Three dimentional acoustic source location estimation with maximum likelihood estimation using Kalman filter

In this study, a novel algorithm based on the Maximum Likelihood Estimator (MLE) is proposed to estimate three dimensional position of an acoustic source which is located in a field with randomly deployed sensors. The received sensor signals have been filtered by using Kalman filter in order to eliminate the background noise. Filtered signals have been subjected to the estimation procedure to obtain the three dimensional source location. Consequently, the usage of Kalman filter has provided better estimation performance in accordance with the case without Kalman filter. Moreover, the performance decreases due to the larger parameter space in the procedure of estimating three dimensional location of the source. In the three dimensional estimation scenario Kalman filter drastically improves the performance of the estimator. The performances of the estimation procedures with and without Kalman filter has been investigated by plotting the estimator variances. The performance of the algorithm has been analyzed by comparing the simulation results and the Cramer Rao Bound expressions.