Direct Localization of Multiple Sources by Partly Calibrated Arrays

TL;DR

This paper introduces new methods for localizing multiple sources using partly calibrated arrays, balancing performance and computational efficiency, including relaxed maximum likelihood and MUSIC-like solutions.

Contribution

The paper proposes novel localization algorithms for partly calibrated arrays, reducing computational complexity while maintaining accuracy, and compares their performance through simulations.

Findings

Relaxed maximum likelihood solution with eigen-decomposition.

Approximate method eliminating eigen-decomposition at each location.

Simpler MUSIC-like and MVDR-like solutions with comparable performance.

Abstract

We present novel solutions to the problem of direct localization of multiple narrow-band and arbitrarily correlated sources by partly calibrated arrays, i.e., arrays composed of fully calibrated sub-arrays yet lacking inter-array calibration. The solutions presented vary in their performance and computational complexity. We present first a relaxed maximum likelihood solution whose concentrated likelihood involves only the unknown locations of the sources and requires an eigen-decomposition of the array covariance matrix at every potential location. To reduce the computational load, we introduce an approximation which eliminates the need for such an eigen-decomposition at every potential location. To further reduce the computational load, novel MUSIC-like and MVDR-like solutions are presented which are computationally much simpler than the existing solutions. The performance of these solutions is evaluated and compared via simulations.