A chirplet-based masking algorithm for smeared spectrum jamming suppression and signal separation

Abstract

Linear frequency modulation (LFM) signal is a common radar signal in modern electronic warfare, and smeared spectrum (SMSP) can generate multiple false targets, causing jamming to radar detection. The authors propose a chirplet-based masking algorithm that can solve the problem of SMSP jamming suppression and address a more complex problem: the separation of jamming signal and multiple LFM signals from intercepted mixed signal. First, the authors obtain matched chirp rates of the source signals through the changing tendency of the Rényi entropy. Then, the ridge of each source signal is extracted from the high-resolution chirplet transform result using an image processing-based algorithm. Finally, the jamming and LFM signals are accurately reconstructed through the time-frequency mask to achieve separation. Even in the extreme case where multiple source signals with close chirp rates are overlapped, the proposed slope-matching ridge extraction method and iterative update reconstruction method can still achieve commendable signal separation effects. Extensive experimental results demonstrate that the proposed algorithm performs well under extreme conditions of low signal-to-noise ratio, high jamming-to-signal ratio, and high sea state.