ACTIVE ARRAY SENSOR LOCALIZATION

Sensor array processing algorithms for source localization often requi re precise knowledge of the sensor locations, which is often not avail able in practice. Errors in the sensor locations are known to degrade severely the performance of these algorithms. This paper proposes iter ative damped Gauss-Newton maximum likelihood array sensor localization algorithms using known calibration source signals, Cramer-Rao bound ( CRB) expressions for the sensor location parameters are found for this problem. Two calibration source signal models are considered. One ass umes that each signal waveform is known up to a scaling constant and p hase, while the other requires complete knowledge of the signal wavefo rm. For uncorrelated, completely known calibration signals, a necessar y and sufficient condition for the optimal choice of the source bearin gs that minimizes the sensor position CRB variances is presented. Nume rical results illustrate that the maximum likelihood calibration algor ithms attain the CRB asymptotically.