Interrupted sampling repeater jamming (ISRJ), falls within the category of intrapulse coherent interference. Leveraging undersampling principles, ISRJ generates multiple false target peaks on the range profile, thus interfering with the detection and tracking of genuine targets. To address this issue, this paper presents a method called Waveform-Domain Adaptive Matched Filtering (WD-AMF) for ISRJ suppression. Initially, recognizing ISRJ's partial matching characteristics, we introduce an extended domain, namely the waveform domain, into the matched filtering process. This extension allows us to analyze the local characteristics of the interference signal and the components of the authentic target echo signal. Based on these characteristics, we define an adaptive threshold function in the waveform domain to distinguish between valid and invalid integration elements, which does not rely on prior information about ISRJ. Additionally, we introduce the Interacting Multiple Model-Kalman Filtering (IMM-KF) algorithm for state estimation of waveform domain signals and establish an estimated state space related to valid integration elements to mitigate the effects of noise. Furthermore, we introduce waveform domain coherent processing methods over slow time to reduce clutter interference. Lastly, while suppressing invalid integration elements in the waveform domain signal, we retain valid integration elements and supplement the corresponding length of integration elements from the estimated state space. This integration yields a range profile devoid of false targets. Through simulations and realworld experiments, we demonstrate the remarkable capability of the proposed method in ISRJ suppression, surpassing other methods even in the absence of a pre-existing ISRJ model.

Parameter estimation of chirp signals plays an important role in the field of radar countermeasures. Compressed sensing (CS) based sub-Nyquist sampling and parameter estimation methods alleviates the pressure on hardware systems to acquire and process chirp signals with large time-bandwidths. In this paper, a framework based on the fractional Fourier transform (FrFT) and alternating direction method of multipliers network (ADMM-Net) is proposed to realize chirp signal parameter estimation under sub-Nyquist sampling. The whole framework is composed of multiple parallel ADMM-Nets, where each ADMM-Net is defined over a data flow graph, which is derived from the iterative procedures of the ADMM algorithm for optimizing a CS-based $p$ -order FrFT spectral estimation model. The chirp rate and central frequency of chirp signals are obtained through a two-dimensional search on the spectrum image output by the network group. Experiments demonstrate that the proposed ADMM-Net-based method can achieve higher estimation accuracy and computational efficiency at lower signal-to-noise ratios and sampling ratios than traditional CS methods. We also demonstrate that the proposed ADMM-Net-based framework has strong generalization ability for multi-component chirp signals. Furthermore, we further generalize ADMM-Net to GADMM-Net, in which the activation function is data-driven instead of model-driven. Experiments demonstrate that GADMM-Net significantly improves on the basic ADMM-Net and achieves higher spectral resolution with faster computation speed.

This paper addresses the problem of estimating the parameters of constant-amplitude chirp signals that have single or multiple components and are embedded in noise. Chirp signals are widely employed in applications such as radar and telecommunications, and it is a key task in countermeasure techniques to estimate their parameters without prior information. Hence, a parameter estimation processor based on a complex-valued deep neural network (CV DNN) is proposed to perform this task efficiently. The CV DNN, which is designed for regression, consists of a function fitter and a predictor. The function fitter acts like a eigenfunction mapping: it maps the one-dimensional input into a two-dimensional feature map suitable for subsequent network learning. As a special feature extraction tool, the predictor extracts local features from the feature map and estimates parameters. Simulation results indicate that the CV DNN outperforms conventional processors. Moreover, it is more accurate than the Wigner–Hough transform while being several orders of magnitude faster, which will enable real-time signal processing with fewer computational resources. Furthermore, we demonstrate that the CV DNN shows strong robustness to changes in modulation parameters and the number of components of a chirp signal. This study shows the advantages of deep learning systems for signal parameter estimation.

The intelligent noise jamming, known as Smart Noise-Interrupted Sampling Repeater Jamming (SN-ISRJ), constitutes a coherent disturbance with suppressive characteristics, engineered to attenuate radar detection performance. The study focuses on anti-SN-ISRJ techniques within the waveform domain, primarily leveraging signal processing methodologies specific to the waveform domain. We illustrate that when utilizing the Waveform Domain Complementary Signal Set (WDCSS) as the transmission waveform, the waveform response function of SN-ISRJ also manifests waveform domain complementarity. Concurrently, we establish that the cumulative waveform coherence function (CWCF) of SN-ISRJ is locally piecewise nonlinear. Moreover, we demonstrate that in the presence of SN-ISRJ, the CWCF of the the received signal constitutes the unique non-zero linear function at the target location. To extract linear components and suppress nonlinear elements in the waveform domain, we introduce Bernoulli filtering within the waveform domain. This procedure, in conjunction with matched filtering, gives rise to a comprehensive signal processing algorithm: Waveform Domain Bernoulli Matched Filtering (WD-BMF). Simulation results substantiate that the proposed WD-BMF attains full suppression of SN-ISRJ without dependence on jamming prior information, preserving the energy of the target echo. It showcases superior adaptive anti-jamming capabilities when compared to conventional methods.

Interrupted-sampling repeater jamming (ISRJ) is a kind of intra-pulse coherent deception jamming that can generate false target peaks in the range profile and interfere with the detection and tracking of real targets. In this paper, an anti-ISRJ method based on the intra-pulse orthogonal waveform is proposed, which can recognize common interference signals by comparing sub-signal matched filtering results. For some special scenes where real targets cannot be directly differentiated from false targets, a new recognition method based on the energy discontinuity of the interference signal in the time domain is proposed in this paper. The method proposed in this paper can recognize real and false targets in all ISRJ modes without any prior information, such as jammer parameters, with a small amount of calculation, which is suitable for actual radar systems. Simulation experiments using different interference parameters show that although this method has a 3 dB loss of pulse compression gain, it can completely suppress different kinds of ISRJ interference when the SNR before pulse compression is higher than −20 dB, with 100% target detection probability.

The interrupted-sampling repeater jamming (ISRJ) is a form of coherent jamming that encompasses both suppression and deception effects, with the aim of disrupting radar detection capabilities. This study focuses on anti-ISRJ techniques in the waveform domain, with a primary emphasis on waveform design combined with anti-jamming signal processing methods. By examining the relationship between waveform-domain adaptive matched filtering (WD-AMF) and the transmitted waveform, we demonstrate that ISRJ can be effectively suppressed when the transmitted waveform exhibits waveform-domain complementarity. We introduce a phase-coded (PC) waveform set with waveform-domain complementarity, referred to as Waveform-Domain Complementary Signal Sets (WDCSS), and propose a method for generating such waveform sets with binary phase shift keying (BPSK) modulation of arbitrary code lengths. The performance of WD-AMF is further enhanced by the designed waveforms, achieving complete suppression of ISRJ in any mode without reducing the radar's normal detection capability. Simulations confirm that WDCSS in WD-AMF has superior adaptive anti-jamming capabilities compared to other traditional waveforms, while also demonstrating its robustness under varied environmental conditions and electronic counter-countermeasures (ECCM). Remarkably, this improved performance is achieved without the need for prior information of ISRJ working parameters at either the transmitter or receiver stages.