Maximum Likelihood Angle-Doppler Estimator using Importance Sampling

A new joint angle-Doppler maximum likelihood estimator (MLE) based on importance sampling (IS) is proposed. The IS method allows one to compute the maximum likelihood estimator in a computationally efficient manner. It is based upon generating random variates using an importance function which approximates the compressed likelihood function. The performance is very close to the Cramer-Rao lower bound (CRLB). The choice of the algorithm parameters, which will affect the estimation performance, is also addressed. With a reasonable parameter choice, even the angles/Dopplers for closely spaced sources can be accurately estimated, whereas conventional subspace methods fail. Comparison with some suboptimal methods demonstrates that the IS method produces better performance at low signal-to-noise (SNR) and/or a small number of snapshots.