Localization of Acoustic Emitters with Time Delay Compensated ML

A novel and efficient approach for finding the Maximum Likelihood (ML) estimates of broadband emitter signals observed via a passive sensor network is presented. The proposed ML solution is based on the delayed observation signal model for the source signal at individual sensor nodes. The approach provides localization of the broadband source location parameters in time domain, The sensors are often randomly deployed in a field; consequently, each time delay measurement is different from every other, depending on the sensor network configuration. Hence, the time delays in particular should be compensated in the corresponding algorithm when processing the broadband source signal in the time domain. In this paper, the observation delays (time shifts) are considered in the ML solution derived herein and also in the corresponding covariance matrix. The Cramer Rao Bound (CRB) expressions are also derived to explore the performance of the time compensated ML (TCML), The simulation results demonstrate that the proposed approach is suitable for broadband source location estimation.