A Segmented Low-Order Bistable Stochastic Resonance Method for Fixed-Distance Target Detection in Millimeter-Wave...

Area of Science:

• Radar Systems Engineering
• Signal Processing
• Environmental Effects on RF

Background:

• Millimeter-wave (MMW) signals used in fuzes suffer severe degradation in rain.
• Rainfall causes attenuation and scattering, reducing signal-to-noise ratio (SNR) and ranging accuracy.
• Existing methods struggle with real-time processing and performance in adverse weather.

Purpose of the Study:

• To develop a robust system for enhancing MMW fuze signals in rainy conditions.
• To improve target detection accuracy and reliability despite rainfall interference.
• To meet real-time processing demands for MMW fuze applications.

Main Methods:

• Proposed a novel segmented low-order bistable stochastic resonance (SLOBSR) system.
• Developed a fixed-distance target detection algorithm.
• Incorporated signal pre-processing, particle swarm optimization (PSO) for parameter tuning, and kurtosis threshold detection.

Main Results:

• Achieved a 9.94 dB SNR enhancement for MMW fuze echoes in rain.
• Enabled reliable target detection at signal-to-noise ratios as low as -15 dB.
• Demonstrated superior performance in SNR enhancement and computational efficiency compared to conventional methods.

Conclusions:

• The SLOBSR system significantly improves MMW fuze performance in rainy environments.
• The proposed detection algorithm enhances anti-rainfall interference capabilities.
• The method offers a viable solution for reliable MMW fuze operation under adverse weather conditions.