Three-Parameter Agile Anti-Interference Waveform Design and Corresponding MUSIC-Based Signal Processing Algorithm

Radar systems with exceptional anti-jamming performance are critical to meeting the high-performance requirements of future intelligent sensing systems. To address the deception jamming challenges encountered by intelligent sensing systems environments, a multi-parameter agile waveform is designed. The proposed waveform exhibits high flexibility across three dimensions—pulse width, pulse repetition interval, and carrier frequency. Compared to traditional single-parameter or two-parameter agile waveforms, which vary only one or two parameters, this multi-parameter approach significantly enhances anti-jamming performance by disrupting periodicity and providing higher flexibility in dynamic interference environments. To address the complex signal characteristics induced by multi-parameter agility, we further develop a low-complexity signal processing method based on a segmented multiple signal classification (MUSIC) algorithm, which accurately extracts Doppler information from pulse-compressed slow-time data to achieve high-precision velocity estimation. Both theoretical derivations and simulation results demonstrate that, compared with the conventional compressed sensing orthogonal matching pursuit method and the conventional MUSIC method that operate on the entire signal, our segmented approach divides the signal into smaller segments, reducing computational complexity and improving velocity estimation accuracy. Notably, even in high-intensity, densely jammed environments, the system reliably extracts target information.