Time delay estimation using Markov Chain Monte Carlo method

Abstract

A novel maximum likelihood estimation model for time delay is constructed to estimate the passive time delay; the signal of emitter is unknown in this model. According to the model characteristics, the fast Fourier transformation (FFT) method is used to achieve time delay estimation (TDE). In order to improve the accuracy of estimator, the Markov Chain Monte Carlo (MCMC) sampling method is used to estimate the time delay value directly. Unlike traditional algorithms, MCMC method can obtain time delay without peak detection. Furthermore, the Cramer-Rao lower bound (CRLB) of this model is derived. Finally, simulations show that the proposed approach is suitable for both narrowband and broadband TDE, and the MCMC algorithm can achieve more precise time delay value with the same sample, and it has lower computational complexity than IS algorithm. The novel approach can estimate also the time delay of non-integer multiple of the sampling interval.