capability of automatic target recognition (ATR) emerges. In the field of ATR for passive radar, the method based on radar cross section (RCS) is an important direction for the simplicity of feature extraction. In this article, we exploit the compensated quasi-echo-power (CQEP), i.e., quasi-RCS, to realize ATR in passive radar. We propose an ATR method on the basis of the angular diversity. First, we divide the angular space, which consists of four components of the incident angle and scattering angle of electromagnetic wave, into multiple subspaces. Then, we build a subrecognizer in each angular subspace according to the CQEP distribution characteristics. Each receiving station of the multistatic passive radar recognizes targets in an angular subspace corresponding to its observation and outputs a preliminary decision. Based on the preliminary decisions of all receiving stations, a voting strategy is adopted to make the final decision on the unknown target class in the fusion center. Experimental results based on real-life target data show that the proposed method has a significant improvement on the average correct recognition rate with fusion of three receiving stations, which validates the proposed method. [ABSTRACT FROM AUTHOR]

This article considers the detection and tracking of weak targets in multiple pulse repetition frequency (multi-PRF) radars using the track-before-detect (TBD) technique. By exploring the measurement independence among different PRFs, we decompose the joint multi-PRF and multiframe optimization problem into several lower dimensional maximizations, each of which corresponds to an intra-PRF multiframe processing. An efficient two-stage TBD procedure with a parallel structure is proposed for the multi-PRF radars. Specifically, in the first stage, a constraint inequality is derived analytically and used to decouple the measurement ambiguities from the nonlinear conversion relationship between polar and Cartesian coordinates. The intra-PRF multiframe integration can then be carried out concurrently using the ambiguous measurements and the target-like measurement plot sequences with different ambiguities are extracted for different PRFs. In the second stage, a covariance combination fusion-based inter-PRF joint disambiguation and estimation algorithm is proposed to solve the ambiguities of the plot sequences and output high-accuracy target tracks. Simulation experiments show that the proposed algorithm can provide a good detection performance and higher tracking accuracy with much lower complexity. [ABSTRACT FROM AUTHOR]

Device-free localization (DFL) enables several new applications in various sectors including smart cities, intelligent transportation, and public safety. DFL relies on a network of sensor radars that transmit, receive, and process reflected signals propagating in a monitored environment. The accuracy of DFL degrades in cluttered environments, due to the presence of undesired objects that reflect the signal. Indeed, the multiple reflections of the signal overlap at the receiver and make the inference of targets' positions challenging. This article presents a theoretical foundation of DFL in cluttered environments by deriving the fundamental limits on DFL accuracy. In particular, we propose a system model that takes into account multiple reflections, nonline-of-sight conditions, and the presence of multiple targets. Building on such a model, we derive the Cramér-Rao bound on the inference accuracy of targets' positions by applying equivalent Fisher information analysis. The proposed bound provides guidelines for the design and analysis of DFL systems operating in cluttered environments. Then, the article presents a case study compliant with the 5G New Radio numerology and channel modeling. Results show how the minimum achievable error is affected by multiple reflections and multiple targets and to which extent the employment of a signal with larger bandwidth and a network with a higher number of receivers can lower the achievable error toward submeter accuracy. [ABSTRACT FROM AUTHOR]

In this article, semidefinite programming (SDP) solutions are proposed for the elliptic localization problem in asynchronous radar networks, where the transmitters are subject to clock offsets. Relaxing the nonconvex problem into different convex forms, we design tight SDP (TSDP) and global SDP (GSDP) solutions for this problem. The TSDP solution includes several SDP cones to solve the problem, and the correlation is not considered. Hence, the TSDP solution performs poorly in the presence of correlated noise. We further put forward the GSDP solution to improve the performance by introducing a new GSDP form, which includes only one SDP cone to handle the correlated noise. We also theoretically prove that the GSDP problem is tight enough so that its performance is able to sufficiently approach the Cramér–Rao lower bound (CRLB) accuracy. The simulated results show that the TSDP solution can provide the comparable performance with the GSDP in the absence of related noise. The performance of the GSDP solution can almost attain the CRLB accuracy using less receivers. [ABSTRACT FROM AUTHOR]

Deep learning with its rapid development and advancement has achieved unparalleled performance in many areas like computer vision as well as cognitive radio and signal recognition. However, the performance of most deep neural networks would suffer from degradation in the data mismatch scenario, e.g., the test dataset has a related but nonidentical distribution with the training dataset. Considering the noise corruption, a classifier's accuracy might drop sharply when it is tested on a dataset with much lower signal-to-noise ratio compared to its training dataset. To address this dilemma, in this work, we propose an efficient denoising and classification network (DNCNet) for radar signals. The DNCNet consists of denoising and classification subnetworks. First, a radar signal detection and synthetic mechanism is designed to generate pairwise clean data and noisy data for the DNCNet to train its denoising subnetwork. Then, a two-phase training procedure is proposed to train the denoising subnetwork in the first phase and strengthen the mapping between the denoising results and perceptual representation in the second. Experiments on synthetic and benchmark datasets validate the excellent performance of the proposed DNCNet against state-of-the-art methods in terms of both signal restoration quality and classification accuracy. [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]

This article deals with the multiple-input–multiple-output (MIMO) radar beampattern design in an effort to the coexistence with multiple communication systems. A waveform optimization model accounting for the minimization of the beampattern integrated sidelobe level (ISL) along with the mainlobe width, peak-to-average power ratio, and energy constraints, as well as multispectral requirements where the interference energy injected by the MIMO radar in each shared frequency band in a particular direction, is precisely controlled to ensure the desired quality of service at each communication system. Through an equivalent reformulation of the original nonconvex problem, a polynomial-time sequential convex approximation (SCA) procedure that involves the tackling of a series of constrained convex problems is proposed to monotonically decrease the ISL with the convergence guaranteed to a Karush–Kuhn–Tucker point. Herein, to speed up the convergence, a fast iterative algorithm based on the alternating-direction-method-of-multipliers framework is introduced to globally solve the convex problems during each iteration of the SCA procedure. Numerical results are provided to assess the proposed algorithm in terms of the computational complexity, the achieved beampattern, and spectral compatibility with some competitive counterparts available in the open literatures. [ABSTRACT FROM AUTHOR]

Electronic countermeasures (ECMs) against a radar are actions taken by an adversarial jammer to mitigate the effective utilization of the electromagnetic spectrum by the radar. On the other hand, electronic counter-countermeasures (ECCMs) are actions taken by the radar to mitigate the impact of ECMs so that the radar can continue to operate effectively. The main idea of this article is to show that ECCMs involving a radar and a jammer can be formulated as a principal–agent problem (PAP)—a problem widely studied in microeconomics. With the radar as the principal and the jammer as the agent, we design a PAP to optimize the radar's ECCM strategy in the presence of a jammer. The radar seeks to optimally trade off the signal-to-noise ratio of the target measurement with the measurement cost: cost for generating radiation power for the pulse to probe the target. We show that for a suitable choice of utility functions, the PAP is a convex optimization problem. Furthermore, we analyze the structure of the PAP and provide sufficient conditions under which the optimal solution is an increasing function of the jamming power observed by the radar; this enables computation of the radar's optimal ECCM within the class of increasing affine functions at a low computation cost. Finally, we illustrate the PAP formulation of the radar's ECCM problem via numerical simulations. We also use simulations to study a radar's ECCM problem, wherein the radar and the jammer have mismatched information. [ABSTRACT FROM AUTHOR]

This article addresses the problem of tracking weak and extended targets in the clutter for marine radar systems. In the proposal, multiple kernelized correlation filters (MKCFs) incorporate a low-threshold constant false alarm rate and segmentation model under the principle of multiframe track before detect (MF-TBD). By setting the similarity score of the correlation filter as the test statistic, the maximization of the integration over frames can be solved by the essentially exhaustive searching of the MKCF. Since the correlation filter measures the similarity of the intensity distributions in the extended templates, the proposed method can differentiate the weak target from the surrounding clutter and other targets at a close range. Compared to other amplitude-based MF-TBD methods, it performs better in detecting and tracking extended targets in the real-radar data with heavy clutter caused by floating sea ice and the simulations with Rayleigh and $K$-distributed sea clutters. [ABSTRACT FROM AUTHOR]

This article deals with the problem of adaptive radar detection in a missing-data context, where the complete observations (i.e., downstream information loss mechanisms) are characterized by homogeneous Gaussian disturbance with an unknown but possibly structured covariance matrix. The detection problem, formulated as a composite hypothesis test, is tackled by resorting to suboptimal design strategies, leveraging the generalized likelihood ratio criterion demanding appropriate maximum likelihood estimates (MLEs) of the unknowns under both hypotheses. Capitalizing on some possible a priori knowledge about the interference covariance matrix structure, the optimization problems involved in the MLE computation are handled by employing the expectation–maximization (EM) algorithm or its expectation–conditional maximization and multicycle EM variants. At the analysis stage, the performance of the devised architectures is assessed both via Monte Carlo simulations and on measured data for some covariance matrix structures of practical interest. [ABSTRACT FROM AUTHOR]

When a missile-borne radar system works in downward-looking surveillance mode, the broadened ground clutter signal in virtue of platform high-speed motion will be received by the radar receiver, which will cause difficulty in moving target detection and attacking. Unlike airborne and spaceborne platforms, a missile-borne platform exhibits some unique motion characteristics, such as diving, spinning, and coning, causing the clutter space–time distribution property significantly different from those of airborne and spaceborne radar platforms. In addition, the forward target striking requirements make the missile-borne clutter space–time spectrum further exhibit the severe range-dependent property. To deal with these issues, accurate motion modeling of a missile-borne radar platform is first carried out in this article, where the complex platform motions including forward-looking diving, spinning, and coning are considered. Then, the autocorrelation processing combined with iterative adaptive approach is applied to estimate the clutter angle-Doppler center frequencies, so as to effectively realize the clutter nonstationary compensation along spatial and temporal directions. Finally, a time-domain sliding window-based subspace projection method is proposed to achieve the robust clutter suppression. Both simulation and real-measured radar data processing results are presented to validate the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

According to the characteristics of signal processing in digital array radar (DAR), an adaptive dwell scheduling algorithm based on the virtual dynamic template is proposed, where a general pulse interleaving method with the overlap of receiving durations is involved. The virtual dynamic template is introduced to realize the general pulse interleaving among tracking tasks, where the tracking tasks with different numbers and repetition intervals of pulses may be interleaved under the time and energy constraints. The proposed dwell scheduling algorithm is a perfect combination of adaptive dwell scheduling technique and novel pulse interleaving based on the virtual dynamic template. It is developed based on the task model with multiple pulses and can realize real-time dwell scheduling, therefore, can be used in an actual radar system. Furthermore, to improve the efficiency of the proposed dwell scheduling algorithm, the efficient version of it is given. Simulation results demonstrate that the proposed algorithms can improve the scheduling performance effectively. Furthermore, the scheduling performance and efficiency can be balanced easily with the control of the utilization ratio threshold in the proposed efficient algorithm. [ABSTRACT FROM AUTHOR]

During the observation of micromotion targets in space, inverse synthetic aperture radar usually obtains the narrowband and wideband echoes simultaneously. In order to exploit their rich information in target electromagnetic scattering, shape, structure, and motion, this article proposes a recognition method of space micromotion targets based on decision fusion. The proposed method extracts physical features from the radar cross section and the joint time–frequency distribution from narrowband echoes, while extracts the data features from high-resolution range profiles and range-instantaneous-Doppler image by convolution neural network. Finally, particle swarm optimization is adopted to decision-level fusion so as to realize high-precision recognition. The recognition results of electromagnetic simulated data under various conditions have demonstrated the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

We derive a detector function for quantum two-mode squeezing (QTMS) radar and noise radar that is based on the use of a generalized likelihood ratio (GLR) test for distinguishing between the presence and absence of a target. In addition to an explicit expression for the GLR detector, we derive a detector function which approximates the GLR detector in the limit where the target is small, far away, or otherwise difficult to detect. When the number of integrated samples is large, we derive a theoretical expression for the receiver operating characteristic (ROC) curve of the radar when the GLR detector is used. When the number of samples is small, we use simulations to understand the ROC curve behavior of the detector. One interesting finding is that there exists a parameter regime in which a previously-studied detector outperforms the GLR detector, contrary to the intuition that LR-based tests are optimal or nearly so. This is because neither the Neyman–Pearson lemma, nor the Karlin–Rubin theorem which generalizes the lemma to composite hypotheses, hold in this particular problem. However, the GLR detector remains a good choice for target detection in certain regimes. [ABSTRACT FROM AUTHOR]

In an airborne maritime radar, inverse synthetic aperture radar (ISAR) is used to image and classify non-cooperative targets. In this article, we investigate a 3D-ISAR as an alternative representation that can potentially improve the final target classification by automatically extracting key features from the radar data, such as the length, width, and height of a target. The technique works by estimating the target scatterers’ positions using measurements from multiple ISAR images. This has previously been achieved using a single receive channel that exploits the temporal motion of the target or with a multichannel dual-baseline interferometric system that uses the phase differences in both the along- and across-track directions. In this article, we combine aspects of these two techniques using a single along-track baseline suitable for airborne maritime radar systems. [ABSTRACT FROM AUTHOR]

Phase-modulated continuous-wave (PMCW) radar is an emerging technology in various civilian applications. Due to the high bandwidth of the PMCW signal, it needs high-speed analog-to-digital converters (ADCs). High-resolution ADCs supporting this high bandwidth are expensive, have a big size on the chip, and consume high power, which are significant challenges for radar. A solution to deal with the aforementioned challenges is to use low-resolution, or in the most extreme case, one-bit ADCs. In this article, we aim at designing the transmit code and the receive filter for the PMCW radar in the presence of one-bit ADC at the receiver side. To this end, we introduce the mean integrated sidelobe level metric, called MISL. MISL generalizes the well-known ISL metric for nonideal ADC usage at the receiver side and takes the effect of noise and target backscattering coefficient into account. For mathematical tractability, we employ the maximum likelihood estimation of the target backscattering coefficient. This leads to casting an optimization problem that does not need knowledge about noise and target characteristics. We utilize the cyclic optimization procedure to optimize the transmit code and the receive filter. The filter design subproblem admits a closed-form solution, whereas we obtain a solution to transmit code design subproblem via the coordinate descent framework. Numerical examples illustrate the superior performance of the proposed method compared to benchmarks that design sequences assuming ideal ADC at the receiver. [ABSTRACT FROM AUTHOR]

In this article, we define an ambiguity function (AF) for the generalized wideband multiple-input multiple-output (MIMO) radar, wherein the AF combines the signal processing in the fractional Fourier domain with all the functions of conventional AFs. We name it as the fractional AF for the wideband MIMO radar. In order to obtain the AF, we start from introducing the fractional order that relates to fractional signal processing into the matched-filter exploited by the AF, based on which we then derive the explicit expression of the AF. We provide explanations and implications of the fractional AF, and also derive its simplifications in terms of the case of slow-moving targets in far field. Moreover, we derive corresponding properties for the fractional AF, and meanwhile, we conduct analysis on the computational complexity of the AF. On the basis of the AF definition as well as its simplifications, we establish relationships of our proposed fractional AF to conventional AF forms. We show that our defined fractional AF can serve as a generalized AF form since conventional AFs are special cases of it. Our simulations show that the proper selection of fractional-orders can enable low sidelobe levels of the fractional AF for wideband MIMO radar. [ABSTRACT FROM AUTHOR]

RF sensors have been recently proposed as a new modality for sign language processing technology. They are noncontact, effective in the dark, and acquire a direct measurement of signing kinematic via exploitation of the micro-Doppler effect. First, this work provides an in depth comparative examination of the kinematic properties of signing as measured by RF sensors for both fluent ASL users and hearing imitation signers. Second, as ASL recognition techniques utilizing deep learning requires a large amount of training data, this work examines the effect of signing kinematics and subject fluency on adversarial learning techniques for data synthesis. The following two different approaches for the synthetic training data generation are proposed: 1) adversarial domain adaptation to minimize the differences between imitation signing and fluent signing data and 2) kinematically-constrained generative adversarial networks for accurate synthesis of RF signing signatures. The results show that the kinematic discrepancies between imitation signing and fluent signing are so significant that training on data directly synthesized from fluent RF signers offers greater performance (93% top-5 accuracy) than that produced by adaptation of imitation signing (88% top-5 accuracy) when classifying 100 ASL signs. [ABSTRACT FROM AUTHOR]

In this article, a joint transmit resource management and waveform selection (JTRMWS) strategy is put forward for target tracking in distributed phased array radar network. We establish the problem of joint transmit resource and waveform optimization as a dual-objective optimization model. The key idea of the proposed JTRMWS scheme is to utilize the optimization technique to collaboratively coordinate the transmit power, dwell time, waveform bandwidth, and pulse length of each radar node in order to improve the target tracking accuracy and low probability of intercept (LPI) performance of distributed phased array radar network, subject to the illumination resource budgets and waveform library limitation. The analytical expressions for the predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and subsequently adopted as the metric functions to evaluate the target tracking accuracy and LPI performance, respectively. It is shown that the JTRMWS problem is a nonlinear and nonconvex optimization problem, where the above four adaptable parameters are all coupled in the objective functions and constraints. Combined with the particle swarm optimization algorithm, an efficient and fast three-stage-based solution technique is developed to deal with the resulting problem. Simulation results are provided to verify the effectiveness and superiority of the proposed JTRMWS algorithm compared with other state-of-the-art benchmarks. [ABSTRACT FROM AUTHOR]

We consider a dual-function radar communication (DFRC) system employing frequency hopping (FH) radar waveforms. Each communication symbol is represented by a phase-modulated pulse sequence that multiplies the radar hops in fast-time and transmitted from a multiple-input multiple-output radar platform. In this respect, the DFRC system considered follows the code-shift keying (CSK) signal embedding strategy. The phase modulations include phase-shift keying (PSK), derivative PSK, and continuous phase modulation. The CSK pulse sequences and the FH code are jointly designed to reduce range sidelobe levels (RSLs) and combat range sidelobes modulation. It is shown that the joint optimization using the genetic algorithm yields desired CSK sequences and FH code matrix with less computational complexity compared to the exhaustive search algorithm. The power spectral density and detection performance of various DFRC systems are analyzed, underscoring the tradeoff between RSLs and spectral sidelobe levels. The proposed DFRC system with optimum phase sequences and hopping code achieves high data communication rates by permitting frequencies to be reused in the radar hops. [ABSTRACT FROM AUTHOR]

A spectrum sharing radar can be guided by a cognitive decision process to determine the optimal radar operating frequency as the spectral environment changes. This decision process utilizes spectrum sensing or spectral prediction to determine the optimal radar transmission for a given situation. The radar transmitter power amplifier performance varies with frequency and bandwidth of the applied waveform, thus adaptive impedance tuners are useful in maximizing the transmitted power and radar range as the transmission frequency range is varied. Since high power handling is required in radar transmissions, and mechanically actuated impedance tuners presently demonstrate the best power handling, the time required to tune is often orders of magnitude greater than the pulse repetition interval. As such, the relatively lengthy impedance tuning operations should be guided to maximize the average output power as the system transitions between different center frequencies, bandwidths, and waveforms over time. This article presents an algorithm that performs impedance tuning with an evanescent-mode cavity tuner based on an average performance gradient computed for multiple transmit pulses. Comparison of test results with traditionally measured amplifier load-pull data shows that the transmitter is effectively optimized for maximum average output power. [ABSTRACT FROM AUTHOR]

The signal coherence on target is vulnerable toconfiguration perturbationsof an airborne distributed radar system where radar nominal positions are disturbed by unknown position errors to radar actual positions in three-dimensional space. This article copes with the configuration calibration problem of an airborne distributed radar system using several auxiliary devices in known locations, e.g., ground auxiliary receivers. To warrant parameter identifiability, for given transmitted signals from radars, two types of echoes are exploited, including the internal ones received by radars and external ones received by auxiliary receivers. The range measurements can be extracted from these echoes subsequently. The theoretical analysis claims that an identifiability condition for configuration calibration, precluding pathological configurations, is to have at least two auxiliary receivers when the location of one radar is known. Besides, configuration calibration is a highly nonlinear problem due to the coupling of position errors between two different radars and to the existence of second-order position error terms in the range measurement equations. We introduce the novel idea of auxiliary ranges to eliminate the second-order position error terms and the iterative fashion to handle the coupling in the methodology to enable the formulation of pseudolinear equations, thereby leading to the establishment of an algebraic solution for radar position estimation. Numerical simulations are included to corroborate the theoretical results and to demonstrate the superiority of the devised method in comparison with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Helicopter main rotor blade flashes in radar measurements are an often-used feature for target classification. While focus is often placed on being able to detect them, little attention has been given to what is required to ensure that they are reliably and persistently intercepted. Using mathematical analysis and simulation, this article explores the probability of helicopter blade flash interception in search radar. The effects of radar antenna azimuth beamwidth, scan rate, and waveform on the average probability of intercept for two-bladed helicopter blade flashes (a worst-case scenario) are examined. The results presented provide an upper bound on achievable flash detection performance and can be used to understand and optimize the performance of helicopter classification modes. [ABSTRACT FROM AUTHOR]

To develop a 3-D imaging synthetic aperture radar(SAR) algorithm, a theoretical expression for a Fourier transform of data cube is required. In this article, we introduce a function representing the Fourier transform of 3-D SAR data cube. The function is derived using the method of stationary phase and similar to the one used for 2-D data matrix. For verification and evaluation, a 3-D range migration algorithm using this function for bulk compression is examined. The simulation results based on the parameters of an experimental terahertz indoor SAR testbed show that the algorithm can well focus the 3-D image and, hence, verify the derived function. The image quality assessments such as spatial resolutions and peak sidelobe ratio are used to further evaluate the function and the algorithm. The measured results are compared with the ones provided by a backprojection algorithm. [ABSTRACT FROM AUTHOR]

In this article, an active multireceiver low-THz imaging radar is used to generate 3-D images of automotive environments by combining time (difference) of arrival (TDoA) information with backprojection techniques to give height profiles of the objects in the scene. The large bandwidth of this system facilitates noncoherent height reconstruction using time of arrival alone. In doing so, the constraints on receiver coherency are relaxed compared to many height-finding techniques. 3-D reconstructions of common roadside targets and scenes are generated through a modified form of backprojection, adapted to use the time of arrival information of the signals from multiple receivers with different sensitivities, which are arranged in a quasi-multistatic configuration. Here, it is experimentally shown that the use of three vertically-aligned receivers is sufficient to provide a fine resolution in range, cross-range, and height. The system is used to generate 3-D reconstructions of a number of different scenarios, including pedestrians moving through a scene, and complex real-world road scenes. [ABSTRACT FROM AUTHOR]

This article presents an image-based radar detector, named neighborhood difference order statistics (NDOS) detector. Different from the classic likelihood ratio detector, the proposed detector treats the echo spectrum as an image and determines the existence of a target by comparing the difference between the test cell and its adjacent cells with a threshold. The closed-form expressions of probabilities of detection and false alarm are derived under Gaussian noise background and Swerling I target model. It is proved that the detection performance of the proposed detector approaches the optimal likelihood ratio detector when the homogenous noise power is known. When the noise power is unknown, we modify the detector into cell-averaging (CA) NDOS detector by estimating the noise power. Analytical derivations show that the CA-NDOS detector holds the constant false alarm rate (CFAR) property. Moreover, CA-NDOS detector possesses a better detection performance compared with two typical CFAR algorithms under the condition of typical reference window size. [ABSTRACT FROM AUTHOR]

Diversity smoothing has been widely used for angle estimation with multiple input multiple output (MIMO) radar in the presence of coherent or correlated targets, and the parameter identifiability is an interesting issue. Previous works have shown sufficient conditions for some special cases, such as the coherent targets with conventional MIMO radar, and the array size are derived as a function of the target number and target structure. In this article, we further introduce the interelement spacings to build a complete parameter identifiability scheme. For monostatic MIMO radars, the antenna numbers and interelement spacings of transmit and receive arrays are derived as functions of the target number and the target structure. The optimization models are built to calculate the maximum number of detectable targets for a given target structure. Additionally, the conditions for the bistatic MIMO radar are derived from two-dimension viewpoint. It is shown that the new results improve upon previous spatial smoothing or diversity smoothing methods and recover them in special cases. Simulation results are presented that corroborate our theoretical findings. [ABSTRACT FROM AUTHOR]

In this article, we consider the problem of adaptive detection of a multichannel subspace signal in the presence of constrained interference, which is assumed to be orthogonal to the signal in the whitened space. We derive the gradient test, which is found to have the same form as the existing subspace-based generalized likelihood ratio test (SGLRT). In addition, we derive the statistical performance of the SGLRT in the presence of orthogonal interference for the case of signal mismatch, which means that the actual signal does not completely belong to the presumed signal subspace. Numerical examples are provided to verify the theoretical results. It is shown that both the orthogonal interference and signal mismatch can degrade the detection performance of the detectors. [ABSTRACT FROM AUTHOR]

The concept of sub-terahertz (sub-THz) imaging and recognition of space objects by a satellite-based inverse synthetic aperture radar (ISAR) is proposed and analyzed in this article. Operation at sub-THz frequencies in the band 200–400 GHz benefits from wide available bandwidth, high sensitivity to surface texture, and very small target features, and requires an antenna of small physical size. A feasibility study is presented based on use case scenarios of the relative motion of monitoring satellite and observed objects. Expected resolutions, power budget, and oscillator stability requirements in this frequency band are discussed. Image formation methods are proposed and evaluated with simulations. A simple electromagnetic modeling capability is proposed to create a comprehensive dataset of sub-THz ISAR images, with the orientation and aspect angles seen by the sensors, which can be used to train semantic classifiers. [ABSTRACT FROM AUTHOR]

A dual-polarized radar system is utilized for non-contact sensing applications involving rotational motion. Polarization-centric methods are employed to sense and characterize responses from an electric motor with various bearing faults and to sense responses from a high-velocity fan. Conventional characterizations, such as power spectral densities and micro-Doppler responses, are compared with polarization-based statistical approaches such as polarization entropy and a newly proposed micro-Entropy approach inspired by the micro-Doppler approach. [ABSTRACT FROM AUTHOR]

This article presents a Bayesian approach for sequential detection of anomalies in the motion of a target and joint tracking. The anomaly is modeled as a binary (on/off) switching unknown control input that goes into action (begins to exist, or "switches on") thereby modifying the object dynamics; and by ceasing its activity (becoming nonexistent, or "switching off") returns the dynamics to nominal. The developed Bayesian framework brings together random finite set (RFS) theory to represent the switching nature of the anomaly, and optimal joint input and state estimation to sequentially update a hybrid state that incorporates a random vector for the kinematic state and a Bernoulli RFS for the unknown control input. In addition, a closed-form solution, the Gaussian-mixture hybrid Bernoulli filter (GM-HBF), has been developed to provide a customized solution for dynamic anomaly detection in the maritime domain characterized by linear Gaussian target dynamics. Based on the Ornstein–Uhlenbeck dynamic model for vessels, where the evolution of the object velocity is governed by a piecewise mean-reverting stochastic process, the anomaly can be represented by a change in the long-run mean velocity (i.e., the unknown control input) from the nominal condition that forces the vessel to deviate from its standard route. We demonstrate the effectiveness of the proposed GM-HBF in both simulated and real-world maritime surveillance applications and test its performance in the face of false measurements, detection uncertainty, and sensor data gaps. [ABSTRACT FROM AUTHOR]

Interference can degrade the detection performance of a detector. Previously, several detectors were proposed according to the criteria of Rao and Wald tests in the presence of coherent interference. However, these detectors were derived by using incorrect Fisher information matrices (FIMs). This article considers the problems of subspace target detection in the presence of three cases of coherent interference. We redefine the complex parameter sets and correct the FIMs as well as the corresponding Rao and Wald tests. It is shown that some corrected detectors are new, some coincide with existing ones, and some are the subspace generalization of an existing one. There is no reasonable Rao or Wald test for a kind of coherent interference. Moreover, we derive the statistical performance of one corrected detector, including the analytical expressions for probability of detection and probability of false alarm. We also show the statistical distributions of another corrected detector which is the subspace generalization of an existing one. Numerical examples are provided to evaluate the performance of the corrected detectors. The results show that two Rao test-based detectors have good selectivity with signal mismatch, and a Wald test-based detector is robust for mismatch signals. [ABSTRACT FROM AUTHOR]

In this article, we propose optimum and near-optimum adaptive coherent detectors of radar targets in compound-Gaussian clutter with generalized inverse Gaussian texture (CG-GIG clutter). The target amplitude and the speckle covariance matrix are modeled as unknown quantities to be estimated. On the basis of the two-step generalized likelihood ratio test (GLRT) and the estimate of the speckle covariance matrix, the optimum coherent detector and its adaptive version are designed in this article. It is demonstrated that the proposed optimum coherent detector contains three common detectors, which are the optimum K detector, the generalized likelihood ratio test linear-threshold detector (GLRT-LTD), and the GLRT detector for compound-Gaussian clutter with inverse Gaussian texture. Due to the existence of the modified Bessel function depending on data, the optimum coherent detector is computationally unrealizable. Therefore, a near-optimum coherent detector with a new structure named $\alpha$ matched filter ($\alpha$-MF) detector and its adaptive version are proposed, which decreases the computational cost. The proposed near-optimum coherent detector contains two common detectors, the GLRT-LTD and the $\alpha$-MF detector in K-distributed clutter, and has a comparable detection performance of the near-optimum detector in compound-Gaussian clutter with inverse Gaussian texture, which was proposed before. Theoretical analysis and numerical experiments illustrate that the proposed two detectors for CG-GIG clutter have the constant false alarm ratio property relative to the estimate of the speckle covariance matrix and the Doppler steering vector. Moreover, the detection performance of the two coherent detectors are evaluated by the simulated and real data. [ABSTRACT FROM AUTHOR]

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

Frequency diverse array (FDA), which employs a small frequency increment across its array elements to generate controllable degrees-of-freedom (DOFs) in range dimension, has attracted extensive attentions in recent years. In order to extract and exploit the DOFs in range and angle dimensions, FDA has been combined with multiple-input multiple-output (MIMO) technique, which shows sufficient performance improvement in clutter suppression in scenarios with range ambiguities. Therefore, clutter rank evaluation is an important issue for FDA-MIMO radar mounted on a moving platform. In this article, a clutter rank evaluation criterion is developed with respect to different transmit/receive array configurations and different number of range ambiguities. It is based on a subspace transformation matrix that decouples the clutter subspace and allows a direct determination of clutter rank. With the proposed clutter rank evaluation criterion, the maximum resolvable number of range ambiguities can be determined, which is bounded by the system's DOFs. Three cases according to different relationships between uniform array element spacings are studied. Then, the aforementioned clutter rank evaluation method is extended to nonuniform array configurations. Moreover, the robustness of the method is studied in the presence of gain-phase errors. Several simulation results are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) is an important airborne radar technique used to improve target detection in environments of clutter and jammers. Effective STAP implementations are dependent on an accurate estimate of the space-time covariance matrix, which characterizes noise and interference in the radar signal. Inside-looking monostatic radar systems, the estimate based on secondary radar observations is rather straight forward as all the samples in secondary data can be argued to be from a single distribution, and the sample covariance can be used as an estimate of the space-time covariance matrix. However, in many other radar configurations, the vital underlying STAP training assumption that secondary data are identically distributed is violated, which implies that detection performance can be significantly degraded. This article develops a new method that can be used when secondary data do not share a common distribution due to geometry-induced range dependencies. This phenomenon is of particular concern in bistatic radar systems. We propose a model-based approach, where the distribution of noise and clutter for each range bin is parameterized by a set of scenario parameters. Using secondary data, the scenario parameters are estimated by maximizing the likelihood function. Based on the estimated scenario parameters, the STAP covariance estimate is formed for the cell under test. The presented method is compared with other state-of-the-art methods for bistatic radar STAP via numerical simulations. The simulations indicate that the presented method, with a proper initialization, yields an estimate of the STAP covariance matrix that significantly increases the signal-to-interference-plus-noise ratio compared to the other investigated methods. [ABSTRACT FROM AUTHOR]

The cylindrical polarimetric phased array radar (CPPAR) project acts as a case study in cylindrical array design for applications in dual-polarized weather radar, allowing fundamental research such as beamformer optimization and weather measurement verification to be conducted. Developed at the University of Oklahoma’s Advanced Radar Research Center (ARRC), the CPPAR system relies on GPS-disciplined reference clocks for its transceivers and nearly synchronous triggering to transmit and receive bistatically from an isolated measurement horn located in the main array’s far-field, allowing the array’s element and beam patterns to be measured. This measurement scheme introduces phase distortion resulting from time-varying differences between the two reference clocks’ frequencies, a phenomenon which adds a time-varying bias to baseband phase measurements, rendering them unusable without significant post-processing. In this article, our approach to managing this and other measurement-specific challenges is detailed. To provide appropriate background information, the CPPAR’s relevant high-level system design, control systems, and theory is also discussed. Lastly, our method for deriving array bias terms from measured element patterns is detailed. The procedures outlined in this article were successfully deployed to measure element patterns and in turn derive power and phase calibration weights for the system, which ultimately allowed for the completion of two major experiments using the platform. [ABSTRACT FROM AUTHOR]

Target localization with unambiguous range and angle estimation of the signal-of-interest is a key task of a radar system, especially for wide region surveillance. In this article, we develop a novel dual-mode array radar scheme, namely phased array and frequency diverse array cooperated radar, to provide unambiguous range and angle estimation. Our approach combines the advantages of the PA in high transmit gain and that of the FDA in resolving range ambiguity. In general, two kinds of cooperative parameter estimation algorithms are derived to determine the range and angle parameters jointly. The proposed algorithms can be interpreted as different combination methods of the PA and FDA, including the uniform combination and weighted combination based on weighted least squares, where the weight is designed with user tunable angle parameters. Particularly, the design criterion for the tunable parameter is given. At the analysis stage, the Cramér–Rao bounds are studied to verify the performance of parameter estimation. Extensive simulation results demonstrate the effectiveness of parameter estimation with the dual-mode array radar. [ABSTRACT FROM AUTHOR]

Carrier modulation, a variant of intermodulation distortion, is described and exploited for the remote detection of radio-frequency (RF) electronics. To generate carrier modulation, a single frequency illuminates an electronic device, which is actively transmitting, and mixing of the applied probe tone with the device's emission produces intermodulation distortion. Theory predicts that carrier modulation is detectable from an emitter at a greater distance away from a radar than the traditional intermodulation distortion. Benchtop measurements taken using commercial RF electronic devices confirm this theory. The experiments demonstrate that carrier modulation may be stimulated remotely from a variety of RF electronics. The spectrum of the distortion may be used to pinpoint an emitter's frequency-of-operation. [ABSTRACT FROM AUTHOR]