Performance of near-field localization algorithms based on high-order statistics

Fourth-order statistics based methods are proposed for estimating the range and bearing of multiple near-field sources. The performance of the maximum likelihood estimator based on the asymptotic distribution of the sample trispectrum is studied and it is shown that the proposed estimator has lower variance when compared to the Cramer-Rao bound. This suggests that under high SNR, and with nonGaussian signals, the nonlinear HOS based method provides better performance than techniques that employ either correlations or spectra. Simulations are provided to illustrate the theoretical results.