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October 3, 2025Sensors2 citationsOpen Access

A Novel Radar Mainlobe Anti-Jamming Method via Space-Time Coding and Blind Source Separation

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XGXinyu GeYWYu WangYZYangcheng Zheng

Key Points

  • The method shows a significant jamming suppression gain of approximately 10 dB in point target scenarios.
  • Compared to traditional techniques, the innovative radar system exhibits about 3 dB improvement in distributed target scenarios.
  • The study employs a JADE-based algorithm, effectively enhancing the blind source separation capability under challenging conditions.
  • Incorporating space-time coding increases spatial degrees of freedom, addressing performance degradation issues linked to low signal-to-noise ratios.

Abstract

This paper proposes a radar mainlobe anti-jamming method based on Space-Time Coding (STC) and Blind Source Separation (BSS). Addressing the performance degradation issue of traditional BSS methods under low Signal-to-Noise Ratio (SNR) and insufficient spatial resolution, this study first establishes the airborne SAR imaging geometric model and the jamming signal mixing model. Subsequently, STC technology is introduced to construct more equivalent phase centers and increase the system’s spatial Degrees of Freedom (DOF). Leveraging the increased DOFs, a JADE-based blind source separation algorithm is then employed to separate the mixed jamming signals. The separation of these signals significantly enhances the anti-jamming capability of the radar system. Simulation results demonstrate that the proposed method effectively improves BSS performance. As compared to traditional BSS schemes, this method provides an additional jamming suppression gain of approximately 10 dB in point target scenarios and about 3 dB in distributed target scenarios, significantly enhancing the radar system’s mainlobe anti-jamming capability in complex jamming environments. This method provides a new insight into radar mainlobe anti-jamming by combining the STC scheme and BSS technology.

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Cite This Study

Ge et al. (2025) studied this question.

synapsesocial.com/papers/68e034fdf0e39f13e7fa370ehttps://doi.org/10.3390/s25196081
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