A new subsample time delay estimation algorithm for LFM-based detection

摘要

This paper investigated a Subsample Time delay Estimation(STE) algorithm based on the amplitude of cross-correlation functionto improve the estimation accuracy. In this paper, a rough time delay estimationis applied based on traditional cross correlator, and a fine estimationis achieved by approximating the sampled cross-correlation sequence to theamplitude of the theoretical cross-correlation function for linear frequencymodulation (LFM) signal. Simulation results show that the proposed algorithmoutperforms existing methods and can effectively improve time delayestimation accuracy with the complexity comparable to the traditional crosscorrelationmethod. The theoretical Cramér–Rao Bound (CRB) is derived,and simulations demonstrate that the performance of STE can approach theboundary. Eventually, four important parameters discussed in the simulationto explore the impact on Mean Squared Error (MSE).