Synthetic aperture radar (SAR) is an effective imaging and observation sensor that has been widely applied in both military and civilian fields. Deep learning approaches have gained prominence in SAR target recognition and received extensive attention. However, these methods often struggle when data is scarce, leading to insufficient training and challenges in effective feature extraction. To address this limitation, we propose a less data-dependent feature extraction framework. Specifically, we introduce the dual-tree complex wavelet transform (DTCWT) to capture multi-frequency feature details of SAR images, integrated with convolutional neural network. This approach enables effective extraction of high- and low-frequency information. By leveraging the characteristics of these frequency features, low-frequency subbands are used to emphasize the global structural features in the images, and high-frequency subbands are employed to identify the significance of different regions in the images. In response to the aforementioned characteristics, we introduced an attention mechanism to effectively incorporate high-frequency local information into low-frequency global information, thereby enhancing feature representation and recognition efficiency. Moreover, we propose an adaptive rotational convolution, and apply it to the high-frequency feature extraction. The adaptive rotational convolution can adapt to the directionally selective subbands with a single convolution kernel. Experiments conducted on the MSTAR and SAR car datasets demonstrate that the proposed method can achieve better recognition performance with fewer parameters, especially on small-scale datasets. The ablation study also confirms the effectiveness of the introduced DTCWT and rotational convolution.

The interrupted-sampling repeater jamming (ISRJ) countermeasure is researched based on waveform agility and mismatched filter design in this work. By analyzing the effects of transmitted waveform on ISRJ imaging characteristic, the principle of the anti-ISRJ method is proposed. The ISRJ range processing gain and energy can be suppressed by designing the waveform and mismatched filter. Meanwhile, the ISRJ azimuth processing gain can be further restrained by utilizing the waveform agility. The anti-ISRJ problem is formulated and is then divided into several subproblems. The analytical solutions of waveform and mismatched filter are derived based on majorization minimization (MM), and a fast algorithm is proposed by applying a squared iterative method (SQUAREM). The fast implementation of the algorithm is derived, and computational complexity is$\mathcal {O}({N\log N})$. Moreover, to improve the anti-ISRJ performance, the peak-to-average power ratio (PAR) constraint is introduced. Specifically, the waveform design subproblem under PAR constraint is solved, and thus, the PAR waveform can also be designed by the proposed algorithm. The simulation results verify that the proposed joint design algorithm shows significantly faster running speed than the conventional algorithms. The anti-ISRJ performance is superior to the existing methods, and the best anti-ISRJ performance can be obtained by applying the PAR waveform agility and mismatched filter design method.

Frequency agility increases the difficulty of jammers to predict and estimate the carrier frequency and, thus, improves the radar electronic counter-countermeasures (ECCM) performance. In this article, frequency agility is introduced into synthetic aperture radar (SAR). The characteristic of the Doppler history of frequency-agile SAR (FASAR) is analyzed, which shows that the azimuth compression could not be achieved by the classic imaging algorithms. In order to reconstruct the images of interested targets, a three-channel sparse imaging method is proposed based on approximated observation model by using the echo data of different carrier frequencies. By deriving the equivalent observation model, it is concluded that the reconstruction performance can be enhanced by improving the coherence of match filter imaging results in different channels. The image characteristic of different channels in FASAR is analyzed based on the chirp scaling imaging operator. A phase compensation method is then proposed to improve the coherence of images of interested targets in different channels. Specifically, the SAR mode of random frequency agility is proposed to improve the reconstruction performance. Finally, simulations are carried out to demonstrate the effectiveness of imaging methods and ECCM performance. Theoretical analysis and simulation results demonstrate that images generated in random FASAR have better performance than those in stepped FASAR. By performing the phase compensation, the reconstruction performance of small targets can be significantly improved.

The interrupted-sampling repeater jamming (ISRJ) is coherent and has the characteristic of suppression and deception to degrade the synthetic aperture radar (SAR) image quality. The anti-ISRJ methods are studied in this work in order to suppress the ISRJ based on the waveform and filter design for SAR. First, the relationship between the ISRJ and waveform is obtained by analyzing the principle of the ISRJ using the ambiguity function. The ISRJ produces multiple false targets based on the high Doppler tolerance of waveform and the characteristic of the matched filter. Next, a method is proposed to counter the ISRJ by transmitting a phase-coded (PC) waveform with low Doppler tolerance and designing the corresponding mismatched filter. The joint design method is then developed to improve the anti-ISRJ and imaging performance. In the proposed methods, the majorization minimization framework is introduced to solve the nonconvex waveform and filter design problem. Finally, several simulations are conducted to demonstrate the effectiveness of the proposed methods. Simulation results show that the joint design method shows better anti-ISRJ and imaging performance in comparison with the separate design method, but it is more sensitive to the ISRJ sampling duty ratio and period.

A joint waveform and filter design method is developed for suppressing radar pulse compression sidelobe level in this article. The problem is formulated as the minimization of the integrated sidelobe level (ISL) under the constraint of waveform constant modular and filter energy. To control the loss-in-processing gain (LPG), an additional function is introduced to constrain the peak level based on penalty function method. The joint design algorithm is then derived based on the alternating minimization and majorization minimization (MM) schemes. The computation complexity is analyzed and the convergence analysis verifies that the algorithm can converge to a critical point. Specifically, the proposed method is extended to the waveform and filter design for suppressing the peak sidelobe level (PSL). Numerical simulations are carried out to analyze the key parameters, which demonstrate the feasibility of the proposed method. Simulation results show that the ISL and PSL can be significantly reduced with small LPG. Moreover, the proposed method exhibits faster running speed than the existing one and it thus can be applied to longer sequence designs.

In this letter, an orthogonal frequency-division multiplexing (OFDM) chirp waveform diversity design scheme is proposed to achieve better correlation properties. First, the subchirp duration and bandwidth are optimized simultaneously to increase the degree of freedom in waveform diversity. Second, an optimization procedure is proposed based on the idea of Gram-Schmidt orthogonalization. To reduce the problem complexity, the design problem is separated into several subproblems. Each subproblem corresponds to designing an OFDM chirp composite waveform and is optimized by the Pareto optimization framework. Simulation results show that the proposed method outperforms the conventional methods in terms of both autocorrelation peak sidelobe level (APSL) and cross correlation peak level (CPL) under the circumstance of the same mainlobe width.

In this letter, a method is proposed to suppress the interrupted sampling repeater jamming (ISRJ) by jointly designing the radar waveform and mismatch filter. The joint design problem under multiple constraints is formulated with the optimization criterion of minimizing the integrated sidelobe levels (ISLs) of waveform mismatch filter output and integrated levels (ILs) of ISRJ signal mismatch filter output. An iterative algorithm is proposed to optimize the waveform and mismatch filter using Lagrange multiplier method and alternating direction multiplier method (ADMM), respectively. Simulation results demonstrate that the proposed method achieves good pulse compression performance while suppressing the ISRJ.

Ground-penetrating radar (GPR) has been widely used for antipersonnel mine (APM) detection. However, its efficiency is often impaired by high false alarm rate (FAR) caused by the ground clutters. In this letter, a novel robust principal component analysis (RPCA)-based method is proposed for fast prescreening of APM in GPR image. Taking advantage of low rank and sparse structure of GPR image, the proposed method first adopts an efficient RPCA technique—Go Decomposition (GoDec)—to extract the target image. Then, thresholds are applied to the extracted image to detect the target and reject false alarms. The proposed method enjoys two advantages over traditional methods: 1) the ability of reducing FAR while maintaining high probability of detection (PD) in strong noise and clutter environment and 2) the fast detection guaranteed by the modified GoDec that yields results within several iterations. Extensive simulations and laboratory experiments are conducted to validate the proposed method, and the results are satisfactory (high PDs up to 99% and low FARs).

Detecting shallow buried antipersonnel mines (APMs) with a ground-penetrating radar (GPR) is a challenging task because of clutter contamination, which often obscures the APM response. In this letter, a novel method combining migration imaging with the low-rank and sparse representation method to suppress clutter and extract target image is presented. The proposed method first focuses and strengthens the target response with migration imaging. Then, since the focused target response and clutter, respectively, constitute the sparse component and the low-rank component of the recorded data, the recently proposed robust principal component analysis (RPCA) can be applied to the recorded data to separate the target response (sparse component) from the clutter (low-rank component). Numerical simulation and experiments with real GPR systems are conducted. Results demonstrate the effectiveness of the proposed method in improving signal-to-clutter ratio and retrieving geometrical information of the target, which permits a better APM identification in heavy clutter environment.