Rotational Angular Velocity Estimation of Rotor Target via 3D-OMP-Based Parametric Sparse Representation

Abstract

As a special feature of rotor target, rotational angular velocity can provide important information for target classification and recognition. In this paper, a rotational angular velocity estimation method is proposed based on complex empirical mode decomposition (CEMD) and three-dimensional orthogonal matching pursuit (3D-OMP)-based parametric sparse representation (PSR) technique. Firstly, the translational radial velocity is estimated from radar echo via Fourier transform, and then the translational compensation is implemented according to the estimated velocity. Secondly, in order to eliminate the influence of the target main body echo on micro-Doppler parameter estimation, CEMD algorithm is adopted for target echo separation. Then, the separated signal of rotor echo is formulated as a jointly sparse signal through a three-dimensional parametric dictionary matrix, which converts the rotational angular velocity estimation into a problem of dynamic representation of jointly sparse signals. Finally, in order to reduce the computational complexity of the estimation method, we propose a 3D-OMP-based parametric sparse representation algorithm to achieve the sparse solution, and the rotational angular velocity can be estimated by minimizing the reconstruction error. The experimental results verify the effectiveness of the proposed algorithm.