NEAR-FIELD MULTIPLE SOURCE LOCALIZATION BY PASSIVE SENSOR ARRAY

A study is presented of the localization of multiple near-field sources in a spatially white Gaussian noise environment. First, we use a modified two-dimensional (2-D) version of the multiple signal classification (MUSIC) algorithm to localize the signal sources; range and bearing. Then, we consider a global-optimum maximum likelihood searching approach to localize these sources. It is shown that in the single source situation, the covariances of both the 2-D MUSIC, estimator and the maximum likelihood estimator (MLE) approach the Cramer-Rao lower bound (CRLB) as the number of snapshots increases to infinity. In the multiple source situation, we observe that for a high signal-to-noise ratio (SNR) and a large number of snapshots, the root mean square errors (RMSE's) of both localization techniques are relatively small. However, for low SNR and/or small number of snapshots, the performance of the MLE is much superior than that of the modified 2-D MUSIC.