Space Time MUSIC: Consistent Signal Subspace Estimation for Wideband Sensor Arrays - 2018


Wideband direction of arrival (DOA) estimation with sensor arrays is an important task in sonar, radar, acoustics, biomedical, and multimedia applications. Several state-of-the-art wideband DOA estimators coherently process frequency binned array outputs by approximate maximum likelihood (ML), weighted subspace fitting, or focusing techniques. This Project shows that bin signals obtained by filter-bank approaches do not obey the finite rank narrow-band array model, because spectral leakage and changes of the array response with frequency among the bin create ghost sources captivated with the particular realization of the supply process. Therefore, existing DOA estimators based on binning don't seem to be consistent even when the array response is perfectly known. In this Project, underneath a more realistic array model, which still has finite rank below a house-time formulation, signal subspaces at arbitrary frequencies can be consistently recovered below delicate conditions by applying house time, MUSIC-sort (ST-MUSIC) estimators to the dominant eigenvectors of the wideband, space-time sensor cross-correlation matrix. A completely unique, consistent ML-based mostly ST-MUSIC subspace estimate is developed to estimate the number of sources active at every frequency by information theoretic criteria. Empirical ST-MUSIC subspaces are fed to any subspace fitting DOA estimator at single or multiple frequencies. Simulations make sure that this approach permits higher performance over binning approaches at sufficiently high signal to noise ratio, when model mismatches exceed the noise floor.

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