Coding Symmetry Breaking Detection: A Signal Processing Framework for Passive Illuminator-of-Opportunity Detection

Abstract

Passive radar, which requires no active transmission, offers strong anti-destruction capability and anti-stealth potential, and has become an important development direction of new-system radar. However, the core detection logic of existing passive detection systems still relies entirely on changes in signal energy to make target decisions, whereas the central design objective of modern stealth aircraft is precisely to suppress such energy variations; the two form a principle-level logical deadlock. This paper proposes a novel coding-structure-domain detection paradigm that lifts the detection dimension from the physical energy layer to the coding structure layer. Built on an endian-reversed mirror-coding mechanism of H/V dual-polarization channels, the scheme establishes a stable coding-domain symmetry baseline and identifies stealth targets by capturing electromagnetic-propagation symmetry-breaking features. Specifically, a three-dimensional metric fusion decision framework is constructed from the JS divergence, mutual-information difference, and statistical-moment difference of the dual-channel LLR residuals, transforming stealth-target detection into a vectorial judgment of coding symmetry breaking. Theoretical analysis shows that the sensitivity amplification factor of endian-reversed coding is proportional to the coding length N; when N = 64, the JS-divergence sensitivity amplification reaches about 18 dB. The scheme comprehensively outperforms conventional energy detection in detection sensitivity, anti-spoofing capability, and target-classification potential; the minimum detectable RCS is as low as the order of 0.01 m², with an equivalent SNR threshold below −15 dB, and principle validation can be accomplished with commercial off-the-shelf components. The paper establishes a complete mathematical framework and theoretical performance bounds, providing a paradigm-level technical route for passive anti-stealth detection.

Keywords