Array shape calibration using eigenstructure methods

Abstract Sensor location uncertainty can severely degrade the accuracy of direction finding system. An eigenstructure based method for simultaneously estimating directions of arrival (DOA) and sensor locations is developed to alleviate this problem. The proposed technique does not require calibration sources at known positions, and can handle non-disjoint sources, (i.e., sources occupying the same frequency band and the same time interval). The procedure is guaranteed to converge and offers an alternative to the procedure presented by Weiss and Friedlander (1990). Numerical examples and Monte-Carlo simulations are used to study the performance of the proposed technique.

[1]  Wee Ser,et al.  Array shape calibration using sources in known locations , 1992, [Proceedings] Singapore ICCS/ISITA `92.

[2]  E. Polak Introduction to linear and nonlinear programming , 1973 .

[3]  T. Kailath,et al.  Spatio-temporal spectral analysis by eigenstructure methods , 1984 .

[4]  Peter M. Schultheiss,et al.  Array shape calibration using sources in unknown locations-Part II: Near-field sources and estimator implementation , 1987, IEEE Trans. Acoust. Speech Signal Process..

[5]  A. Booth Numerical Methods , 1957, Nature.

[6]  Peter M. Schultheiss,et al.  Array shape calibration using sources in unknown locations-Part I: Far-field sources , 1987, IEEE Trans. Acoust. Speech Signal Process..

[7]  James Ting-Ho Lo,et al.  Eigenstructure methods for array sensor localization , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  Ralph Otto Schmidt,et al.  A signal subspace approach to multiple emitter location and spectral estimation , 1981 .

[9]  Åke Björck,et al.  Numerical Methods , 1995, Handbook of Marine Craft Hydrodynamics and Motion Control.

[10]  Anthony J. Weiss,et al.  Array shape calibration using sources in unknown locations-a maximum likelihood approach , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.