Huagui Du

Inverse synthetic aperture radar (ISAR) imaging technique has shown significant potential in civilian and military fields. However, radar systems encounter serious obstacles in acquiring long or full apertures. ISAR imaging targets exhibit sparsity in the spatial domain, which is beneficial for sparse recovery (SR) methods to achieve aperture reconstruction. This paper proposes the fast frequency-selective (FS) gridless sparse ISAR imaging methods to achieve high-precision aperture reconstruction, which fully considers the advantages of gridless techniques, structured model and prior knowledge. Usually, the FS atomic norm minimization (ANM) (FSANM) encodes the prior knowledge as a additional constraint into semi-positive definite programming (SDP), resulting in high computational complexity. The proposed method assumes that the selected frequency range follows the prior probability distribution, which is used to construct a weighting function for the atoms. Meanwhile, vandermonde decomposition and linear interpolation are employed to enhance the selection probability of adjacent atom. Then, a non-conveax metric has been implemented to enhance atomic sparsity. Although the alternating direction method of multipliers (ADMM) typically provides a more efficient solution for semidefinite programming problems, the standard eigen decomposition still faces problem of high computational complexity. Finally, the Lanczos decomposition algorithm is employed to achieve faster eigen-decomposition, thereby enhancing the computational efficiency of ISAR imaging. The experimental results of the simulated and measured data show that compared with the FSANM method, the proposed method has higher computational efficiency and better imaging performance under lower signal-to-noise ratio and data rate.

Translational motion compensation for geosynchronous orbit (GEO) targets represents a crucial yet challenging task in inverse synthetic aperture radar (ISAR) imaging. The maneuvering tendencies of GEO targets introduce nonlinear distortions to the range profile, posing significant difficulties for precise translation compensation. Additionally, the vast distance between the radar and GEO targets results in an extremely low signal-to-noise ratio (SNR) of the echo signals, thereby undermining the coherence between consecutive pulses. In light of these challenges, this paper introduces a robust method for translational motion compensation tailored to GEO targets. This approach leverages genetic algorithm improved particle swarm optimization (GAPSO) merged with the generalized Radon-Fourier transform (GRFT). Initially, we delve into the intricacies of GEO target behavior to pinpoint an optimal imaging interval. Subsequently, translational motion is modeled using a high-order polynomial and GRFT is utilized to estimate the associated parameters. Furthermore, GAPSO is employed to streamline the parameter search process, thereby reducing computational demands. The proposed method can not only achieve effective motion compensation under ultra-low SNR conditions but also maintain high computational efficiency. Both simulated results and electromagnetic calculation data results affirm the efficiency and robustness of our proposed method.

Frequency synchronization error, as one of the inevitable technical challenges in distributed synthetic aperture radar (SAR), has different impacts on different SAR systems. Multi-monostatic SAR is a typical distributed configuration where frequency synchronization errors are tiny in distributed airborne and low earth orbit (LEO) SAR systems. However, due to the long time delay and long synthetic aperture time, the imaging performance of a multi-monostatic geosynchronous (GEO) SAR system is affected by frequency oscillator errors. In this paper, to investigate the frequency synchronization problem in this configuration, we firstly model the echo signals with the frequency synchronization errors, which can be divided into fixed frequency errors and random phase noise. Secondly, we talk about the impacts of the two kinds of errors on imaging performance. To solve the problem, we thirdly propose an autofocus back-projection (ABP) algorithm, which adopts the coordinate descent method and iteratively adjusts the phase error estimation until the image reaches its maximum sharpness. Based on the characteristics of the frequency synchronization errors, we further propose the Node ABP (NABP) algorithm, which greatly reduces the amount of storage and computation compared to the ABP algorithm. Finally, simulations are carried out to validate the effectiveness of the ABP and NABP algorithms.

Refocusing moving targets in synthetic aperture radar (SAR) poses inherent challenges. The difficulty is amplified when SAR raw data are missing in the azimuth direction, mainly because of the unknown motion parameters of non-cooperative targets. Estimating these parameters from SAR azimuth missing data (SAR-AMD) is notably more challenging than from complete echoes. To address this problem, we propose the MPE-CM method, a fast and robust motion parameter estimation method for SAR-AMD based on contrast maximization. Initially, following the range walk correction (RWC) by Keystone transform (KT), a coarse-focused image is derived in the range-Doppler (RD) domain by constructing a phase compensation function with varying focusing factors. Subsequently, the estimation of motion parameters is converted into the estimation of focusing factors, which is accomplished through the maximization of contrast in the coarse-focused image. Concurrently, we propose a Five-Point method to efficiently and robustly determine the focusing factor. Finally, the along-range and azimuth velocities can be retrieved from the estimated focusing factor and Doppler shift, where the Doppler shift is obtained by identifying the peak energy shift of the coarse-focused image. The proposed MPE-CM method, ensures computational efficiency through a fast and robust approach, while coherent processing improves its anti-noise performance. The experimental results demonstrate the effectiveness of the proposed MPE-CM method in SAR-AMD.

In this article, an efficient method for parameter estimation of polynomial phase signal (PPS) is proposed. Instead of the existing methods based on parametric ergodic search or phase differentiation, the proposed method adaptively tracks the PPS phase through extended Kalman filtering (EKF), termed as APT-EKF. First, based on the smoothness assumption of the local phase, a state-space model describing the PPS phase is constructed. Then, by solving the state-space model through EKF, the PPS phase can be tracked. Finally, the least square estimation (LSE) is performed for the inversion of PPS coefficients, and the O’Shea refinement strategy is implemented to enhance the estimation accuracy, thereby achieving the Cramér–Rao lower bound (CRLB). Compared with most existing studies, the proposed method occupies an obvious advantage in signal-to-noise ratio (SNR) threshold and computational efficiency. It is suitable for the arbitrary order PPS. Moreover, this article provides a comprehensive analysis of the parameter initialization, performance bounds, and computational complexity of the proposed method. Both simulation and experiment results are provided to demonstrate the effectiveness of the proposed method.

Ground moving targets with complex motions often appear seriously dislocated and smeared in synthetic aperture radar (SAR) imagery due to non-cooperative motion. Refocusing such targets in SAR is challenging because of unknown motion parameters. In this paper, a novel SAR moving target imaging method based on adaptive phase tracking (MTIm-APT) is proposed. First, the Hough transform (HT) and the second(2nd)-order Keystone transform (SOKT) are performed to correct the range migration. Second, the Doppler phase can be adaptively tracked based on the improved extended Kalman filter (EKF). With the tracked Doppler phase, the motion parameters required for moving target imaging is estimated. Finally, the moving target is well-focused after motion parameters compensation since the high-order Doppler phase errors are efficiently eliminated. Unlike existing research that only considers the 2nd- or third(3rd)-order Doppler phase, the proposed method considers higher-order Doppler parameters which can be simultaneously estimated, thus eliminating error propagation effect. More importantly, due to the suboptimal filtering characteristics of EKF, the proposed method maintains excellent imaging performance at lower signal-to-noise ratio (SNR) than classical PGA. On the other hand, our method has a certain advantage in computational efficiency. Both simulated and real data processing results are provided to validate the feasibility and effectiveness of the proposed SAR MTIm-APT method.

Due to significant electromagnetic interference, radar interruptions, and other factors, Azimuth Missing Data (AMD) may occur in Synthetic Aperture Radar (SAR) echo, resulting in severe defocusing and even false targets. An important approach to solving this problem is to utilize Compressed Sensing (CS) methods on AMD echo to reconstruct complete echo, which can be abbreviated as the AMD Imaging Algorithm (AMDIA). However, the State-of-the-Art AMDIA (SOA-AMDIA) do not consider the influence of motion phase errors, resulting in an unacceptable estimation error of the complete echo reconstruction. Therefore, in order to enhance the practical applicability of AMDIA, this article proposes an improved AMDIA using Sparse Representation Autofocusing (SRA-AMDIA). The proposed SRA-AMDIA aims to accurately focus the imaging result, even in the Phase Error AMD (PE-AMD) echo case. Firstly, a Phase-Compensation Function (PCF) based on the phase history of the scene centroid is designed. When the PCF is multiplied with the PE-AMD echo in the range-frequency domain, a coarse-focused sparse representation signal can be obtained in the range-Doppler domain. However, due to the influence of unknown PE, the sparsity of this sparse representation signal is unsatisfying, breaking the sparse constraints requirement of the CS method. Therefore, we introduced a minimum entropy autofocusing algorithm to autofocus this sparse representation signal. Next, the estimated PE is compensated for this sparse representation signal, and a more sparse representation signal is obtained. Hence, the non-PE complete echo can be reconstructed. Finally, the estimated complete echo can be used with classic imaging algorithms to obtain high-resolution imaging results under the PE-AMD condition. Simulation and real measured data have verified the effectiveness of the proposed SRA-AMDIA.