Maximum likelihood angle-doppler estimator using importance sampling
Date of Original Version
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. © 2010 IEEE.
IEEE Transactions on Aerospace and Electronic Systems
Wang, Huigang, and Steven Kay. "Maximum likelihood angle-doppler estimator using importance sampling." IEEE Transactions on Aerospace and Electronic Systems 46, 2 (2010): 610-622. doi:10.1109/TAES.2010.5461644.