Maximum a posteriori estimation of signal rank

Estimating the number of linearly independent signals impinging on a collection of receivers is of increasing recent interest, particularly in connection with MIMO systems for communications and sensing. This paper derives maximum a posteriori estimators for signal rank from noisy data collected at multiple receivers. Situations in which noise variance is known and unknown are both treated. These estimators are shown to significantly outperform maximum-likelihood/BIC rank estimators.