Reconstruction Method for Radio Fuze Signals Based on Compressed Sensing

In response to the challenges posed by the advancement of radio fuze towards millimeter-wave and higher frequency bands, which enhance target detection accuracy and anti-jamming capabilities, this paper addresses the resulting burdens on signal reception, storage, and transmission. Under constraints of limited hardware resources and costs, we propose a reconstruction method for sampled data based on compressed sensing, leveraging the frequency domain sparsity of radio echoes. This approach aims to reduce the equivalent sampling rate of the received signals, thereby alleviating storage and transmission loads. The method employs an observation matrix for compressive sampling and utilizes the interior-point method to solve the convex optimization problem inherent in the reconstruction of sparse coefficients. Simulation results demonstrate that the proposed method significantly reduces the equivalent sampling rate of the receiver and mitigates data storage and transmission pressures while maintaining signal accuracy. This provides a technological foundation for the efficient and cost-effective implementation of signal processing in radio fuze.