Robust perception for safety-critical Internet of Things (IoT) applications in dense jamming environments demands advanced radar sensing capabilities with enhanced interference mitigation and array-beampattern optimization. When the target direction overlaps with the jamming region, both a narrower mainlobe and a deeper sidelobe are desirable but hard to meet simultaneously since they both consume array degrees of freedom. This paper investigates the application of Movable Antenna Array (MAA) in aiding radar beampattern synthesis under dense jamming scenarios. Using the extra spatial degrees of freedom provided by MAA, this work jointly optimizes both weighting vectors and antenna element positions to achieve superior beampattern without adding array element number. The integrated sidelobe level is chosen as the optimization objective with practical constraints, which leads to a highly non-convex optimization. To address this, we propose an Alternating Optimization-based Sequential Approximation (AOSA) algorithm. In each iteration, the weighting vector subproblem is solved through convex approximations of the primary nonconvex constraints, while the antenna position vector subproblem is tackled indirectly with a specifically derived proposition. Simulation results verify that the joint optimization framework effectively improves target separability and jamming rejection in complex electromagnetic environments, demonstrating its promising potential for advancing radar detection capabilities.

For high-resolution squinted airborne synthetic aperture radar (SAR) imaging, both linear range walk correction (LRWC) and motion error introduce significant azimuth spatial-variant (ASV) characteristics in the radar echo, rendering the classical assumption of "azimuth translational invariance" no longer valid. Existing sub-aperture methods attempt to overcome the ASV characteristics of the signal by performing segmentation processing in the data domain or the image domain. However, grating lobes or image stitching problems inevitably occur in the focused images. Existing full-aperture methods, on the other hand, utilize azimuth resampling or nonlinear chirp scaling (NCS) to address the ASV problem. Nevertheless, the above-mentioned methods basically handle the ASV characteristics introduced by LRWC and motion errors separately, without considering the coupling characteristics between the two. Therefore, this paper proposes a high phase-preservation squint airborne SAR autofocus imaging method by modifying the traditional azimuth resampling processing, so that only a single azimuth resampling factor is required to simultaneously solve the ASV problems brought about by LRWC and motion errors. The imaging processing results of airborne squint SAR real-data verify its good focusing effect. Meanwhile, the interferometric processing results of multi-pass cross-track SAR real-data also indicate that the proposed algorithm exhibits a high phase-preservation capacity. The images processed by the proposed algorithm and the comparison algorithms, as well as the multi-pass cross-track SAR complex images after registration, can be downloaded from https://pan.baidu.com/s/1okgAkp18ynK7qzKXe2lceQ?pwd=nquf.

As a novel configuration of along-track Multistatic SAR (Multi-SAR), the high frame rate Along-Track Swarm SAR (ATS-SAR) has garnered significant attention in recent years due to its exceptional efficiency in reducing data acquisition time. Motivated by its potential for high-resolution imaging of moving targets, this paper investigates the application of ATS-SAR in moving target imaging. However, high frame rate ATS-SAR-based moving target imaging confronts two critical challenges: time-space coupling and partial data loss in moving target echoes. To address these challenges, we first conduct a comprehensive analysis and theoretical derivation of the moving target echo model under the high frame rate ATS-SAR configuration. Subsequently, we propose an innovative motion parameter estimation algorithm that exploits unique echo characteristics to achieve high-performance imaging. Furthermore, we introduce the highresolution, high frame rate ATS-SAR Sub-Aperture Collaborative Imaging algorithm for Moving Targets (MT-SACIm-ATS). Extensive simulations and a real measured experiment validate the effectiveness of the MT-SACIm-ATS algorithm, demonstrating imaging performance that closely approximates reference imaging results. Comparative analysis with several state-of-the-art algorithms further highlights the superiority of the proposed approach in terms of resolution and robustness.

Due to its rapid data acquisition capabilities, the Along-Track Swarm SAR (ATS-SAR) system offers a distinct advantage in simplifying the motion states of moving target. This paper addresses challenges encountered in ATS-SAR moving target imaging, specifically focusing on issues related to partial aperture data loss and the spatiotemporal non-equivalence of the ATS-SAR moving target echo. A novel ATS-SAR imaging method for moving target is introduced. Initially, the ATS-SAR moving target echo model is established. Subsequently, the proposed algorithm designs a phase compensation function that contributes to the accurate reconstruction of the complete echo for ATS-SAR moving target, resulting in outstanding imaging performance.

The motion error of airborne microwave photonic synthetic aperture radar (SAR) has 2-D spatial variation characteristics, and the range spatial variant motion error (RVE) and azimuth spatial variant motion error (AVE) significantly interplay during the motion error estimation. For the RVE estimation, the standard weighted least square (WLS) algorithms are susceptible to the AVE and the moving targets. In addition, the azimuth subimage-based WLS algorithms face the problem of dominant points decreasing dramatically. This letter proposes a WLS estimation kernel based on nonlinear chirp scaling (NCS) to address the issues above. The AVE is first significantly corrected by the NCS processing, and RVE is subsequently estimated using the standard WLS kernel. In addition to eliminating the adverse effects of AVE and moving targets, the proposed method can retain sufficient dominant points to ensure the accuracy of phase gradient autofocus (PGA). The measured data processing results verify the effectiveness of the proposed method.

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.

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.

In order to solve the problem of SAR imaging with azimuth missing data, a novel missing data SAR imaging algorithm is proposed in this paper. In the algorithm, the complete echo can be recovered at the two-dimensional frequency-domain by using the generalized orthogonal matching pursuit (GOMP) algorithm. The simulation result verifies the effectiveness of the proposed algorithm. Since the proposed algorithm only needs to recover the echo corresponding to sparse target-located range gates, compared with the state-of-the-art SAR imaging algorithm with azimuth missing data, it shows the advantage in the computational complexity when the targets are sparse enough in the range direction and it has a better imaging performance than the state-of-the-art azimuth missing data algorithm in noiseless and noisy settings.

The virtual sensing technique allows the active noise control (ANC) system to work with error microphones that are placed far from the desired zone of quietness (ZoQ). Conventionally, a training stage is required to obtain the auxiliary filters with the temporary error microphones placed in the ZoQ. When the characteristics of the primary noise changes, the auxiliary filters have to be retrained. As a result, the conventional virtual sensing technique can only be used when the frequency band of the primary noise remains unchanged. In order to solve this limitation, this paper proposes a selective virtual sensing technique for the multi-channel feedforward ANC system. The selective virtual sensing technique obtains a bank of auxiliary filters in the subband structure. Based on the frequency-band-matching mechanism, a linear combination of the auxiliary filters is calculated and used in the real-time control stage. Experimental results show that the selective virtual sensing technique achieves better noise reduction performance than the conventional virtual sensing technique when the frequency band of the primary noise fluctuates.