By partial interception and multiple forwarding of a radar transmitting signal, digital radio frequency memory‐based interrupted sampling repeater jamming can yield a partial processing gain and form multiple false target groups in the range direction, achieving jamming effects of both suppression and deception. Various improved jamming strategies have been proposed, while jamming suppression problems have not been fully addressed. In this study, a jamming suppression method based on the idea of ‘reconstruction and cancellation’ is proposed by analysing the jamming principle. The method firstly analyses the pulse compression results with time‐frequency analysis to obtain the intercepted slice number and forwarding times; then, the slice width is estimated by deconvolution processing; finally, iterative cancellation is used to suppress the jamming. Performance of the method was verified by Monte Carlo simulation. The results show that the normalised error of the slice width estimation is 16 dB.

Conventional least mean square (LMS) algorithm is one of the most popular algorithms, which is widely used for adaptive beamforming. But the performance of the LMS algorithm degrades significantly because the constant step size is not suitable for varying signal-to-noise ratio (SNR) scenarios. Although numerous variable step size LMS (VSS-LMS) algorithms were proposed to improve the performance of the LMS algorithm; however, most of these VSS-LMS algorithms are either computationally complex or not reliable in practical scenarios since they depend on many parameters that are not easy to tune manually. In this letter, a fast and robust VSS-LMS algorithm is proposed for adaptive beamforming. The variable step size is obtained based on normalized sigmoid function, where the sigmoid function is calculated by using the mean of instantaneous error firstly and then normalized by the squared cumulative sum of instantaneous error and estimated signal power. The proposed algorithm can update the step size adaptively without tuning any parameter and outperform state-of-the-art algorithms with low computational complexity. The simulation results show better performance of the proposed algorithm.

This paper proposes a deep learning-based compound jamming cognition method to recognize, detect individual jamming elements and estimate key parameters of them. The method first uses a time-frequency distribution (TFD) to characterize jamming in multiple dimensions (time, frequency and energy) and then applies an object detection network to identify and locate jamming in the time-frequency domain. This paper summarizes the types of jamming parameters and gives corresponding methods for estimating parameters. Unlike traditional studies, this paper models jamming recognition as an object detection problem and applies a deep learning framework to find solutions. Therefore, the proposed method has better stability and robustness than conventional techniques, which solves the problem of feature selection caused by the lack of mapping relationship between jamming and features. Another advantage over conventional methods is the multi-jamming detection capability of this algorithm, which provides more information about individual elements of compound jamming. In terms of jamming parameter estimation, the proposed method makes full use of the geometric characteristics of TFDs, so it is more versatile than conventional methods based on analytical analysis. Simulations and experimental data are used to verify the effectiveness of the proposed method.

Phase autofocus is a significant step in translational motion compensation for inverse synthetic aperture radar (ISAR) imaging. Among the existing autofocus methods, contrast maximization-based algorithms are superior in accuracy and robustness. However, the existing maximum contrast autofocus methods are parametric, which significantly affects their convergence speed and, in some cases, limits their applicability. In this paper, a novel non-parametric maximum contrast ISAR autofocus algorithm based on Newton's method is proposed to inherit the high level of accuracy and robustness of contrast-based algorithms and, at the same time, achieve faster convergence. The simplified Newton's method, the modified Newton's method, and the secant method are used to obtain a higher computational efficiency. The proposed method is then compared to a well-established non-parametric method, namely the minimum entropy autofocus method. It will be shown that the proposed method can improve the computational efficiency by a factor from 5 to 10 times in typical scenarios. Both simulated and real data will be used to test the proposed algorithm, particularly in terms of computational effectiveness.

The interrupted sampling repeater jamming (ISRJ) produces multiple false targets after pulse compression, making it difficult for radar to detect real targets. In this paper, an anti-ISRJ algorithm is developed based on the joint design of the waveform and mismatched filter with expanded mainlobe. First, to prevent false alarms when the range sidelobes and jamming peaks of the filter output are too high, the sum of the peak sidelobe level of the waveform compressed with mismatched filter and the peak level of the ISRJ signal compressed with mismatched filter is taken as an optimization criterion for minimization. Meanwhile, a penalty function is used to control the mainlobe shape of the correlation function between the mainlobe. To maximize the transmitted power efficiency of the radar and control the signal-to-noise ratio loss caused by the mismatched filter, a constant modular constraint for the waveform and an energy constraint for the filter are adopted. Then, an iterative algorithm based on the majorization– minimization framework is proposed to solve the joint design problem. In each iteration, an analytical expression is derived to realize the simultaneous updating of the waveform and filter. The convergence of the proposed algorithm is proven, and its computational complexity is analyzed. Finally, simulations are provided to demonstrate the effectiveness of the proposed algorithm. Compared with existing algorithms, the waveform and filter designed in this paper exhibit better pulse compression performance and anti-jamming ability.