This paper addresses the problem of broadband spatial spectrum estimation using multiple spatially-aliased arrays. Unlike previous approaches using sparse arrays, the signals here are assumed to be uncorrelated between multiple arrays which, in fact, may be receiving the same source during different time intervals. This paper presents an approach which jointly exploits spatial-orientation and broadband temporal diversity in order to estimate the spatial spectrum even when the inter-element spacing within each array is greater than a half-wavelength. A dynamical model for the spatial spectrum is employed to formulate a maximum likelihood estimate, which is computed via a recursive version of the expectation-maximization algorithm using data from different arrays. Simulation results are presented to demonstrate the ability of the method to suppress spatial grating lobes and increase low SNR target detection in an interference dominated environment. © 2012 IEEE.