4.6 Article

Interrupted-sampling repeater jamming suppression based on iterative decomposition

Journal

DIGITAL SIGNAL PROCESSING
Volume 138, Issue -, Pages -

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.dsp.2023.104059

Keywords

Interrupted-sampling repeater jamming; (ISRJ); Iterative decomposition; Sampling sequence; Jamming suppression

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Interrupted-sampling repeater jamming (ISRJ) is a type of intra-pulse coherent interference. The classical ISRJ model has constraints on forwarding strategies. We generalize the classical ISRJ model and propose an iterative decomposition method based on it. Simulation experiments show that the proposed method has excellent recognition and suppression performance for different types of ISRJ.
Interrupted-sampling repeater jamming (ISRJ) is a type of intra-pulse coherent interference. The classical ISRJ model has constraints on forwarding strategies. We generalize the classical ISRJ model. The entire ISRJ is viewed as the sum of multiple sub-interference signals corresponding to different sampling sequences. Based on the general model, an iterative decomposition method is proposed. The received signal is decomposed into several sub-signals. In each iteration, the processes of extraction, estimation, smoothing, identification and cancellation are finished. First, the signal sample corresponding to the current target is extracted. Then we use the amplitude information of the main peak to establish the objective function. The sample points that contribute to the amplitude are selected during optimization and the corresponding sampling sequence can be estimated. Finally, the attribution of the current target is determined by the characteristic value, and the corresponding sub-signal is eliminated from the original echo. The processes are repeated until all sub-signals are eliminated. Simulation experiments show that the proposed method has excellent recognition and suppression performance for direct forwarding, repeated forwarding, and periodic forwarding ISRJ. (c) 2023 Elsevier Inc. All rights reserved.

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