Zhong Liu

Synthetic Aperture Radar (SAR) imaging relies on using focusing algorithms to transform raw measurement data into radar images. These algorithms require knowledge of SAR system parameters, such as wavelength, center slant range, fast time sampling rate, pulse repetition interval, waveform, and platform speed. However, in non-cooperative scenarios or when metadata is corrupted, these parameters are unavailable, rendering traditional algorithms ineffective. To address this challenge, this paper presents a novel parameter-free method for recovering SAR images from raw data without the requirement of any SAR system parameters. Firstly, we introduce an approximated matched filtering model that leverages the shift-invariance properties of SAR echoes, enabling image formation via convolving the raw data with an unknown reference echo. Secondly, we develop a Principal Component Maximization (PCM) method that exploits the low-dimensional structure of SAR signals to estimate the reference echo. The PCM method employs a three-stage procedure: 1) segment raw data into blocks, 2) normalize the energy of each block, and 3) maximize the principal component's energy across all blocks, enabling robust estimation of the reference echo under non-stationary clutter. Experimental results on various SAR datasets demonstrate that our method can effectively recover SAR images from raw data without any system parameters. To facilitate reproducibility, the matlab program is available at https://github.com/huizhangyang/pcm.

To improve the multi-target detection ability of an active array radar in interference environments, we deploy an reconfigurable intelligent surface (RIS) near the active array to assist the radar transmitter. By considering the minimum signal-to-interference-plus-noise ratio (SINR) among all targets as the metric, we jointly optimize the RIS reflection coefficients as well as the radar’s transmit and receive beamformers under the constant modulus constraints on the RIS reflection coefficients and phase-only transmit beamformer. To tackle the resultant problem, we decompose it into three sub-problems under the alternating optimization framework, and separately address these sub-problems through generalized Dinkelbach transform, majorization-minimization, and minimum variance distortionless response methods. Numerical results demonstrate that the assistance of RIS effectively enhances the illumination power of radar transmitter towards all targets, thereby improving their SINRs.

This letter investigates the wide-beam power gain pattern synthesis (PGPS) with accurate sidelobe control of phase-only linear arrays by maximizing the minimum power gain of mainlobe region. The resultant problem is greatly intractable due to plentiful nonconvex fractional constraints and unimodular constraints. To address this issue, we convert each fractional constraint into a convex one via iterative convex enhancement and tackle the unimodular constraints by leveraging the penalty convex-concave procedure method. The original problem is then translated into a series of constrained convex subproblems which are easy to solve. Numerical results illustrate that the proposed method has superior performance of mainlobe power gain to typical shaped beam pattern synthesis (SBPS) methods, and the power gain loss yielded by the phase-only transmitting seems acceptable compared to the corresponding digital array.

In this work, we investigate the gridless parameter estimation of pulse–Doppler radar targets using a reduced number of samples under a limited bit budget. We propose a hybrid analog and digital (HAD) acquisition system integrating a tunable analog component, low-resolution quantizers, and a digital filter. Under the framework of task-based quantization, the HAD architecture is designed to optimize target parameter estimation within the constraints of the bit budget. Specifically, a small subset of the received signal samples is observed and the low-rank parameter matrix is recovered using matrix completion techniques. The atomic norm minimization method is applied to reconstruct the complete parameter matrix, enabling gridless estimation of the parameters. Numerical experiments are conducted to validate the effectiveness of the proposed receiver in gridless parameter estimation.

Synthetic aperture radar (SAR) can produce well-focused images based on accurate observation models. However, motion errors in the data acquisition process often introduce inaccuracies in the models and degrade the image quality. Classical motion compensation (MOCO) methods can mitigate this problem, but they are not applicable to compressed sensing (CS) SAR imaging. Existing CS SAR imaging methods can jointly estimate and compensate the motion error from the data by iterative optimization, but they incur a high computational cost. To solve these problems, in this paper we propose an efficient data-driven MOCO strategy for CS SAR imaging. Specifically, we develop a 2-step measurement estimation scheme followed by a fitting and filtering procedure to extract the motion error from the data. Then we use the estimated motion error to correct the CS SAR observation model and reformulate a sparse SAR reconstruction problem based on the corrected model. This strategy significantly reduces the computational cost compared to existing CS SAR MOCO methods. To further expedite the image recovery, we design a fast imaging algorithm that exploits the feature of the observation matrix to accelerate the matrix-vector products and the interpolation operations involved in the recovery problem. Experimental results show that the proposed method can efficiently reconstruct SAR images from CS SAR data with motion errors and offer favorable imaging performance.

This paper proposes a novel eigenvalue-based detector, called Lambda-1 detector, for adaptive and robust interference detection in single-look-complex synthetic aperture radar (SAR) images. The proposed method leverages the increased eigenvalues caused by interference in SAR image blocks, where the interference is expected to have a small set of eigenvalues, particularly with a dominating one. Specifically, the method segments the image into multiple blocks, computes the eigenvalues of each block’s covariance matrix, and compares the largest eigenvalue λ₁ with a threshold to determine the presence of interference under the criteria of constant false alarm rate, thereby enabling adaptive interference detection against varying levels of interference-to-signal ratios. The largest eigenvalue is characterized by the order-2 Tracy-Widom distribution (no closed-form expression) under the assumption of image’s homogeneity, and the threshold is adaptively determined based on a scaled and shifted Gamma distribution that fits this distribution with a closed-form expression. The method is robust by first modeling and then correcting the impacts of upsampling and windowing of SAR image data on the fitted distribution’s parameters, and by incorporating outlier removal preprocessing. Experimental results validate the effectiveness of the proposed method in successfully detecting both strong and weak interference in various SAR images, including Sentinel-1 and Gaofen-3. The detection performance is quantitatively evaluated using false alarm rate P_fa and detection rate P_d. In summary, the proposed Lambda-1 detector effectively identifies interference artifacts in focused SAR images and holds promise for improving the quality of SAR imagery by incorporating adaptive interference removal.

Limited by the number of antenna elements, conventional array radars usually have insufficient ability of signal-dependent interference suppression in harsh environments, even though transmit and receive beamformers are jointly designed. This article deploys an active reconfigurable intelligent surface (RIS) to assist the receive array, which provides numerous extra degrees-of-freedom to suppress interferences and to enhance the beamforming gain of target echo simultaneously since the active RIS has the capability of adjusting and amplifying incident signals. Aiming to maximize the output signal-to-interference-plus-noise ratio (SINR), we jointly design the phase-only or power-limited transmit beamformer, receive beamformer, and active RIS reflection coefficients. We devise an alternating optimization-based algorithm to handle the resultant nonconvex-constrained fractional programming problem. Specifically, the phase-only transmit beamformer is determined by the Riemannian gradient descent (RGD)-based method while the power-limited one is given as a closed-form optimal solution, and the active RIS reflection coefficients are updated by the concave-convex procedure (CCCP)-based method. Moreover, we derive the convergence condition of the proposed algorithm based on the properties of RGD and CCCP. Numerical results reveal that the proposed active RIS-aided array radar significantly outperforms the passive RIS-aided and RIS-free ones in terms of output SINR.

In conventional colocated multiple-input multiple-output (MIMO) radars, practical waveform constraints including peak-to-average power ratio, constant or bounded modulus lead to a significant performance reduction of transmit beampattern, especially when the element number is limited. This paper adopts an active reconfigurable intelligent surface (ARIS) to assist the transmit array and discusses the corresponding beampattern synthesis. We aim to minimize the integrated sidelobe-to-mainlobe ratio (ISMR) of beampattern by the codesign of waveform and ARIS reflection coefficients. The resultant problem is nonconvex constrained fractional programming whose objective function and plenty of constraints are variable-coupled. We first convert the fractional objective function into an integral form via Dinkelbach transform, and then alternately optimize the waveform and ARIS reflection coefficients. Three types of waveforms are unifiedly optimized by a consensus alternating direction method of multipliers (CADMM)-based algorithm wherein the global optimal solutions of all subproblems are obtained, while the ARIS reflection coefficients are updated by a concave-convex procedure (CCCP)-based algorithm. The convergence is also analyzed based on the properties of CADMM and CCCP. Numerical results show that ARIS-aided MIMO radars have superior performance than conventional ones due to significant reduction of sidelobe energy.