Bayesian Direction of Arrival Estimation with Prior Knowledge from Target Tracker

The performance of traditional direction arrival (DOA) estimation methods always deteriorates at a low signal-to-noise ratio (SNR) or without sufficient observations. This paper investigates the Bayesian DOA problem aided by prior knowledge from target tracker. Cramér–Rao lower bounds (CRLB) and expected CRLB are first derived to evaluate theoretical estimation. Based on maximum posterior (MAP) estimator in framework, two proposed. One is two-step grid search method for single case. other gradient-based iterative solution multiple targets case, which extends Newton incorporating knowledge. We also propose minimum mean square error (MMSE) using Monte Carlo method, requires trading off accuracy against computational complexity. By comparing with likelihood (ML) estimators MUSIC algorithm, proposed three improve SNR limited snapshots. Moreover, not affected correlation between sources.