The increasing complexity of the electromagnetic environment poses significant challenges to jamming countermeasures. Active jamming recognition serves as a crucial prerequisite for active jamming countermeasures. Addressing the blindness of feature extraction in radar active jamming recognition, this paper proposes an active jamming recognition method based on Fisher discriminant and feature selection. This method employs the Fisher discriminant criterion to theoretically derive feature separability and rank the importance of feature attributes. the features with the best performance are selected as inputs for the classifier. The proposed method substantially reduces the computational cost of feature extraction without compromising recognition accuracy. In simulation experiments, when jamming-to-noise ratio is above$-$5 dB, the recognition rate consistently exceeds 96%, demonstrating superior generalization and robustness.

Simultaneous polarimetric radars (SPRs) are powerful tools for measuring the polarization scattering matrix of targets in a single pulse. However, a major challenge associated with SPRs is the design of orthogonal waveforms that can effectively minimize the interference arising from simultaneous transmission and reception. Currently, frequency modulation (FM) and phase-coded modulation are the most commonly used signal structures. However, these signal structures suffer from certain limitations that necessitate the development of new orthogonal waveforms that are both well-suited to high-power transmitters and maximally free in design. This article proposes an approach that leverages polyphase-coded FM waveforms to design orthogonal structures that combine the benefits of FM and phase-coded waveforms. A mathematical model for designing such waveforms is established considering a matched filter at the receiver. The model unifies the parameters of orthogonality and sidelobe level into a fraction expressed in terms of the sidelobe integral level, main-lobe integral level of autocorrelation functions, and entire-lobe integral level of cross correlation functions. This fraction accurately characterizes the orthogonality and sidelobe level of a pair of waveforms. The optimal waveform group can be obtained using a binary gradient descent algorithm, with peak sidelobe levels and isolation levels simultaneously approaching −30 dB. Finally, this article describes the implementation of the proposed orthogonal waveforms on hardware and verifies their performance by presenting experimental results that confirm the effectiveness of the proposed method.

Machine learning methods for synthetic aperture radar (SAR) image automatic target recognition (ATR) can be divided into two main types: traditional methods and deep learning methods. The deep learning methods can learn the high-dimensional features of the target directly, and usually obtain high target recognition accuracy. However, they lack full consideration of SAR targets’ inherent characteristics resulting in poor generalization and interpretation ability. Compared with the deep learning methods, traditional methods can get more interpretable and stable results with model-based features. In order to take full advantage of these two kinds of methods, we propose target part attention network based on the attributed scattering center (ASC) model to integrate the electromagnetic characteristics with the deep learning framework. Firstly, considering the importance of scattering structure for SAR ATR, we design a target part model based on ASC model. Then, a novel part attention module based on Scaled Dot-Product Attention mechanism is proposed, which directly associates the features of target parts with the classification results. Finally, we give the derivation method of the importance of each part, which is of great significance for practical application and the interpretation of SAR ATR. Experiments on the MSTAR data set demonstrate the effectiveness of the proposed part attention network. Compared with existing studies, it can achieve higher and more robust classification accuracy under different complex conditions. Furthermore, combined with the importance of parts, we constructed two effective interpretable analysis methods for deep learning network classification results.

Sequence set design with good correlation properties and Doppler tolerance has been a classic and important problem in many multichannel systems, including but not limited to the multiple-input multiple-output (MIMO) and simultaneous polarimetric radar systems. In this article, we first consider the problem of jointly designing Doppler resilient unimodular sequences and receiving filters to minimize the metric weighted integrated sidelobe level (WISL), which can be used to construct sequence sets with “thumbtack” co-channel and zero cross-channel ambiguity functions (AFs). To control the signal-to-noise ratio (SNR) loss caused by the mismatched filter, a peak constraint function is added to the objective function based on the penalty function method. An algorithm based on the alternatively iterative scheme and general majorization-minimization (MM) method is developed to tackle the constrained joint design problem. Moreover, the proposed algorithm is then extended to optimize the${l_{p}}$-norm of sidelobes of AFs, which gives a way to optimize the weighted peak sidelobe level (WPSL) metric. Due to the use of the fast Fourier transform (FFT) algorithm and a general acceleration scheme, the proposed algorithm can be realized efficiently. A number of simulations are provided to demonstrate the excellent performance of the proposed sequence set synthesis algorithms. Besides, an application of using the designed Doppler resilient sequence set on the simultaneous polarimetric radar to detect multiple moving targets in a strong clutter scene is given via simulation, which further verifies the practicability of the proposed algorithm.

Compared to the steady-state part of the extended target echo, few scholars have paid enough attention to the echo establishment and disappearance part. They contain abundant target information, but have not been fully explored and utilized. Since the pulse duration of both parts is typically short, they can be considered as instantaneous processes. Polarization is an inherent property of electromagnetic waves, which can bring information gain to the analysis of the echo instantaneous part. The objective of this study is to utilize the instantaneous polarization response (IPR) of the echo to achieve super-resolution for two targets with different polarization scattering characteristics. It is analyzed that when two target echoes are “equal-amplitude and opposite-phase", echo decoupling can be achieved, which is the precondition for achieving range super-resolution. While making full use of the polarization information is instrumental in achieving the echo with “equal-amplitude and opposite-phase". A trihedral and a dihedral are selected to verify the effectiveness of the proposed method. Multiple sets of measured data show that the two target echoes do have the instantaneous part. When the signal-to-noise ratio (SNR) of the echo data is about 6-7dB, the range super-resolution and range estimation of the two targets are achieved by using the edge threshold detector.

The realistic false targets caused by interrupted sampling repeater jamming (ISRJ) can mask the real target, leading to the failure of radar target detection. In this letter, a method based on jointly designing complementary sequences and receiving filters under the signal-to-noise ratio loss constraint is proposed to suppress ISRJ. A gradient-based nonlinear programming solver and the Lagrange multiplier method are used to optimize the sequences and filters alternately. Numerical results show that the proposed method can generate sequences and receiving filters with good correlation properties and anti-ISRJ performance.

Complete complementary sequence (CCS) design has become an important topic in recent years due to its theoretically ideal zero sidelobe performance. However, the main obstacle for the widespread use of the CCS is its sensitivity to the Doppler shift. In this letter, a method for constructing the CCS with the desired matrix-valued ambiguity functions under a peak-to-average-power ratio (PAR) and an energy constraint is proposed. A gradient-based nonlinear iterative algorithm is used to solve the constrained sequence design problem. Numerical results are presented to show that, compared with the state-of-the-art methods, the proposed method can produce CCS with better correlation properties and the Doppler tolerance. Finally, indoor experiments are conducted to verify the superior performance of the designed CCS, and a peak sidelobe level under −45 dB is realized on the hardware system.

Deep learning has been widely used in automatic target recognition (ATR) for synthetic aperture radar (SAR) recently. However, most of the studies are based on the network structure in optical images and lack full consideration of the inherent characteristics of SAR targets, which limits the improvement of recognition accuracy and makes poor generalization ability. In addition, due to the black-box characteristics, it is difficult to effectively interpret SAR ATR results. To conquer these problems, we propose an electromagnetic scattering feature (ESF) module embedded network based on attributed scattering center (ASC) model to incorporate the SAR targets’ characteristics into the deep learning framework. First, a novel convolutional neural network (CNN)-based algorithm for extracting ASC parameters is proposed, which makes the network focus on target features under the guidance of physical model. Then, the ESF module is designed based on a well-trained ASC parameters extractor to inject the learned target features into the classification network for more robust and interpretable results. Besides, two structures are proposed combined with the ESF module for single-view and multiview SAR target classification, which further illustrates the portability of the module. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show the validity of the proposed CNN-based ASC extractor and the ESF module embedded classification network. Compared with ordinary networks, our method can achieve higher classification accuracy under complex conditions, which reflects the better generalization performance of the algorithm. Furthermore, through visualization analysis of the classification results, we show the interpretability of the network combined with the electromagnetic scattering characteristics.

The ambiguity function (AF), which is used to evaluate the range and Doppler resolutions, plays an important role in radar systems. In this article, we consider the problems of jointly designing unimodular sequence and receiving filters, and also unimodular complementary sequences and corresponding receiving filters with desired AF shapes. The design problems are formulated as the minimization of the weighted integrated sidelobe level (WISL) and the minimization of the complementary integrated sidelobe level (CISL), respectively, under the constraint of the signal-to-noise ratio (SNR) loss. Algorithms based on the alternately iterative minimization method and the majorization-minimization method are developed to tackle the optimization problems. Numerical results are demonstrated to show the superior performance of the proposed algorithms in terms of the achieved objective and running time in comparison with the state-of-the-art algorithms. Meanwhile, the excellent capability of the designed sequences to detect multiple moving targets under the strong clutter is evaluated via simulations. Moreover, the designed sequences are also implemented on an indoor hardware system, and their performance is verified.

Simultaneous polarimetric radars can measure the polarization scattering matrix (PSM) of moving targets within one pulse. The key point is the orthogonal waveform design to reduce the interference caused by the simultaneous transmission and reception. Meanwhile, to ensure the measurement accuracy of moving targets, the Doppler tolerance of the transmitted waveforms is also important. In this article, first, a matched filter (MF) at the receiver is considered and a unified framework with a newly proposed objective function to design Doppler resilient sequences under several constraints is described. The proposed objective function contains the widely used peak sidelobe level (PSL) and the integrated sidelobe level (ISL) as special cases. Meanwhile, a series of constraint conditions, including the unimodular constraint, the uncertainty modulus constraint, and the PAR constraint, is considered. Thereafter, a mismatched filter is considered for further improving the correlation properties and a similar framework for designing the optimal receiving filter at the expense of a controllable signal-to-noise ratio (SNR) loss is proposed. Numerical results are presented to verify the performance of the proposed methods. The designed sequence pair is also implemented on a fully polarimetric system to measure the PSM of a canonical target in the anechoic chamber, and the effectiveness of the proposed framework is verified by using the simultaneous polarimetric radar. It is predicted that the framework has the potential to be applied for other multichannel systems.

A pair of waveforms with good auto- and crosscorrelation properties is required in simultaneous polarimetric radar. Complete complementary sequence (CCS) has ideal range sidelobes along the zero Doppler axis, which is desirable in polarimetric radar. However, CCS is not widely used due to its sensitivity to the Doppler shift. In this letter, a L-BFGS (Limited Memory Broyden Flecher Goldfarb and Shanno) iterative method of constructing CCS with high Doppler tolerance is proposed, by which the range sidelobes can be suppressed in certain Doppler shifts. Numerical results are shown to demonstrate that the sidelobe peak of the designed CCS is about 30 dB lower than that of the CCS designed by the state-of-the-art GPTM (Generalized Prouhet-Thue-Morse) method in a certain Doppler shift interval.

Image quality assessment (IQA) aims to devise perceptual models to predict the image quality consistently with human subjective evaluation. The representative metrics focus on measuring the image quality with low-level features. In this letter, we assumed that the distortion in specific regions containing semantically significant objects would be enhanced by HVS significantly. According to this hypothesis, a novel IQA metric based on a commonly used object-detecting feature, Speed Up Robust Features (SURF), was proposed. First, it determined the interest points which represented significant objects through the SURF features both on the reference image and distorted image. Then it computed the multilevel SURF descriptors differences between the reference image and the distorted one. Finally, all the difference results were combined with a suitable pooling strategy. Comparing with other nine state-of-the-art IQA models on three biggest IQA databases, SURF-SIM demonstrated its highly competitive prediction accuracy especially on complicated applications and excellent robustness across different distortion types.