The flexibility needed to construct DoA estimators that can be used with rotating arrays subject to rapid variations of the signal frequency is offered by the stochastic maximum likelihood approach. Using a combination of analytic methods and Monte Carlo simulations, we show that for low and moderate source correlations the stochastic maximum likelihood estimator that assumes noncorrelated sources has accuracy comparable to the estimator that includes the correlation coefficient as one of the parameters. We propose several fast approximations of the stochastic maximum likelihood estimator and compare their accuracy with the Cramer-Rao lower bound. We also discuss the model order selection problem for the binary- and multiple-hypotheses cases.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1109/tsp.2020.3022207
- Category
- Publikacja w czasopiśmie
- Type
- artykuły w czasopismach
- Language
- angielski
- Publication year
- 2020
