In this paper, we focus on an optimization of the transmit parameters of a frequency diverse array (FDA) radar in order to reduce tracking root mean square error (RMSE) in complex environment with significant measurement errors. Since the classical interacting multiple model (IMM) method cannot effectively utilize the degrees of freedom caused by frequency offset, a cognitive tracking method based on closed-loop cognitive feedback and transmit frequency offset optimization has been developed to adapt to the changing environment. In addition, perceptual information entropy is introduced to quantify the uncertainty in the tracking process. Simulation results demonstrate that the proposed method exhibits superior tracking performance over conventional methods. • This paper presents a cognitive target tracking framework. • The proposed method demonstrates robust tracking capability. • This paper optimizes frequency offset with a minimum tracking error criterion.

As an extension of the phased-array (PA) and frequency diverse array multi-input multi-output (FDA-MIMO) radars, the coherent FDA (C-FDA) radar simultaneously enjoys the merits from a higher coherent array gain and range-dependency, especially the range-angle decoupling. Based on these properties, C-FDA radar is potential to implement more robust target tracking. This paper proposes a subspace-based range-angle tracking method, including the subspace tracking and range-angle estimation. We first design a data processing structure for C-FDA radar, separating the range-dependent data from the angle-dependent data. Then we use the exponential decay function inspired from the information geometry to achieve fast convergence for the subspace tracking. Numerical results describe the range-angle tracking trajectories of C-FDA radar by using the proposed algorithm, indicating its superiority through the root mean square error (RMSE) and the subspace estimation performance (SEP).

Frequency diverse array multiple-input multipleoutput (FDA-MIMO) allows the suppression of signals from undesired range-azimuth regions, positioning it as a promising approach for mitigating mainlobe spoofing jamming that traditional phased-array (PA) find challenging to address. However, distinguishing real targets from false ones, especially when confronted with deceptive signals generated by modern Digital Radio Frequency Memory (DRFM) jammers, remains a significant challenge for FDA-MIMO systems due to the difficulty of signal feature-based discrimination. To address this, we introduces a strategy for differentiating between real and false mainlobe target signals using a dual-node distributed FDA-MIMO (DD-FDAMIMO) radar system. This method leveraging the mismatch between the observed Capon spectra of the received signals and their theoretical counterparts. Simulation results demonstrate that the proposed method achieves correct discrimination probability exceeding 99% for both true and false target when SNR/INR greater than 7dB. The impact of system configurations on correct discrimination rate for false target is also simulated.

Target two-dimensional (range and angle dimensions) localization is meaningful in sensor application areas, but most of typical techniques provide angle-only localization. Frequency diverse array (FDA) sensor offers potential target two-dimensional localization due to its angle-range-dependent beampattern, but it has the range and angle coupling problem. In this paper, an ambiguity function(AF)-based multiple signal classification (MUSIC) algorithm, namely, AF-MUSIC, is presented for the FDA and multiple-input multiple-output (MIMO) combined FDA-MIMO radar to address two-dimensional localization of moving targets. The essential difference lies in that traditional MUSIC algorithm regards the FDA-MIMO radar transmitted multi-carrier waveform as multiple signals, while the AF-MUSIC algorithm takes the multi-carrier waveform as a whole signal. Simulation results show that the proposed method achieves better performance in the localization accuracy and resolution than traditional MUSIC algorithms.

This paper studies the problem of adaptive distributed target detection for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar, where the target is embedded in Gaussian clutter with unknown covariance matrix. The proposed FDA-MIMO radar detection model establishes distributed target detection as a summation expression, which is different from the traditional detection models in MIMO or phase array radars that discuss only point-like target. Next, according to the rules of generalized likelihood ratio tests (GLRT), Rao and Wald tests, we designed three adaptive detectors without the training data. The numerical results validate the proposed method and all theoretical analyses.

Non-contact vital signs monitoring with frequency modulated continuous wave (FMCW) MIMO radar has attracted increasing attention. However, in practical applications, existing methods cannot accurately and robustly measure vital signs due to the influence of indoor multipath propagation. Therefore, this paper proposes a joint design method of waveform parameter optimization and transmit-receive weighting based on FMCW-MIMO radar to suppress multipath interference. This method uses quasi-orthogonal FMCW with low range sidelobe level, which can separate undesired multipath from the desired signal and suppress it by adaptive transmit-receive weighting. Theoretical and simulation results show that the method has good performance in indoor multipath environments with mean errors of 0.608 (bpm) for respiration rate and 3.164 (bpm) for heart rate.

This paper investigates the adaptive target detection problem in FDA-MIMO radar embedded in Gaussian noise with an unknown covariance matrix, considering the scenario of mismatched steering vectors. Firstly, we assume that the covariance matrix of the test data and training data has the same structure with different magnitude levels. Next, the subspace model is adopted to enhance the robustness of the detector against mismatched signals. Furthermore, TS-GLRT detector is designed based on the generalized likelihood ratio test (GLRT), then we validated its constant false alarm rate (CFAR) property. Finally, simulation results demonstrate the effectiveness of the proposed approach.

Ambient backscatter communications (AmBC), which utilizes ambient radio frequency (RF) signal for communications, is a promising technology for Internet-of-Things (IoT) with stringent power-and-cost constraints. To address the issue of jammer attacking a multi-antenna AmBC system with M-ary frequency shift keying (MFSK) modulation, we propose two detectors: Maximum A Posterior probability (MAP) detector and Energy-Threshold determination (ETD) detector, which are considered for the frequency spectrum of the jammer overlaps the passband of filters or not. Simulation results demonstrate that implementing multiple antennas and higher order modulation can enhance the system performance. Moreover, when the frequency spectrum of the low power jammer does not overlap with the passband of the filters at the receiver, the MAP detector performs better, conversely, the ETD detector outperforms the MAP detector.

To achieve full coherence, it is indispensable for distributed coherent aperture radar (DCAR) to estimate coherence parameters (CPs) using orthogonal waveforms in MIMO mode. However, incomplete orthogonality between waveforms and initial time and phase synchronization errors between the subapertures introduce estimation errors, which degrade full coherence performance. To tackle these issues, we propose an orthogonal waveform design framework based on ambiguity function (AF) shaping to improve CPs’ estimation accuracy. We combine the alternating direction method of multipliers (ADMM) and the majorization-minimization (MM) algorithm to transform the nonconvex quartic optimization problem into a quadratic optimization problem. Numerical simulation results show that the optimized waveforms significantly enhance the estimation accuracy of CPs.

In frequency diverse array multiple-input–multiple-output (FDA-MIMO) radar for target detection, besides range migration and Doppler migration, serious Doppler spread in the slow-time domain also will be a problem because the frequency offset between transmitting arrays is coupled with the target velocity, which results in the signal energy of each receiving channel not being coherently accumulated to reduce the detection performance. To address this issue, this letter proposes a method for FDA-MIMO radar moving target detection using planar array. The moving target returned signal of FDA-MIMO radar using planar array is established. After multi-channel matched filtering, resampling in slow-time domain and phase compensation functions of the acceleration and velocity ambiguity factor are applied to correct the range migration and Doppler migration, and compensate for the Doppler spread caused by frequency offset. In doing so, the target’s energy is coherently accumulated. Simulation results verify the effectiveness of the proposed algorithm. Under the same parameter conditions, in order to achieve a 90% detection probability, the proposed algorithm allows the signal-to-noise ratio (SNR) of input echo signal reduction of at least 3dB.

To address the problems of requiring a large number of labeled training jamming samples in practical application, we propose the PSPNet, an improved prototypical network with pretraining and self-supervised fine-tuning, which can achieve high-precision radar active deception jamming recognition with few shots. For the time-frequency images of radar returns including deception jamming, we construct a deep network with multiple convolutional layers as the encoder. The encoder firstly preformes supervised pretraining on the Omniglot dataset with multiple categories, to extract the geometrical characteristics through learning. After that, for domain adaptation, the encoder is fine tuned in a self-supervised paradigm on the simulated unlabeled active deception jamming data. Finally, the encoder using prototypical network is applied to active deception jamming recognition under very few samples. Comparative experiments show that, the proposed PSPNet achieves high accuracy with 97.02% and 98.49% respectively in 5-shot case on both simulation and real data, which outperforms existing methods. The ablation experiments also demonstrate that both pretraining and self-supervised fine-tuning can improve the proposed network’s performance.

This work investigates the design of beampatterns for frequency diverse arrays-multiple-input multiple-output (FDA-MIMO) in the range-angle plane, in order to improve the approximation of a desired beampattern. Recognizing that the energy radiated by the array cannot be locked at a fixed range and angle, the beampattern is designed for the equivalent beampattern at the receiving end, differing from the traditional emphasis on the transmit beampattern. The proposed scheme formulates the beampattern design as a minimization problem, where the cost function is defined as the squared error between the designed and the given beampatterns. The developed scheme is then implemented through an optimal selection of both the FDA frequency offsets and the receiver steering weights. To this end, an iterative algorithm with monotonic convergence is introduced to solve the resulting non-convex optimization problem involving a fourth-order polynomial objective function as well as multiple non-convex constraints. Numerical simulations verify the performance of the algorithm and show that the proposed scheme can efficiently concentrate the energy of the echo signal on the desired region in the range-angle plane.

Considering that frequency diverse array multiple-input multiple-output (FDA-MIMO) possesses extra range information to enhance sensing performance, this paper explores the FDA-MIMO-based integrated sensing and communication (ISAC) system. To reinforce the system communication capability, we propose the frequency offsets permutation index modulation (FOPIM) scheme, which conveys extra information bits by selecting and permutating frequency offsets from a frequency offsets pool. For the system communication function, considering the fact that the traditional maximum likelihood detection method suffers from high complexity and bit error rate (BER), the maximum likelihood-based two-stage detection (MLTSD) approach is presented to overcome this issue. For the system sensing function, we employ the two-step maximum likelihood estimator (TSMLE) to stepwise estimate the angle and range of the interested target. Furthermore, we derive the closed-form expressions for the tight upper bound on the communication BER, along with the sensing Cramér-Rao bound (CRB). The simulation results validate the theoretical analysis, demonstrating that the proposed system exhibits lower BER and superior range resolution than independent MIMO communication and sensing modules.

Image despeckling is important to synthetic aperture radar (SAR) image restoration and related downstream tasks. Due to the fact that existing SAR image denoising algorithms are difficult to simultaneously achieve high performance, efficiency and interpretability, in this paper, we propose a new image denoising method, namely, Trans-NLM, which incorporates the Transformer architecture into traditional nonlocal means filtering (NLM) based denoising algorithm. In doing so, the SAR image despeckling performance is enhanced, while the algorithm interpretability is retained. First, each pixel and its surrounding pixels are simultaneously mapped to a high-dimensional space as a neighborhood matrix through a shallow fully connected layer. Then, positional encodings are added to the neighborhood matrix, which is further mapped to the internal vectors Key, Query and Value. The final vector representation of each pixel is also calculated according to the multi-head attention mechanism. Finally, the representation vector is passed through a shallow fully connected layer for data dimension reduction to predict the corresponding pixel value. Moreover, layer normalization and residual learning are applied to accelerate the convergence. Experiments on both simulated and real SAR data demonstrate that compared with representative denoising models, e.g., NLM, fastNLM, SAR-CNN, CNN-NLM and MONet, the proposed Trans-NLM exhibits better performance in despeckling enhancement and efficiency simultaneously, along with more explainable inference process and transfer learning capability.

For multiple-input multiple-output (MIMO) frequency diverse array (FDA) communications, this paper proposes a frequency offsets index modulation (FOIM) scheme, which conveys extra information by selecting transmitting frequency offsets from a frequency offsets pool. To improve system spectral efficiency, we firstly design an orthogonal baseband waveform for FDA with the exact expression, then the corresponding receiver structure is proposed. Further, considering that the traditional maximum likelihood (ML) detection algorithm suffers from high complexity, an output combined maximum likelihood (OCML) approach is presented. Moreover, the closed-form expressions for the upper bound on bit error rates (BERs) of both ML and OCML methods are derived, as well as the counterpart of the system capacity. The simulation results show that the capacity of the proposed FOIM scheme outperforms the MIMO scheme, and meanwhile, our method achieves a higher communication rate when compared with the quadrature spatial modulation (QSM) scheme. Additionally, the proposed OCML algorithm can lead the BER performance of FOIM to be superior to that of the aforementioned approaches with significantly lower computational complexity.

In this work, we introduce a novel approach for designing the transmit frequency offset scheme based on Cramér-Rao lower bound (CRLB) minimization for a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. The problem originates in non-uniform FDA radar where each frequency offset scheme derives from a specific mathematical model, but where no optimization is conducted with respect to the frequency offset design. We propose two frequency selection schemes for FDA-MIMO radar based on A-optimal minimization, both incorporating a priori knowlegdge. The worst case optimal FDA-MIMO (OFDA-MIMOW) radar forms the frequency selection by minimizing the CRLB over a grid of the unknown parameters, whereas the Bayesian optimal FDA-MIMO (OFDA-MIMO-B) radar instead minimizes the Bayesian CRLB (BCRLB). The performances of the methods are evaluated using simulated data and compared to other common frequency selection schemes.

The space-time adaptive processing of the airborne distributed flexible array radar is a forefront technology that has attracted much discussion. In this letter, we consider three types of distributed flexible arrays mounted on different aircraft parts: the wing array, the aircraft nose array, and the fuselage array. According to the deformation structure, three types of flexible arrays' space-time signals are modeled. Then, a non-stationary clutter suppression method based on angle-Doppler compensation is proposed. The clutter eigenspectrum, spatial-Doppler spectrum, and STAP performance of three kinds of arrays are compared in numerical simulations, from which the proposed compensation STAP method has a positive effect on the improvement factor for the flexible array, and the aircraft nose array has better STAP performance than the others.

In this paper, we design the physical layer security of a dual-hop spatial modulation system provided the relay node transmits its own private information. Meanwhile, the quadrature virtual channel spatial modulation (QVCSM) is proposed to enhance the physical layer security in the second hop. To achieve system security in both angle and range dimensions, the frequency diverse array (FDA) is used in the relay node with a decode-and-forward (DF) protocol. Moreover, the baseband directional modulation (DM) and artificial noise (AN) are jointly utilized to suppress the received signal at the passive eavesdropper. Additionally, the Ergodic secrecy rate of the proposed system is analyzed, whereas the closed-form expressions for tight upper bounds on the bit error rates (BERs) of the source and relay are derived for both the destination and eavesdropper. The simulation results show that the proposed FDA relay design outperforms the traditional phase array (PA) relay system in terms of the system secrecy rate. In contrast to the quadrature spatial modulation (QSM) method, the proposed designs achieve an improved suppression performance of the eavesdropper.

In this paper, we study the moving target detection problem for frequency diverse array multiple input multiple output (FDA-MIMO) radar under the assumption of Doppler spread (DS) and range migration (RM). Doppler spread is caused by the frequency offset between the arrays being closely coupled with the target velocity. Due to the range migration in each re-ceiving channel in a coherent processing interval and Doppler spread in the joint spatial-time domain, the degraded coherent integration gain will reduce the target detection performance. Even, false targets will appear and interfere with detection when the frequency offset or target velocity is large. To ad-dress this problem, a quadratic scale transform combined with resampling method is proposed to eliminate the DS problem caused by frequency offset and the RM caused by velocity in moving target detection. The effectiveness of the proposed approach is validated by numerical results.

Differs from conventional phased-array, frequency diverse array (FDA) employs a frequency increment across the array elements and thus produces a range-angle-dependent beampattern. This additional range dependence finds applications in target detection/localization, high-resolution synthetic aperture radar (SAR) imaging, and SAR deceptive jamming. To capture the essence behind those applications, we analyze the range, angle, and Doppler resolution capabilities of FDA radar signal via ambiguity function. As conventional ambiguity function is defined for a single-input single-output (SISO) signal, we extend it to the generalized ambiguity function for FDA radar signal, parameterized with the range, angle, and Doppler shifts of a target. Both theoretical analysis and numerical results show that the FDA radar with a large frequency increment has potentials in high-resolution ranging but will suffer from additional range ambiguity within the range bin and Doppler ambiguity. Moreover, the use of frequency increment brings FDA radar a virtual transmit aperture but decreases the directional gain.

In this letter, we present an adaptive Bayesian detection framework for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. The targets are detected in Gaussian clutter with unknown but stochastic covariance matrix. We designed two detectors in the Bayesian framework, namely, Bayesian Rao (BRao) detector and Bayesian Wald (BWald) detector without training data. Numerical results reveal that the proposed detectors outperform conventional non-Bayesian counterparts. It is necessary to note that the BWald detector requires higher computational complexity than the BRao detector.

In this letter, a frequency diverse array (FDA) is introduced into synthetic aperture radar ground moving target indication (SAR GMTI) to mitigate both the blind velocity and Doppler ambiguity problems. Due to the$2\pi $periodicity of the echo phases, notches will occur periodically in clutter cancelers at nonzero velocities, leading to the blind velocity problem. In the proposed scheme, the dependence of the measured blind velocity on the transmission frequency is considered, and a new clutter canceler unaffected by the blind velocity problem is constructed via the integration of multiple cancelers with diverse frequencies. Moreover, to resolve the Doppler ambiguity, along-track interferometry (ATI) based double interferometry is proposed to extend the maximum unambiguous radial velocity (RV). Additionally, search-based clustering is adopted to enhance the precision of RV estimation. Finally, a moving target can be brought into focus and correctly relocated using the estimated RV. Numerical results verify the effectiveness of the proposed method.

Ground moving target indication (GMTI) has been extensively applied in modern warfare. In this letter, a novel deceptive jamming technique for countering multichannel synthetic aperture radar (SAR)-GMTI is presented, which is implemented with the frequency diverse array (FDA). Different from the phased array (PA), FDA modulates the transmitting signal with multiple frequencies across its array elements, which, consequently, enables range-dimension false target deception for SAR imaging. Furthermore, to achieve effective deception for the GMTI, micromotion modulation is adopted to simulate dynamic features of the moving targets. Mathematical derivation and simulation results show that the proposed technique can efficiently produce massive false targets in both the range and azimuth dimensions.

In this letter, we consider a target with high-order motion and propose two long-time coherent integration algorithms with low computational cost for target detection via phase compensation and time-reversal transform. Due to the even/odd characteristic, the coupling effect between the range frequency and slow time can be eliminated by constructing the phase compensation function and doing the time-reversal transform. In doing so, the target energy can be accumulated by inverse fast Fourier transform (IFFT) in range frequency and fast Fourier transform (FFT) in slow time. Simulations are given to demonstrate the effectiveness of the proposed algorithms. Moreover, compared with the generalized Radon–Fourier transform (GRFT) and generalized dechirp-keystone transform (GDKT), the proposed algorithms have much lower computational burden.

Deep learning algorithms have been widely utilized for synthetic aperture radar (SAR) target detection. Nevertheless, the traditional feature extraction methods and deep learning methods achieve improved ship detection accuracy at a cost of increased complexity and lower detection speed. As detection speed also is meaningful, especially in real-time maritime rescue and emergency military decision-making applications, we propose a new framework of faster region-based convolutional neural network (R-CNN) detection method to handle this problem. A new lightweight basic network with feature relay amplification and multiscale feature jump connection structure is designed to extract the features of each scale target in the SAR images, so as to improve its recognition and localization task network. Moreover, the K-Means method is used to obtain the distribution of the target scale, which enables to select more appropriate preset anchor boxes to reduce the difficulty of network learning. Finally, RoIAlign instead of region of interest (RoI) Pooling is used to reduce the quantization error during positioning. Experimental results show that the detection performance of the proposed method achieves 0.898 average precision (AP), which is 2.78% better than the conventional Faster R-CNN and 800% faster detection speed.

To address the problem of Doppler ambiguity in low pulse repetition frequency (PRF) radar, we propose the use of frequency diverse array multiple-input–multiple-output (FDA-MIMO) radar to detect high-speed moving targets. This method does not require the radar to transmit multiple-PRF pulses. The possible Doppler ambiguity is resolved by exploiting the multicarrier characteristics of FDA-MIMO radar. The effectiveness of the proposed method is verified by simulation results.

In this letter, we propose a frequency diverse array multiple-input multiple-output radar detection architecture for distributed targets embedded in Gaussian interference with unknown but stochastic covariance matrix. At the design stage, we model distributed targets within one range cell as a linear combination of several contributions and assume that the interference covariance matrix obeys the inverse complex Wishart distribution. Then, we devise an adaptive decision rule by jointly exploiting the maximum likelihood approach and the Bayesian framework. Unlike existing contributions in this context, the proposed detector does not require the conventional set of training data to estimate the interference covariance matrix. The numerical examples validate the effectiveness of the proposed method also in comparison with suitable counterparts.

This letter first outlines a flexible conformal array geometric model on variable airborne wings, which can undergo complete shape change according to different combat situations, whereas the deformation may result in array beampattern distorted. In order to realize fast beampattern synthesis for a flexible conformal array, we propose a real-time beampattern optimization algorithm, where we split the protoproblem into two sub-problems based on the alternating direction method of multipliers (ADMM). In the first sub-problem, we transform the quadratic inequality constrained problem into a linear equality constrained problem, and then get the iterative update expression for the variables. Unlike the traditional interior point method, the simulation results demonstrate that the proposed algorithm can efficiently solve large-scale convex optimization problems and greatly reduce the computational complexity.

In this letter, we develop an effective algorithm for direction-of-arrival (DOA) estimation of coherent sources with coprime arrays. Firstly, we generate a virtual uniform linear array (ULA) through coprime array interpolation. Subsequently, we derive an augmented noise-free covariance matrix that is constructed using the entries of the covariance matrix of the virtual ULA outputs, and recover the Hermitian Toeplitz matrix by solving an atomic norm minimization problem with multiple measurement vectors. Finally, the DOAs of coherent sources are estimated via MUSIC spectral search. Unlike the state-of-the-art approach, our algorithm is insensitive to the phase differences among sources. Numerical results demonstrate the superiority of the proposed algorithm over several existing techniques.

Ambient backscatter communication (AmBC), whose passive tags transmit information to readers through radio frequency (RF) signals in the air, has attracted much attention due to its promising prospects in green internet of things (IoTs). However, AmBC detection has faced a new challenge at the reader because the received signal is mainly a hybrid signal from direct link and backscatter link, which makes it difficult to detect symbols of the backscatter tag. To address this problem, in this paper, frequency diverse array (FDA) is utilized to transmit time-variant ambient signal, which can separate the two link signals received by the reader. We propose an adaptive dual-threshold detector by employing the time-variant characteristic of FDA RF signal, which is based on the position of the boundary backscatter tag defined. Specifically, the closed form expressions for maximum likelihood (ML) threshold and adaptive thresholds are derived, respectively. Furthermore, the computational complexities are analyzed. Finally, numerical results are presented to show that the proposed adaptive dual-threshold detector achieves better bit error rate (BER) performance than the conventional ML detector.

In this paper, we develop a novel OFDM-chirp modulation scheme with low peak-to-average power ratio (PAPR). The OFDM-chirp modulation signaling jointly utilizes the frequency-domain chirp modulation and OFDM signaling technique, which can deal with the disadvantages of traditional OFDM modulation including high PAPR, sensitivity to carrier frequency offset (CFO), poor Doppler tolerance and strong inter-carrier interference (ICI). We also introduce an analytical approach to evaluate the corresponding symbol error rate (SER) under CFO in Rayleigh fading channel. Simulation results verify the superiorities of OFDM-chirp modulation signaling over traditional OFDM signaling schemes.

Frequency diverse array (FDA) employs frequency offsets along its array element, thus generating a range-dependent beampattern, giving it the capability to deal with the range-dependent deceptive jamming, which cannot be achieved by the traditional phased array. However, the range and angle are coupled in conventional FDA. To address this problem, the frequency offsets are optimized in this paper with particle swarm optimization to focus the beam energy to the interested spot, facilitating the following imaging process. The effectiveness of the proposed method is validated by numerical simulations.

Abstract Frequency diverse array (FDA) has received much attention due to its both range–angle‐dependent beampattern. However, most of the existing works focus on the linear FDA, and time‐varying characteristics of its beampattern only give a rough qualitative analysis. To further study the spatial beampattern dwell time issues, the pulsed‐planar FDA measurement model is first given, and then, the quantitative analysis of beam dwell time which can be affected by observation point location (azimuth, elevation, and range) and array antenna frequency offset coding method for a given pulse transmitting signal is presented. Finally, the theoretical analysis is proved to be consistent with the simulations.

Abstract A mixed near‐field and far‐field targets localization method based on sparse signal reconstruction and subspace method is presented, which can obtain both direction‐of‐arrival (DOA) and range information of the targets by utilizing symmetric nested frequency diverse array (SNFDA). Due to the frequency diverse array (FDA) owing time‐angle‐range‐dependent beampattern, the authors first properly design frequency increments and choose the sensor outputs to construct a fourth‐order cumulant matrix of SNFDA which is only related to DOAs of targets, and the authors estimate the DOAs of all targets by solving the ℓ 1 ‐norm minimization convex optimization problem. Then a mixed range‐dependent overcomplete dictionary with DOAs estimation is formulated in the compressive sensing framework to classify targets types by using periodic characteristics of the estimated range‐dependent beampattern. Finally, the range estimations of all targets are obtained via 1‐D range‐domain spectral searching. Compared with the existing mixed near‐field and far‐field targets localization methods, the proposed method jointly uses FDA angle‐range‐dependent beampattern and increased degrees‐of‐freedom of nested array, which can achieve both improved resolutions and accuracies in DOAs and ranges estimation for all targets. A set of numerical examples is reported and discussed to validate the superiority of the proposed algorithm.

In this letter, we develop a coarray interpolation method for direction-of-arrival (DOA) estimation using the coprime electromagnetic vector-sensor (EMVS) array. Firstly, we derive the coarray signal model of the coprime EMVS array, which can be viewed as a combination of multiple polarization components. Subsequently, we fill in zero elements in each polarization component and recover the corresponding low-rank covariance matrix by solving a nuclear norm minimization (NNM) problem. By exploiting the recovered covariance matrices, we eventually construct a larger covariance matrix to carry out DOA estimation. Numerical experiment results demonstrate the superiority of the proposed algorithm over conventional methods.

Deception against inverse synthetic aperture radar (ISAR) has been a topic of active research in electronic warfare. However, most of the studies mainly focus on producing one-dimensional deception, which is inadequate for two-dimensional imaging system like ISAR. Thus, in this letter, a joint two-dimensional ISAR deception based on frequency diverse array (FDA) and interrupted sampling is proposed. Compared with conventional phased-array (PA) and single-channel methods, FDA permits more degrees of freedom in producing range deception. Combined with interrupted sampling in the slow time domain, the proposed method is anticipated to produce a group of false targets in both range and cross-range directions. The benefit is easy to control the number and distribution of false targets with high efficiency and less computation burden. Simulation results verify the effectiveness of the proposed method.

Quadrature spatial modulation (QSM) based millimeter wave (mmWave) communication system design using frequency diverse array (FDA) is proposed in this article. Since spatial modulation techniques (SMT) employ maximum likelihood (ML) based detector at receiver, the computational complexity increases vastly. Moreover, numerous existing SMT methods utilize phased array (PA), where only angle dependent communication is possible. In this article, we propose to use a standard FDA that exploits a small linearly increasing frequency offset across the array for range-angle dependent QSM wireless communications. That is, the in-phase and quadrature components of transmission vector utilize slightly different frequencies to deliver the information. Furthermore, we propose a suboptimal multi-stage (MS) detector in the receiver, which applies a matched filtering (i.e., bandpass filters matched to the corresponding frequency offsets) approach to decode the index bits at first step, whereas, merely, two most probable estimated indices are utilized further to decode associated data bits using a standard ML method. Adhering an improved signal to noise ratio (SNR) due to an FDA based range-angle dependent transmission, simulation and numerical results show the improved performance of the proposed design over existing SM and QSM based schemes, while MS approach reduces the receiver's computational complexity.

Spaceborne bistatic synthetic aperture radar (BiSAR) system with geosynchronous-earth-orbit (GEO) transmitter and a low-earth-orbit (LEO) receiver has many advantages which are fine resolution, higher signal-to-noise (SNR), shortest revisit cycle. However, BiSAR data are often azimuth variant because of the sum of two hyperbolic range equation which bring technical challenges to image focus. We address an improved nonlinear chirp scaling (NLCS) algorithm in this paper. The key is introduced a cubic perturbation function in the azimuth domain and used an modified NLCS algorithm to complete azimuth compression, the method of series of reversion (MSR) to obtain an accurate signal spectrum. Simulation results shows the effective of our proposed method.

In this study, the authors develop a new algorithm for passive source localisation using time‐difference‐of‐arrival (TDOA) and frequency‐difference‐of‐arrival (FDOA) measurements. In this algorithm, the position and velocity of the target source are successively estimated by formulating two alone semidefinite programming problems, where the variances of the TDOA and FDOA measurement noises can be separated and neglected. Unlike the traditional methods, the authors’ approach does not require the prior knowledge of the TDOA/FDOA measurement noise power. Simulation results indicate that the proposed algorithm outperforms the existing methods under unknown noise power knowledge.

In this letter, we propose a scattered-wave deceptive jamming approach using a frequency diverse array (FDA) radar against spaceborne synthetic aperture radars (SARs). As an emerging array radar, FDA transmits multiple carrier frequencies across its elements, and thus, a multiple deceptive jamming is potentially produced in the range dimension of the SAR image. Moreover, we consider the scattered-wave jamming, since it has low complexity when used to create spurious scenes and control the jammer locations. The theoretical analysis and numerical simulations show that the scattered-wave jamming with FDA can generate multiple fake targets with the number of targets being dependent on the number of FDA carrier frequencies.

Frequency diverse array (FDA) is an emerging array technique, its essential array characteristics and corresponding potential applications have not been well studied. As array manifold plays an essential role in various array applications, this letter presents an FDA manifold sensitivity framework to analyze the impact of element position uncertainties. Both absolute sensitivity and relative sensitivity are derived for the FDA. The manifold sensitivity analysis is suited for identifying the important position of each array element in an FDA antenna. Moreover, the overall sensitivity can be used to evaluate the robustness of an FDA configuration. All analytical manifold sensitivity characteristics are verified by numerical results.

In this letter, we propose a sparse array design method for adaptive beamforming in the presence of interferences. Our solution is based on finding the beamformer weight vector such that maximum output signal-to-interference-plus-noise ratio is attained. To control the sidelobes of the beampattern, quadratic fractional constraints are also introduced to optimize the beamformer weights. We formulate the array design problem as real-valued quadratically constrained quadratic program (QCQP) with reweighted l1-norm to promote sparsity. Moreover, we adopt semidefinite relaxation (SDR) and linear fractional SDR together to solve the QCQP problem. The resulting array yields excellent beamforming performance and a beampattern with low sidelobes. Numerical results demonstrate the effectiveness of the proposed sparse array design.

In this letter, we devise an efficient approach for estimating the directions-of-arrival (DOAs) of coherent signals using coprime arrays. Specifically, we firstly derive an augmented uniform linear array (ULA) via coprime array interpolation. Subsequently, we define a Toeplitz matrix that is formed using the correlation information of the interpolated ULA theoretical outputs, and recover the Toeplitz matrix by solving a nuclear norm minimization problem. After the low-rank Toeplitz matrix is recovered, the coherent signals are well resolved by the MUSIC algorithm. Simulation results demonstrate the advantages of the proposed approach over various existing methods when dealing with coherent signals.

In this letter, we focus on the problem of direction-of-arrival (DOA) estimation with a nested array in the presence of unknown mutual coupling. Firstly, the measurement model of the nested array with mutual coupling is established, and the properties of the corresponding array covariance matrix are derived. Based on the obtained properties, the coarray output signal is then reconstructed with reduced mutual coupling. Finally, joint sparse recovery technique is employed to extract the DOAs. Our algorithm can achieve underdetermined DOA estimation with satisfactory performance under unknown mutual coupling. Numerical results demonstrate the effectiveness of the proposed algorithm.

The weighted structure tensor (ST) can be usually used on edge detection of multi-channel images, including polarimetric synthetic aperture radar (polSAR) images. The traditional weighting method is average weighting, each channel considered to provide the same edge information. But in fact different channels tend to contain different amount of edge information, so the traditional weighting method cannot be adopted to extract full edge information of the multi-channel images. This letter proposes a novel weighting method for ST, in which the weight of each channel is obtained by the eigenvalue-measured way. Experimental results show that the proposed method outperforms the traditional method on edge detection.

Compared to the monostatic geosynchronous synthetic aperture radar (GEO SAR), GEO bistatic SAR can provide finer spatial resolution and higher signal-to-noise ratio (SNR) with lower system complexity, which leads the GEO-LEO (low earth orbit) SAR be a hot research topic. In this paper, an imaging scheme using cooperative high-orbit and low-orbit satellites is proposed. Firstly, the bistatic range history is extended by Taylor series, and then two-dimensional non-uniform fast Fourier transform (2D-NUFFT) is presented to formulate a bistatic SAR image in the range-velocity domain. The effectiveness of the proposed imaging scheme for GEO-LEO bistatic SAR is verified by simulation data for multi-point targets.

The max–min fairness (MMF) strategy has been widely employed to manage wireless networks since it guarantees fairness among users. However, with a large number of users awaiting service, the network tends to be congested and the quality‐of‐service (QoS) will degrade substantially. This motivates us to study the MMF problem jointly with the consideration of admission control. Specifically, the authors consider a downlink network consisting of a multi‐antenna base station (BS) and multiple single‐antenna users. By jointly optimising the admissible users and the BS transmit beamformers, they aim to maximise the minimum signal‐to‐interference‐plus‐noise‐ratio of the admissible users, such that high QoS and fairness can be guaranteed simultaneously for them. This problem is essentially NP‐hard, and hence they pursue an efficient approximate solution to it. To this end, they first reformulate this problem from the perspective of sparse optimisation, and then develop a low‐complexity algorithm to iteratively solve the approximate problem. Moreover, to facilitate the algorithm's implementation, they further recast the subproblem in each iteration, such that it fits into the framework of the alternating direction methods of multipliers. Finally, an efficient distributed algorithm is designed, with each step being simply computed in a closed form.

Simultaneous wireless information and power transfer (WIPT) has so far mainly relied on a phased-array antenna, but only angle-focusing capability without range focusing can be achieved for phased-array antennas. In this paper, we depart from the phased-array antenna and propose the use of retrodirective frequency diverse array (FDA) focusing in base station for long-range WIPT applications. In the retrodirective FDA focusing, an additional small frequency offset is added across the phase-conjugating mixers in the retrodirective array to produce both range-dimension and angle-dimension focused beamforming for energy-efficient WIPT. The FDA beampattern is decoupled in range-angle dimension and can be independently controlled through properly designing the transmit weighting vector or frequency offsets. More importantly, the proposed WIPT scheme allows self-tracking the direction-of-arrival of the pilot signals without requirement of any a priori knowledge. A maximum efficiency optimization approach based on discrete prolate spheroidal sequence is also proposed for the beampattern synthesis. In addition, robust physical-layer security can be obtained by utilizing FDA angle-range-time-dependent beampattern characteristics. The harvested energy, achievable bit rate, and achievable secrecy rate at the user receivers are derived for the WIPT, along with extensive numerical results. Both the theoretical analysis and numerical results verify the superiority of FDA over phased array in energy efficiency and security for WIPT applications.

Joint direction-of-arrival (DOA), direction-of-departure (DOD), and Doppler estimation for multiple-input multiple-output (MIMO) radar usually are too time-consuming to implement because of high-dimensional searching procedures. This letter proposes a computational efficient algorithm for MIMO radar joint DOA, DOD, and Doppler estimation, which adopts an unstructured maximum likelihood method to sequentially estimate the Doppler, DOD, and DOA by three one-dimensional searching procedures based on the extended invariance principle. Numerical results validate the effectiveness together with its superiorities over conventional maximum likelihood algorithms.

In this letter, we devise a subspace updating algorithm for tracking directions-of-arrival (DOAs) using the difference coarray of a sparse array. Our solution is based on the projection approximation subspace tracking (PAST) framework, where we design new cost function and iterative process via spatial smoothing of the difference coarray output signal. Since the proposed spatial smoothing PAST (SS-PAST) algorithm exploits the aperture of the difference coarray, it attains higher DOA tracking accuracy than the conventional PAST method operating in a uniform linear array. Simulation results demonstrate the effectiveness of the SS-PAST algorithm.

When synthetic aperture radar (SAR) imaging is applied to observe ground scene containing a moving target, the imagery of moving target will be typically smeared due to range cell migration and Doppler spectrum broadening caused by the target motions, especially for the accelerating targets within a long observation time. To eliminate these effects, a novel imaging algorithm using cooperative between SAR under high-orbit and low-orbit satellites is proposed. The range migration including range walk and range curvature within the coherent integration period has been corrected via the third-order keystone transform. Then, by estimating and compensating the phase errors and the fold factor terms, the target's resolution is improved and the motion parameters are correct-ly estimated. The effectiveness of the proposed algorithm is demonstrated by simulations.

Frequency diverse array (FDA) is referred to an array transmitting linearly increasing carried frequencies across the array elements. Compared to traditional phase-arrays (PA), the difference in multi-frequency characteristics will result in that the effective signal-to-noise ratio (SNR) changes with time, angle and distance. This paper also demonstrates that the S-NR of FDA is does not outperform PA under the same conditions in most of time. The decrease of SNR will greatly degrade the estimation accuracy of Frequency Different of Arrival (FDOA) and Time Different of Arrival (TDOA). The significantly degraded FDOA and TDOA estimation accuracy will make the intercepted emitter position being significantly deviated from its actual position, which also means the achievement of localization deception capability.

A uniform linear motion is generally assumed in traditional synthetic aperture radar(SAR) processing algorithms. However, it is inevitable that if there is 3-D acceleration, the image formulation processing complexity will be significantly increased, especially in highly squinted situations. This paper aims to handle the imaging problems in the highly squinted SAR with three-dimensional acceleration, which utilizes the Taylor formula to expand the distance equation and further resolve it. Based on the formulated range formula, we further utilize the two- dimensional non-uniform Fast Fourier Transform (2D-NUFFT) to obtain focused SAR imagery. The proposed methods are verified with simulation results.

For airborne synthetic aperture radar (SAR) processing in its diving stage, the diving velocity brings additional range variance to the range history in the synthetic aperture time. The traditional hyperbolic range model is not accurate enough to approximate the actual range equation and the precise two-dimensional spectrum cannot be achieved. In order to address this problem, a highly accurate spectrum deduction based on high-order equivalent range model is proposed in this paper. By introducing the high-order terms, more degrees-of-freedom are obtained for the equivalent range model and the actual range history can be accurately fitted. Based on the achieved range model, the two-dimensional spectrum for diving stage SAR can be accessed. Simulation experiments are carried out to validate the effectiveness of proposed spectrum.

Compared with stationary targets imaging, detection and imaging of moving targets becomes more difficult in Geosynchronous synthetic aperture radar (GEO SAR), especially when the moving target is drowned in strong ground clutter. In this paper, a scheme of moving target detection and imaging with three antennas is proposed for GEO SAR. Firstly, the displaced phase center antenna (DPCA) is used to eliminate stationary targets' echo. Then, the third-order keystone transform is applied to correct the range migration. Next, after coherent integration, the constant false alarm ratio (CFAR) is used to detect the moving target. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

Based on the synthetic aperture radar (SAR) geometric model, a novel method of jamming space-borne SAR by utilizing the array beam-pattern characteristics of frequency diverse array (FDA) is proposed. A small frequency increment, as compared to the carrier frequency, is applied between the FDA elements to produce multiple false targets in the final space-borne SAR imaging. The separations between the false targets are determined by the frequency offset. Based on the imaging geometry in side-looking mode, this paper derives a range-Doppler (RD) algorithm for the space-borne SAR imaging under FDA jamming signals. Simulation results demonstrate the effectiveness of the proposed approach.

Low‐altitude target detection is of great importance in radar applications, especially for military radar to counter the targets penetrated from low grazing angles. However, it is a challenge to detect the targets in low grazing angle area due to severe multipath reflection effects. In this study, the authors propose orthogonal frequency division multiplexing (OFDM) chirp waveform for target detection in low grazing angle region. The OFDM chirp waveform has better peak‐to‐average‐power ratio level and provides larger time–bandwidth product than that of conventional OFDM waveform, which is beneficial for target detection. Two different radar schemes, namely, OFDM chirp radar and OFDM chirp multiple‐input multiple‐output (MIMO) radar, are studied under the Earth's curvature geometry model with the consideration of realistic multipath reflection effects. The generalised likelihood ratio test algorithm is developed by jointly exploiting the advantages of OFDM chirp waveform and MIMO configuration in increased degrees of freedom. In addition, an adaptive algorithm is further applied to enhance the target detection performance. All proposed schemes are verified by numerical results.

Millimeter-wave (mmWave) wireless communication system allows a compact antenna array with a large number of colocated elements, which enables flexible array design and beampattern synthesis. However, security is also particularly important but challenging for the fifth-generation (5G) wireless communications, as the broadcasting nature allows any receivers to have a copy of the transmitted signals. In this paper, we propose a hybrid multiple-input multiple-output (MIMO) phased-array time-modulated directional modulation (DM) scheme for physical layer security in mmWave wireless communications. The hybrid MIMO phased-array enjoys the advantages of MIMO in spatial diversity without sacrificing the main advantage of phased-array in coherent directional gain. The essence is to divide the transmit array into multiple subarrays. Each subarray can be used to form a directional beam, while all subarrays are jointly combined to work as an MIMO for higher angular resolution capabilities. More importantly, a time-modulated DM scheme is applied for the phased-MIMO to achieve physical layer security for mmWave wireless communications, even without the knowledge of the eavesdropper's position information. The feasibility of the proposed scheme is verified by numerical results.

Different from traditional phased-array, frequency diverse array (FDA) generates both angle and range dependent beampattern by the use of a small frequency offset across its array elements. Particularly, FDA range-dependent beampattern provides many new properties and potential applications. As array manifold curve plays an essential role in various array applications, this letter analyzes the generalized linear FDA manifold curve characteristics including arc length and its rate-of-change. It is proved that the arc length of FDA manifold curve is longer (implies better performance) than that of a phased-array for symmetric linear array configuration. Moreover, the derived Cramér-Rao lower bound also verifies that symmetric FDA outperforms phased array in direction-of-arrival estimation. All theoretical analyses are validated by numerical results.

Transmit beamforming and artificial noise-based methods have been widely employed to achieve physical-layer (PHY) security. However, these approaches may fail to provide satisfactory secure performance if the channels of legitimate user (LU) and eavesdropper (Eve) are highly correlated, which usually occurs in the case of close-located LU and Eve. The goal of this paper is to address the PHY security problem for proximal LU and Eve in millimeter-wave transmissions. To this end, we propose a novel frequency diverse array (FDA) beamforming approach, which intentionally introduces some frequency offsets across array antennas to decouple the highly correlated channels of LU and Eve. By exploiting this decoupling capability, the FDA beamforming can degrade Eve's reception and thus enhance PHY security. Leveraging FDA beamforming, we aim to maximize the secrecy rate by jointly optimizing the frequency offsets and the transmit beamformer. This secrecy rate maximization problem is difficult due to the tightly coupled variables. However, we show that it can be reformulated into a form only depending on the frequency offsets. Building upon this reformulation, we further employ the block successive upper-bound minimization method to iteratively obtain a solution with stationary convergence guarantee. Numerical results demonstrate that FDA beamforming can provide higher secrecy rate than conventional beamforming, especially for proximal LU and Eve.

Detecting the sinusoidal order is a prerequisite step for parametric multidimensional sinusoidal frequency estimation methods, whose applications range from radar and wireless communications to nuclear magnetic resonance spectroscopy. Although the Bayesian information criterion (BIC) has been commonly applied for model order selection, its application to sinusoidal order estimation is recent. By means of estimation of Fisher information matrix, we extend the 1-D BIC to multidimensional case for multidimensional sinusoidal order selection. The multidimensional BIC is shown in simulations to outperform the state-of-the-art algorithms in terms of probability of correct detection.

Frequency diverse array (FDA) antenna using uniform frequency increments produces a S-shaped range-angle-dependent transmit beampattern, which can provide more degrees of freedom (DOFs) to suppress the interferences with different ranges. However, the S-shaped beampattern is coupled in the range-angle dimension and consequently results in inaccurate target localizations. In this letter, we proposed a multi-carrier FDA scheme to generate a dot-shaped transmit beampattern. To further improve the range-angle resolution capability, the sparse reconstruction respective of compressive sensing is also adopted. Theoretical analysis and simulation results demonstrate that the proposed approach produces a decoupled high-resolution dot-shaped beampattern. Moreover, both single-dot and multi-dot shaped beampatterns can be synthesized in this way.

Traditional interference suppression approaches will enjoy the additional benefits when angle as well as polarization domain information are involved by polarization sensitive array (PSA). However, the information from range domain and its collaboration with other domains are rarely explored. In this paper, we present the polarization sensitive frequency diverse array (PSFDA) which combines frequency diverse array (FDA) and PSA for robust angle-range-polarization beamforming. To improve the angle-range-polarization resolution, the sparse reconstruction respective of compressive sensing is further applied. Theoretical analysis and simulation results demonstrate that our proposed algorithm can provide the good range-polarization resolution not just the target direction.

Distributed passive radar imaging has been an emerging topic in radar imaging society because of its low-cost, increased-survivability and robustness. In the inverse problem of distributed passive imaging, location of receivers affects imaging quality a lot, while illuminators of opportunity remain to be uncontrollable. Therefore, we investigate the problem of receiver disposition optimization and propose the optimal scheme to locate those receivers by combining genetic algorithm(GA) with compressive sensing(CS) based imaging technique. Simulation results validate the effectiveness of the proposed algorithm.

In this paper,we propose a sparse Baysesian learning (SBL) based approach for the DOA estimation in the presence of coherent sources. First, the difference technique is used to enhance the input SNR. Then, we construct a virtual array manifold to eliminate the cross-term effect between each coherent group, after eigenvalue decomposition (EVD) of the difference covariance matrix, reduce the dimension of input data and regard each column as one snapshot of the virtual signal containing all bearing information of the coherent sources. Finally, the DOAs of the coherent sources are estimated via the SBL algorithm. Numerical results validate superior performance of the proposed approach.

One key problem in forward-looking synthetic aperture radar (SAR) imaging is that symmetrical targets along the flight path have the same Doppler history, this paper proposes a new forward-looking SAR imaging by the use of frequency diverse array (FDA) antenna. Different from conventional phased-array and multiple-input multiple-output (MIMO) antennas, which provide angle-dependent only transmit beam-pattern, the FDA offers range-angle dependent transmit beam-pattern and thus, the serious azimuth ambiguity in conventional forward-looking SAR imaging can be resolved by exploiting FDA range-angle dependent beam-pattern. Moreover, multicarrier transmission is also employed in the FDA radar to decouple the range and angle response of the targets. The effectiveness of the proposed methods are validated by simulation results.

Simultaneous synthetic aperture radar (SAR) imaging and ground moving target indication (GMTI) is of great importance in remote sensing applications, but it is difficult to be implemented for existing methods due to the cross-interferences between stationary targets/clutter and moving targets. Generally, the imaged moving targets may be displaced in azimuth according to their radial velocities and superimposed upon clutter at a wrong location. In this paper, we proposes a simple simultaneous SAR and GMTI approach by exploiting the fractional Fourier transform (FrFT) algorithm, different from existing methods that perform first stationary clutter suppression and thereafter handle GMTI via Doppler parameters estimation. The feasibility is verified by simulation results.

For circular trace scanning synthetic aperture radar (CTSSAR), the conventional approximated quadratic range equation assumption does not hold due to the impact of the curved flight path. Thus, existing SAR methods cannot focus the target accurately and efficiently in CTSSAR. To address this problem, the stationary phase can be regarded as an implicit function of the Doppler frequency. Therefore, the 2-D spectrum can be consequently achieved by employing the principle of stationary phase. In this letter, an imaging algorithm based on the derived spectrum is developed for CTSSAR. Promising results from simulation demonstrate the validity and effectiveness of the approach.

Denoising is a critical preprocessing step for hyperspectral image (HSI) classification and detection. Traditional methods usually convert high-dimensional HSI data to 2-D data and process them separately. Consequently, the inherent structured high-dimensional information in the original observations may be discarded. To overcome this disadvantage, this letter tackles an HSI denoising by jointly exploiting Tucker decomposition and principal component analysis (PCA). A truncated Tucker decomposition method based on noise power ratio (NPR) analysis and jointed with PCA is presented. We call this jointed method as NPR-Tucker+PCA. Experimental results show that the proposed method outperforms existing methods in the sense of peak signal-to-noise ratio performance.

Inspired by a cognitive radar in exploiting its environment to update current operation parameters and frequency diverse array (FDA) in offering a range-dependent beampattern due to the employment of frequency increments across the elements, this paper proposes a moving-target tracking approach to achieve cognitive radio frequency stealth using an FDA antenna for surveillance applications. Since surveillance systems are highly visible to intercept receivers, a traditional high-gain phased-array antenna beam is replaced by a series of low-gain FDA beams with spoiled frequency increments to reduce the system visibility, without degrading the surveillance system performance. Moreover, a cognitive closed-loop update scheme is presented to update the operation parameters in real time for improved moving-target tracking performance. All of the proposed methods are verified by simulation results.

Large time-bandwidth product waveform diversity design is a challenging topic in multiple-input multiple-output radar high-resolution imaging because existing methods usually can generate only two large time-bandwidth product waveforms. This paper proposes a new low peak-to-average ratio (PAR) orthogonal frequency division multiplexing chirp waveform diversity design through randomly subchirp modulation. This method can easily yield over two orthogonal large time-bandwidth product waveforms. More waveforms means that more degrees-of-freedom can be obtained for the system. The waveform performance is evaluated by the ambiguity function. It is shown that the designed waveform has the superiorities of a large time-bandwidth product which means high range resolution and low transmit power are allowed for the system, almost constant time-domain and frequency-domain modulus, low PAR and no range-Doppler coupling response in tracking moving targets.

Strong clutter reflections of terrain and marine surfaces obscure the contrast between the target-of-interest and clutter (terrain and marine surface reflections) in synthetic aperture radar (SAR) images and consequently hinder the efficiency of image interpretation and analysis. To overcome this problem, this letter proposes an efficient clutter suppression method in SAR images, which is named shedding irrelevant patterns (SIP). The essence is to construct a regression function that can suppress clutter and preserve the target patterns concurrently. We assume that the clutter is irrelevant to the target-of-interest and distinguishable in patterns in terms of image-pixel distribution and intensity (spatial information). Experimental results show the efficiency of the proposed method in both clutter suppression and target pattern preservation.

Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) has received much attention due to its interesting application potentials, but effective waveform diversity design is still a technical challenge. In a MIMO SAR, each antenna should transmit a unique waveform, orthogonal to the waveforms transmitted by other antennas. The waveforms should have a large time-bandwidth product, low cross-correlation interferences, and a low peak-average ratio. To reach these aims, this paper proposes an orthogonal frequency division multiplexing (OFDM) chirp waveform with random matrix modulation. The designed waveforms are time-delay and frequency-shift decorrelated. Referring to MIMO SAR high-resolution imaging, the proposed OFDM chirp waveform parameters are optimally designed, and their performances are analyzed through the ambiguity function and range-Doppler-based MIMO SAR imaging algorithm. Extensive and comparative simulation results show that the waveforms have the superiorities of high range resolution, constant time domain and almost constant frequency-domain modulus, large time-bandwidth product, low peak-average ratio, and low time-delay and frequency-shift correlation peaks. More importantly, this scheme can easily generate over three orthogonal waveforms with a large time-bandwidth product.

Large time-bandwidth product waveform diversity design is a challenging topic in multiple-input multiple-output radar high-resolution imaging because existing methods usually can generate only two large time-bandwidth product waveforms. This paper proposes a new low peak-to-average ratio (PAR) orthogonal frequency division multiplexing chirp waveform diversity design through randomly subchirp modulation. This method can easily yield over two orthogonal large time-bandwidth product waveforms. More waveforms means that more degrees-of-freedom can be obtained for the system. The waveform performance is evaluated by the ambiguity function. It is shown that the designed waveform has the superiorities of a large time-bandwidth product which means high range resolution and low transmit power are allowed for the system, almost constant time-domain and frequency-domain modulus, low PAR and no range-Doppler coupling response in tracking moving targets.

Waveform diversity design is a vital issue in multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) systems because the waveforms should have good orthogonality, high coherence, and large time-bandwidth product. However, most of the existing waveforms are not suitable for MIMO SAR and difficult to be implemented in a real-life scene. This letter investigates a scheme for designing chirp modulation diversity waveforms with large time-bandwidth product, constant modulus, implementation simplicity, good Doppler tolerance, and orthogonality. The four waveforms are orthogonal on both transmit and receive. The waveform performances are investigated through the correlation and ambiguity functions. Numerical results validate the superiorities of the designed four waveforms in MIMO SAR high-resolution imaging.

Along-track interferometry (ATI) and displaced phase center antenna (DPCA) are two representative synthetic aperture radar (SAR) ground moving target indication (GMTI) techniques. However, the former is a clutter-limited detector, whereas the latter is a noise-limited detector. In this letter, we propose a two-antenna SAR with waveform diversity for GMTI. The two antennas placed in the azimuth dimension simultaneously transmit two orthogonal waveforms, namely, the up- and down-chirp waveforms. In doing so, four independent transmit-receive channels are obtained for the receiver, and thus, an efficient GMTI is developed by cooperatively utilizing the ATI GMTI and DPCA GMTI processing techniques. This method first uses the DPCA technique to cancel clutter and then the ATI technique to suppress noise. Next, the fractional Fourier transform is employed to estimate the Doppler parameters and corresponding reference filters are designed to focus the moving targets. The proposed approach cooperatively utilizes the advantages of ATI and DPCA GMTI techniques and overcomes their disadvantages. It does not prerequire knowledge of the target's across-track velocity. The effectiveness is verified by simulation results.

Range-ambiguity suppression is a technical challenge for high-pulse-repetition-frequency (PRF) synthetic aperture radar (SAR). This letter proposes a practical approach to mitigate the range ambiguities in high-PRF SAR by using the orthogonal frequency-division multiplexing (OFDM) waveform diversity. The system scheme, waveform design, and range-ambiguity-to-signal-ratio performance are detailed. The approach eliminates the ambiguities, instead of just suppressing them like other techniques. The proposed OFDM chirp diverse waveform has a large time–bandwidth product. It is validated by computer-simulation results. Although OFDM radar has received much attention in recent years, there appears to be little work done in applying OFDM concepts to mitigate high-PRF radar range ambiguities, as is the subject of this letter.

Multiple-input and multiple-output (MIMO) synthetic aperture radar (SAR) should use the waveforms that have a big product of time-width and frequency-width and a constant modulus. In this case, the Barker code-based waveforms are not suitable. In this paper, a kind of orthogonal frequency diversion multiplexing (OFDM) chirp diverse waveform is presented for MIMO SAR imaging. The waveform performance is evaluated by the radar ambiguity function. Numerical results show that the waveform has the superiorities of high range resolution, constant modulus, large time-banwidth product, implementation simplicity, and low ambiguity function sidelobes.

Near-space is recognized as the atmospheric region from 20 to 100 km above the Earth's surface. Near-space vehicles offer several advantages compared to low earth orbit satellites and airplanes because near-space vehicles are not constrained by orbital mechanics and fuel consumption. These advantages provide potential for future remote sensing applications, but little related work has been published. This paper explains what near-space is and how it should be exploited for remote sensing applications. A near-space vehicle-borne synthetic aperture radar (SAR) with reflector antenna and digital beamforming on receive is proposed for high-resolution and wide-swath (HRWS) remote sensing. The system configuration, signal model, imaging scheme, system performance, and nadir echo suppression are investigated. An example system is conceptually designed, along with its system performance analysis. It is shown that the near-space vehicle-borne SAR with reflector antenna can operate with high flexibility and reconfigurability, thus enabling a satisfactory HRWS remote sensing performance.

Ground moving target indication (GMTI) is of great important for surveillance and reconnaissance. The representative GMTI technique of along-track interferometry (ATI) synthetic aperture radar (SAR) does not take into account the fact that the stationary clutter unavoidably corrupts the interferometric phase of the targets. In this paper, we aim at multiple-input and multiple-output (MIMO) SAR-based solution. We proposed a scheme of MIMO SAR which is different from normal MIMO radar for ground moving targets detection and imaging. This approach employs MIMO along-track antenna configuration and waveform diversity. The system schemes, signal models, and waveform diversity are investigated.

Multiple-input and multiple-output (MIMO) radar has received renewed attention in recent years, but little work on MIMO synthetic aperture radar (SAR) remote sensing has been reported. This paper presents a scheme of space-time coding MIMO orthogonal frequency division multiplexing (OFDM) SAR for high-resolution imaging. This system employs MIMO configuration in the elevation direction and the Alamouti space-time coding scheme in the azimuth direction, along with the use of OFDM waveform diversity and displaced phase center antenna (DPCA) techniques. As an orthogonal transmission waveform is required for this novel space-time coding MIMO-OFDM SAR system, one kind of OFDM linearly frequency modulated waveforms is investigated. The matched filtering and multibeam forming in the elevation direction are detailed. Additionally, the corresponding mathematical relations and signal models for high-resolution imaging are formed. In this way, efficient spatial diversity gain and improved range resolution are obtained by the MIMO configuration, and the requirement of pulse repeated frequency for wide-swath remote sensing is reduced by the DPCA technique. The feasibility is validated by numerical simulation results.

To integrate a synthetic aperture radar (SAR) into an operational unmanned airborne vehicle (UAV), it should be as small as possible to meet stringent limitations of size, weight, and power consumption. It appears that the novel combination of millimeter-wave frequency-modulated continuous-wave (FMCW) technology and SAR techniques can provide an optimal solution. However, some efficient techniques should be applied to resolve range/Doppler ambiguities in FMCW UAV SAR systems. As such, a technique of waveform-diversity-based millimeter-wave UAV SAR imaging is presented in this paper. Along with the described system concept and signal model, the performance of the diversified waveforms evaluated by their cross correlations is detailed. As the conventional stop-and-go approximation is not valid for FMCW SAR, a modified wavenumber-domain algorithm with a consideration of continuous antenna motion during transmission and reception is derived. This imaging algorithm is validated with computer simulations. Furthermore, one parallel direct-digital-synthesizer-driven phase-locked-loop synthesizer with adaptive nonlinearity compensation, which has been validated by the experimental results, is proposed to obtain a millimeter-wave FMCW signal with fine frequency linearity.

Bi- and multistatic synthetic aperture radars (SARs) can achieve reduced vulnerability in military systems, especially in directional responsive jamming, and avoid physical attack to radar platforms. In this letter, a technique for jamming bi- and multistatic SAR systems is proposed, which is implemented with a novel transponder. This technique is based on a delayed retransmission of the received radar signal toward the scene. Unlike conventional delayed jamming, here, the jammer's delay time is random in the whole range interval. Moreover, the jamming signal is not retransmitted to the receiver directly but to the areas with targets that need to be protected against being imaged by radar. By this, it is then possible to jam the radar echoes independent of the receiver location