Acceleration and velocity estimation of maneuvering targets based on Sparse and Redundant Representantions

The acceleration and velocity of a highly maneuvering target in signal processing can be helpful to enhancing the performance of the tracker and facilitating the classification of targets. At present, most of researches on acceleration and velocity estimation are carried out in case of single target with time-frequency analysis methods such as Fractional Fourier transformation (FRFT), Hough-Ambiguity transformation (HAT), and Wigner-Ville distribution (WVD), which need satisfy enough time duration and sampling theorem. Sparse and Redundant Representations has the advantage of sampling at low rate and short time duration without sacrificing estimation performance. Therefore this paper proposes a new method of acceleration and velocity estimation for multiple maneuvering targets with the unknown number of targets based on Sparse and Redundant Representations and clustering technique with pulse Doppler signal. Simulation results validate the effectiveness of the proposed algorithm under several conditions with different time duration, measurement number and signal to noise ratios (SNR) respectively. Simulation results also show that the performance of the proposed method in the condition of short time duration is superior to that of FRFT for acceleration estimation of maneuvering targets when SNR >; -4dB.