LAUSR

Micro-range (m-R) signatures expressed by high resolution range profiles (HRRPs) are important in target classification. However, when the measured rotational target has maneuverability, the translational motion of that would destroy the periodicity of m-R signatures and makes a challenge for rotational parameter estimation. In this paper, we proposed a novel method to compensate the translational motion and estimate the rotational parameter under low SNR based on particle swarm optimization (PSO) algorithm. First, a HRRP-based method is utilized for the coarse estimation of the rotational period and translational parameters, then compensating the HRRPs with coarse translational motion. Second, a two-level iterative algorithm is used to estimate precise rotational period and the parameter of residual translational motion. In the inner iteration, the image entropy of the inverse radon transform result of compensated HRRPs (IEIRCH) is utilized as the objective function to estimate the residual translational parameters and accurate period, which is solved by the PSO algorithm and one-dimension search alternately. In the outer iteration, the final IEIRCH motivated by the inner iteration is regarded as a criterion to determine the polynomial order. Finally, the HRRPs compensated by estimated translational motion and period are used to obtain the rotational amplitude and initial phase by inverse radon transform. The effectiveness and robustness of the proposed method are verified by measured radar data.