Optimal source localization and tracking using arrays with uncertainties in sensor locations
Citable URI
https://hdl.handle.net/1912/6429DOI
10.1575/1912/6429Keyword
Array processorsAbstract
We develop a computationally efficient iterative algorithm for source localization and
tracking using active/passive arrays with uncertainties in sensor locations. We suppose that
the available data consist of time delay, or differential time delay, measurements of the signal
wavefront across the array. We consider a general senario in which the array uncertainties
may be correlated in time and in space. The proposed algorithm is optimal in the sense that
it converges montonically to the Maximum Likelihood (ML) estimate of the source trajectory
parameters. In the case of multiple sources, the algorithm makes an essential use of the
information available from all sources to reduce the array uncertainties (the so-called array
callibration) and thus to improve the localization accuracy of each signal source. We also derive
new expressions for the log-likelihood gradient, the Hessian, and the Fisher's information
matrix, that may be used for efficient implementation of gradient based algorithms, and for
assessing the mean square error of the resulting ML parameter estimates.
Suggested Citation
Technical Report: Segal, Mordechai, Weinstein, Ehud, "Optimal source localization and tracking using arrays with uncertainties in sensor locations", 1989-08, DOI:10.1575/1912/6429, https://hdl.handle.net/1912/6429Related items
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