An Improved Method for Estimating Ballistic Target Micromotion and Structural Parameters Based on ISAR Image Sequences

Micromotion and structural parameter extraction is a critical technique for recognizing ballistic missiles. It is generally known that parameter estimation methods based on inverse synthetic aperture radar (ISAR) image sequences can effectively extract target information. However, when the signal-to-noise ratio (SNR) is low, the traditional methods are prone to cross-association problems in the nearest-neighbor association procedure, and the structural parameters obtained may only be the local optimal solution. To solve the above two problems, an improved method for estimating ballistic target micromotion and structural parameters based on ISAR image sequences is proposed in this article. A distance association probability revision matrix is used to address the cross-association problem, reducing the risk of association with the incorrect nearest neighbor; at the same time, the impact of noise on the frequency estimation is decreased by utilizing refinement domain search-match algorithm, which can discover a narrower estimation interval with less noise to improve SNR. The global optimization capacity for the structural parameter estimation of the traditional particle swarm optimization (PSO) algorithm is enhanced by the step-by-step optimization algorithm, which reduces the dimension of the high-dimensional micromotion projection cost function to ensure optimization direction variety. Simulation results show that when the SNR is $-$5 dB, the accuracy of the precession frequency estimation is improved by 0.2 Hz compared with the traditional fast Fourier transform method, and the root-mean-square error values for cone height, precession angle, and nutation angle are 0.02 m, 0.2$^\circ$, and 0.3$^\circ$, respectively, which are better than those of the traditional PSO algorithm.