Space Time MUSIC: Consistent Signal Subspace Estimation for Wide-band Sensor Arrays

Wide-band Direction of Arrival (DOA) estimation with sensor arrays is an essential task in sonar, radar, acoustics, biomedical and multimedia applications. Many state of the art wide-band DOA estimators coherently process frequency binned array outputs by approximate Maximum Likelihood, Weighted Subspace Fitting or focusing techniques. This paper shows that bin signals obtained by filter-bank approaches do not obey the finite rank narrow-band array model, because spectral leakage and the change of the array response with frequency within the bin create \emph{ghost sources} dependent on the particular realization of the source process. Therefore, existing DOA estimators based on binning cannot claim consistency even with the perfect knowledge of the array response. In this work, a more realistic array model with a finite length of the sensor impulse responses is assumed, which still has finite rank under a space-time formulation. It is shown that signal subspaces at arbitrary frequencies can be consistently recovered under mild conditions by applying MUSIC-type (ST-MUSIC) estimators to the dominant eigenvectors of the wide-band space-time sensor cross-correlation matrix. A novel Maximum Likelihood based ST-MUSIC subspace estimate is developed in order to recover consistency. The number of sources active at each frequency are estimated by Information Theoretic Criteria. The sample ST-MUSIC subspaces can be fed to any subspace fitting DOA estimator at single or multiple frequencies. Simulations confirm that the new technique clearly outperforms binning approaches at sufficiently high signal to noise ratio, when model mismatches exceed the noise floor.Comment: 15 pages, 10 figures. Accepted in a revised form by the IEEE Trans. on Signal Processing on 12 February 1918. @IEEE201

Focusing Operators and Tracking Moving Wideband Sources , Journal of Telecommunications and Information Technology, 2016, nr 4

In this paper, the localization of wideband source with an algorithm to track a moving source is investigated. To locate the wideband source, the estimation of two directions of arrival (DOA) of this source from two different arrays of sensors is used, and then, a recursive algorithm is applied to predict the moving source’s position. The DOA is estimated by coherent subspace methods, which use the focusing operators. Practical methods of the estimation of the coherent signal subspace are given and compared. Once the initial position is estimated, an algorithm of tracking the moving source is presented to predict its trajectory

Wideband source localization by space-time MUSIC subspace estimation

Accurate estimation of the direction of arrivals (DOAs) of multiple wideband signal sources by sensor arrays is of paramount importance in recent developments of Ultra-Wide Band (UWB) and MIMO communication systems, acoustic applications, ultrasound, beside classical radar and sonar sensing. The array model changes with frequency. Narrowband analysis is not suited for short duration and, more in general, non-stationary sources. Most existing wideband direction finding algorithms are based on sensor output channelization (frequency binning) and neglect correlations among frequency bins, intra-bin finite bandwidth effects and spectral leakage that may create ghost sources during signal subspace estimation and impair the consistency of DOA estimators at high signal to noise (SNR) ratios. In this paper, a minimum leakage MUSIC-based estimator of subband signal subspaces from the space-time array covariance is introduced. Resulting subspace estimates can be fed to any frequency domain Maximum Likelihood (ML), Weighted Subspace Fitting (WSF) or focusing algorithm for final DOA estimation. Realistic simulations demonstrate the superior performance of the new estimator in difficult environments. © 2013 University of Trieste and University of Zagreb

Journal of Telecommunications and Information Technology

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