Adaptive bearing estimation and tracking of multiple targets in a realistic passive sonar scenario

Details Adaptive bearing estimation and tracking of multiple targets in a realistic passive sonar scenario Adaptive bearing estimation and tracking of multiple targets in a realistic passive sonar scenario

Jun 1997

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997spie.3086..139r&link_type=abstract

Proc. SPIE Vol. 3086, p. 139-150, Acquisition, Tracking, and Pointing XI, Michael K. Masten; Larry A. Stockum; Eds.

Statistics

Computation

Scientific paper

In a realistic passive sonar environment, the received signal consists of multipath arrivals from closely separated moving targets. The signals are contaminated by spatially correlated noise. The differential MUSIC has been proposed to estimate the DOAs in such a scenario. This method estimates the 'noise subspace' in order to estimate the DOAs. However, the 'noise subspace' estimate has to be updated as and when new data become available. In order to save the computational costs, a new adaptive noise subspace estimation algorithm is proposed in this paper. The salient features of the proposed algorithm are: (1) Noise subspace estimation is done by QR decomposition of the difference matrix which is formed from the data covariance matrix. Thus, as compared to standard eigen-decomposition based methods which require O(N3) computations, the proposed method requires only O(N2) computations. (2) Noise subspace is updated by updating the QR decomposition. (3) The proposed algorithm works in a realistic sonar environment. In the second part of the paper, the estimated bearing values are used to track multiple targets. In order to achieve this, the nonlinear system/linear measurement extended Kalman filtering proposed is applied. Computer simulation results are also presented to support the theory.

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