Efficient Parameters Estimation of Multi-target Based on Space-Time Cascaded Monopulse
An ACM-ML (Amplitude Comparison Monopulse-Maximum Likelihood) algorithm that employs a two-dimensional relaxation iterative search for range and velocity inevitably leads to low computational speeds and large amount calculation. Focusing on the problems mentioned above, a method for efficient Multi-target parameter estimation based on a Space-Time Cascaded MonoPulse algorithm (M-STCMP) is proposed in the paper. The algorithm introduces the monopulse technique to the pulse domain for target velocity computing with temporal monopulse, which results in a one-dimensional search for range and significantly reducing the computational burden of a two-dimensional iterative search within the ACM-ML algorithm. Because the temporal monopulse cannot simultaneously match multiple targets of varying velocities across main beams, the M-STCMP algorithm is improved by estimating the velocity in each Doppler cell with the Doppler information of received signals. To suppress energy leakage between targets, estimations produced in the main beams are cascaded and iterated for each target, that results in greater accuracy. Theoretical analysis and simulation verify the effectiveness of the proposed algorithm.
万方数据
维普
