ArticleSensors (Basel, Switzerland)2019
Bayesian Compress Sensing Based Countermeasure Scheme Against the Interrupted Sampling Repeater Jamming.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- End-to-End Interrupted Sampling Repeater Jamming Countermeasure Network Under Low Signal-to-Noise Ratio.Sensors (Basel, Switzerland) · 2025Article
- An Anti-Interrupted-Sampling Repeater Jamming Method Based on Simulated Annealing-2-Optimization Parallel Optimization of Waveforms and Fractional Domain Extraction.Sensors (Basel, Switzerland) · 2025Article
- Transient Interference Excision and Spectrum Reconstruction with Partial Samples Using Modified Alternating Direction Method of Multipliers-Net for the Over-the-Horizon Radar.Sensors (Basel, Switzerland) · 2024Article
- An Anti-Jamming Method against Interrupted Sampling Repeater Jamming Based on Compressed Sensing.Sensors (Basel, Switzerland) · 2022Article
- An Anti-Jamming Method against Two-Dimensional Deception Jamming by Spatial Location Feature Recognition.Sensors (Basel, Switzerland) · 2021Article
- Intelligent Recognition of Chirp Radar Deceptive Jamming Based on Multi-Pulse Information Fusion.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
The interrupted sampling repeater jamming (ISRJ) is considered an efficient deception method of jamming for coherent radar detection. However, current countermeasure methods against ISRJ interference may fail in detecting weak echoes, particularly when the transmitting power of the jammer is relatively high. In this paper, we propose a novel countermeasure scheme against ISRJ based on Bayesian compress sensing (BCS), where stable target signal can be reconstructed over a relatively large range of signal-to-noise ratio (SNR) for both single target and multi-target scenarios. By deriving the ISRJ jamming strategy, only the unjammed discontinuous time segments are extracted to build a sparse target model for the reconstruction algorithm. An efficient alternate iteration is applied to optimize and solve the maximum a posteriori estimate (MAP) of the sparse targets model. Simulation results demonstrate the robustness of the proposed scheme with low SNR or large jammer ratio. Moreover, when compared with traditional FFT or greedy sparsity adaptive matching pursuit algorithm (SAMP), the proposed algorithm significantly improves on the aspects of both the grating lobe level and target detection/false detection probability.
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