By unfolding traditional optimization algorithms into the form of neural networks, the unfolding network methods have attracted more and more attention in sparse inverse synthetic aperture radar (ISAR) imaging because of their high reconstruction performance and good interpretability. However, existing unfolding network methods mainly focus on 1-D sparse ISAR imaging and cannot be directly applied to 2-D sparse ISAR data. For this reason, a novel learned 2D-two-step iterative shrinkage/thresholding algorithm (L-2D-TwISTA) is proposed for high-efficiency and high-accuracy 2-D sparse ISAR imaging. Specifically, each stage of L-2D-TwISTA corresponds to an iterative solution step of the developed 2D-TwISTA approach. Moreover, a complex-valued (CV) residual network is designed in L-2D-TwISTA to improve training efficiency and solve the nonlinear problem of proximal mapping of the 2D-TwISTA more effectively. The experimental results of real-measured data confirm that the L-2D-TwISTA can realize high-performance 2-D sparse ISAR imaging.

The high speed and maneuverability of the spacecraft make the traditional wideband tracking algorithms fail to achieve echo coherent recovery under low SNR, resulting in reduced tracking accuracy. This paper proposes a fully coherent accurate measurement and tracking method based on the multi-channel hypothesis to realize the fully coherent recovery of dechirp echo and Doppler phase disambiguation. The algorithm performs initialization estimation and designs the multi-channel hypothesis velocity ambiguity interval. Then, it traverses the interval to perform the fully coherent accurate measurement and tracking to obtain the motion state update, employed to echo coherent recovery. Finally, the N/M criterion is introduced to evaluate the envelope alignment of the recovered echoes and filter out the unambiguous velocity. Experiments demonstrate its remarkable performance in low SNR conditions.

The spatiotemporal prediction of on-orbit satellites is crucial for intention understanding and ensuring the successful completion of missions. Current spatiotemporal prediction methods primarily use convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to process sequential observation images to predict future states. However, these methods often result in poor prediction performance due to the network’s inherent limited ability to express complex details. In this work, a residual spatiotemporal diffusion (RSTD) model is proposed to learn the spatial and temporal characteristics of targets from spaceborne imaging sequences. Leveraging a historical database, the model utilizes the patterns of image feature changes to assist in predicting target shape variations during the next observation period. A spatiotemporal perception module, capable of capturing long-term dependencies, is incorporated into the denoising process, thereby endowing it with forecasting capabilities. Furthermore, by incorporating a residual dual-stream structure, the model separates the prediction of the target’s overall shape and dynamic changes, thus overcoming the issue of overly smooth predicted images. Comprehensive experiments demonstrate that the proposed method achieves a peak signal-to-noise ratio (PSNR) of about 35 dB during uniform and uniformly variable motion. It also outperforms existing methods in subsequent feature extraction and attitude estimation, supporting the spatiotemporal attitude prediction of on-orbit satellites.

Measuring target angles is a crucial issue in airborne radar detection. Traditional dynamic programming track-before-detect (DP-TBD) often operates in low signal-to-noise ratio (SNR), posing challenges to angle measurement and localization. This letter presents an angle estimation technique for TBD processing. It interprets target angles across multiple frames using initial frame angle (IF-AG) and inter-frame angular rate of change (IF-ARC). Compensating for the latter and performing noncoherent accumulation to improve SNR before estimating the IF-AG enhance multiframe target angle estimation precision. The method initially formulates the cost function for multiframe angle measurement based on the raw data extracted from the multiframe array by DP-TBD. It subsequently estimates and compensates for IF-ARC, related to velocity, followed by IF-AG estimation, providing target angles for all frames concurrently. Simulation results validate the effectiveness of this method.

Radio frequency interference (RFI) has become a serious problem for synthetic aperture radar (SAR) systems. In recent years, numerous methods have emerged for the suppression of radio frequency interference (RFI). These methods often rely on assumptions regarding the narrowband characteristics and low-rank nature of radar echoes or interference signals. However, these assumptions may not hold true when dealing with complex RFI. To mitigate complex interference in dual-polarization (dual-pol) synthetic aperture radar (SAR) systems, a proposed method is the utilization of a filtering algorithm based on sidelobe cancellation (SLC) technology. This method leverages the discrepancies in gain and coherence between observed targets and RFI signals in the co-polarization (co-pol) and cross-polarization (cross-pol) channels. It suppresses interference through SLC filtering on a range line by range line basis in the focused image domain, serving as a post-processing step. The method is capable of tackling multiple complex interferences optimally under the least mean square criterion, such as narrowband, wideband, and noise-modulated pulsed interferences. Both simulation and real data experiments show that the method can remove RFI artifacts and effectively recover the desired SAR images.

The Video synthetic aperture radar (VideoSAR) technique is capable of providing high frame rate imaging. Through tracking the target shadow in a high resolution VideoSAR image sequence, moving target location and recognition would be achieved potentially. However, surrounded by complex clutter, moving targets’ shadows usually involve blurring, making shadow tracking difficult in real VideoSAR applications. To address clutter interference, this letter develops a shadow tracking framework based on the discriminate correlation filter (DCF) and the interacting multiple model (IMM) filter. Within this framework, the integration of DCF with the two-parameter constant false alarm rate (2P-CFAR) detector enables the assessment of the target’s appearance state for appropriate updating, mitigating the degradation of appearance templates. Moreover, a dynamic threshold is incorporated into 2P-CFAR, leveraging the historical information of the IMM motion model to establish a prior probability threshold, thereby reducing false alarms. Lastly, the integration of prior road information with the IMM model further enhances the reliability of the IMM prior motion model and indirectly adapts the threshold, effectively mitigating the impact of roadside clutters. Comparative experiments confirm that the proposal outperforms other rivals in tracking performance.

With the development of space resources, high-precision positioning is a significant way to obtain the on-orbit status information of space targets. Ground radar interferometric is an active space target positioning method, which can provide positioning information in all-weather and all-time. However, the directly measured interferometric phase will be ambiguous when the range difference between space targets and radar sites exceeds a certain boundary. In this letter, phase ambiguity resolution (AR) is established as a hidden Markov model (HMM) dynamic optimization problem in combination with the space target's movement process at different pulse times, in which no auxiliary baseline is required compared with traditional methods. And the Viterbi algorithm is used to solve the optimal ambiguous number (AN) sequence. Since the sequence decoding is mainly based on solving the optimal AN path that to explain the target position at different times best, a transmission technique of staggered carrier frequency signals is adopted, which significantly increases the robustness of phase AR. Extensive simulation experiments show that the proposal performs effectively and accurately for high-precision space target positioning.

Forward-looking multi-channel synthetic aperture radar (FLMC-SAR) is of the capability to achieve unambiguous 2-D images in the forward-looking slight direction. FLMC-SAR imagery usually suffers from relatively low spatial resolution as only limited Doppler diversity can be generated from the synthetic aperture. In this article, a sparsity-driven resolution enhancement algorithm is proposed to improve the resolution FLMC-SAR image of the forward-looking area. Different from conventional beamforming processing to resolve the FLMC-SAR left–right ambiguity, a Bayesian sparsity reconstruction optimization is developed for jointly ambiguity resolving and resolution enhancement in the azimuth angle image domain. The spatial structure of the target in the preliminary image domain is used as the signal sparsity with prior information to solve the constrained optimization problem for FLMC-SAR image resolution enhancement. A local least square estimator of the prior noise and signal statistics in the FLMC-SAR nonisotropic image is established in terms of determining the sparsity weight parameter. Extensive simulation and real FLMC-SAR data experiments confirm that the proposed algorithm is capable of achieving the unambiguous and resolution-enhanced FLMC-SAR image.

Formation targets have the characteristics of close distance, RCS difference and strong motion correlation. Radar should have enough resolution and anti-jamming ability to detect formation targets in the complex electromagnetic environment. The stepped frequency signal can be synthesized into a large bandwidth signal, so as to improve the range resolution and enhance the recognition performance of formation targets. In order to detect small targets next to large targets in formation targets, the signal needs to have low sidelobe characteristics. In this letter, we propose an optimized stepped frequency signal that can be synthesized into a large bandwidth with low autocorrelation sidelobes and a stopband to against narrowband interference. The proposed method can well detect small targets next to large target in formation, and has a certain narrowband anti-jamming ability. The simulation experiment results verify the advantages of the proposed waveform.

Traditional radar waveforms are easily intercepted and interfered with by enemy’s reconnaissance system with the time–frequency periodic pattern recognition. Frequency and pulse repetition frequency (PRF) agility is an effective approach to decrease interception probability and increase anti-jamming capabilities. On the other hand, the agility brings about high sidelobes and the difficulty of parameter estimation using the range-Doppler signal processing. In this article, an optimization and high-resolution imaging algorithm for sparse stepped linear frequency modulation waveform (SSLFMW) with frequency and PRF agility is developed. The range and Doppler 2-D autocorrelation function of the agile waveform is investigated to pave a way to find an optimization strategy for frequency and PRF to suppress range and Doppler sidelobes. Relied on the pulse trains of low Doppler sidelobes, we propose a method of cognitive transmitting and motion retrieval based on the maximum likelihood principle to eliminate the frequency and range coupling in velocity estimation. The 2-D sparse reconstruction with conjugate gradient solver is proposed to efficiently reconstruct the high-resolution range-Doppler image with the frequency and PRF agility waveform. Both simulated and real-measured data sets are used to verify the improved performance of the proposal.

Frequency agile waveform effectively decreases interception probability and increases the anti-jamming ability for radar probing in the electromagnetic countermeasure environment. However, the transmitting agile frequency introduces jitter phases causing de-coherence of echoes, which decreases signal-to-noise ratio (SNR) accumulation gain and leads to difficult motion estimation. Therefore, stable target detection, motion parameters estimation, and inverse synthetic aperture radar (ISAR) imaging for frequency-agile radar are still intractable problems in the low SNR environment. Aiming at these problems, we proposed a fully coherent processing method for weak target detection and ISAR imaging for frequency agile waveform in this paper. The agile waveform with low range and Doppler side lobes is designed to improve motion parameters estimation and ISAR imaging performance. Then, the joint jitter phases and motion parameters estimation based on the generalized likelihood ratio test (GLRT) is proposed to realize robust target detection and motion parameters estimation in low SNR conditions. The translational motion compensation and sparse ISAR imaging methods are also presented based on the detection and motion parameters estimation results. Finally, both simulated and real-measured data are used to verify the remarkable detection, motion parameters estimation, and ISAR imaging performance compared with the traditional non-coherent accumulation and mixture of coherent and non-coherent accumulation methods.

Accurate ranging and wideband tracking are treated as two independent and separate processes in traditional radar systems. As a result, limited by low data rate due to nonsequential processing, accurate ranging usually performs low efficiency in practical application. Similarly, without applying accurate ranging, the data after thresholding and clustering are used in wideband tracking, leading to a significant decrease in tracking accuracy. In this article, an integrated Kalman filter of accurate ranging and tracking is proposed using methods of phase-derived-ranging and Bayesian inference in wideband radar. Besides the motion state, in this integrated Kalman filter, the complex-valued high-resolution range profile (HRRP) is also introduced as a reference signal by coherent integration in a sliding window, which incorporates target's scattering distribution and phase characteristics. Corresponding kinetic equations are derived to predict the motion state and the reference signal in the next moment. A ranging process is constructed based on the received signal and the predicted reference signal in order to estimate innovation using methods of phase-derived-ranging and Bayesian inference, and a sequential update for motion state can be accomplished with the Kalman filter as well. In every recursion, the complex-valued reference signal is also updated by coherently integrating the latest pulses. The integrated Kalman filter takes full use of high range resolution and phase information, improving both efficiency and precision compared with conventional approaches of ranging and wideband tracking. Implemented in a sequential manner, the integrated Kalman filter can be applied in a real-time application, realizing simultaneous ranging with high precision and wideband tracking. Finally, simulated and real-measured experiments confirm the remarkable performance.

The stepped-frequency waveform is usually used to synthesize a wideband signal in the radar imaging system. To reduce the amount of data and coherent pulse intervals (CPIs), as well as to improve antijamming abilities, the sparse stepped frequency is employed in the area of inverse synthetic aperture radar (ISAR) imaging. Nevertheless, the traditional sparse stepped linear frequency modulation waveform (SSLFMW) has a shortage of high grating lobes caused by missing frequency bands, resulting in a degradation of the ISAR imaging quality. Many methods have been proposed to reduce the effect of grating lobes by echo signal processing. However, the method of grating lobe reduction is rarely studied from the aspect of waveform optimization. In this article, a novel SSLFMW with the low grating lobes in the ISAR imaging system is proposed. By deriving the autocorrelation function (ACF), the relation between grating lobes and waveform parameters, including stepped-frequency and phase-coded elements, is established. An optimization method based on alternate iteration is designed to optimize waveform parameters and reduce grating lobes. Based on this optimized SSLFMW, we establish an ISAR imaging framework with the compressive sensing (CS) theory. Finally, the experiments are designed to show that the optimized SSLFMW has lower grating lobes. Both simulated and real measured data are used to prove that the optimized waveform has a better performance in the high-resolution range profile (HRRP) synthesis and ISAR imaging compared with the traditional SSLFMW.

Artificially structured metamaterials apertures antennas (MAA) enables producing the pseudorandom and spatially variant radiation fields to encode spatial information and retrieve scene images using computational imaging (CI) algorithms. Combined with synthetic aperture radar (SAR) technologies, by moving a linear shape MAA in crosswise direction, a hybrid imaging system with the combination of MAA and SAR is demonstrated in this paper. Focusing on this peculiar imaging geometry, we propose a postprocessing algorithm which combines the classic omega-k algorithm and CI algorithms to achieve fully three dimensional scene images. Compared with traditional frequency-diverse imaging reconstruction algorithms, the postprocessing is more efficient could achieve as high efficiency as SAR algorithms do. Extensive imaging simulations are conducted to illustrate the effectiveness of the proposed algorithms.