Anti-Jamming Multi-Target Parameter Estimation Using Space-Time Cascaded Adaptive Monopulse

Abstract

The multi-target space-time cascaded monopulse (M-STCMP) algorithm is an efficient method for parameter estimation in radar systems. However, under jamming conditions, the signal-to-jamming-plus-noise ratio (SJNR) deteriorates significantly, causing the sum and difference beam weights computed by the M-STCMP algorithm to become unreliable for target parameter estimation. To address this limitation, this paper proposes an anti-jamming multi-target space-time cascaded monopulse (AM-STCMP) algorithm as a robust framework for multi-target parameter estimation. The proposed AM-STCMP algorithm improves the conventional M-STCMP framework by integrating spatial adaptive monopulse processing. Unlike conventional derivative-based methods, this approach adaptively optimizes the sum and difference beam weights through maximum likelihood estimation, thereby effectively suppressing strong jamming while maintaining estimation accuracy. In addition, iterative optimization of the angle discrimination curve enhances the SJNR and improves parameter estimation accuracy. In the subsequent processing stage, the algorithm employs space-time cascaded monopulse processing for efficient range-velocity estimation and uses the RELAX algorithm for high-precision angle-velocity-range estimation, thereby maintaining accuracy while reducing computational complexity. Theoretical analysis and Monte Carlo simulations validate the AM-STCMP algorithm and demonstrate its improved robustness under strong jamming conditions.