# A Segmented Low-Order Bistable Stochastic Resonance Method for Fixed-Distance Target Detection in Millimeter-Wave Fuze Under Rainy Conditions

## TL;DR

This paper introduces a new method to improve millimeter-wave fuze signal detection in rainy conditions by boosting signal quality and detection accuracy.

## Contribution

The paper proposes a segmented low-order bistable stochastic resonance system with piecewise polynomial potential functions for improved signal processing.

## Key findings

- The proposed system achieves a 9.94 dB SNR enhancement for millimeter-wave fuze echoes in rainy conditions.
- The method enables reliable target detection at SNRs as low as −15 dB.
- The SLOBSR method outperforms conventional approaches in SNR enhancement and computational efficiency.

## Abstract

Millimeter-wave (MMW) fuze signals experience significant degradation in rainy environments due to combined raindrop-induced attenuation and scattering effects, substantially reducing echo signal-to-noise ratio (SNR) and critically impacting ranging accuracy. To address these limitations while satisfying real-time processing requirements, this study proposes (1) a novel segmented low-order bistable stochastic resonance (SLOBSR) system based on piecewise polynomial potential functions and (2) a corresponding fixed-distance target detection algorithm incorporating signal pre-processing, particle swarm optimization (PSO)-based parameter optimization, and kurtosis threshold detection. Experimental results demonstrate the system’s effectiveness in achieving a 9.94 dB SNR enhancement for MMW fuze echoes under rainy conditions, enabling reliable target detection at SNRs as low as −15 dB. Comparative analysis confirms the SLOBSR method’s superior performance over conventional approaches in terms of both SNR enhancement and computational efficiency. The proposed method significantly enhances the anti-rainfall interference capability of the MMW fuze.