Maximum likelihood parameter estimation of chirp signals based on MCMC

The imaging of moving targets by synthetic aperture radar (SAR) needs to accurately estimate the parameters of chirp return signals of moving targets. This paper presents a new method to obtain the maximum likelihood estimate of mono-component chirp parameters. The method merges the Markov chain Monte Carlo (MCMC) technique and mean likelihood estimation (MELE) with discrete chirpogram as the initial value selection method. Simulations and analyses showed that the parameter estimation performance of this method can attain the Cramer Rao bound (CRB) at low signal-to-noise ratio (SNR). The method is simple and can be implemented with modest amount of computations. The method jointly estimates the parameters with no error propagation effect.