PSEUDO-RANDOMLY GENERATED ESTIMATOR BANKS - A NEW TOOL FOR IMPROVING THE THRESHOLD PERFORMANCE OF DIRECTION FINDING

A new powerful tool for improving the threshold performance of directi on finding is considered. The main idea of our approach is to reduce t he number of outliers in DOA estimates using recently proposed joint e stimation strategy (JES), For this purpose, multiple different DOA est imators are calculated in a parallel manner for the same batch of data (i.e., for a single data record). Employing these estimators simultan eously, JES improves the threshold performance because it removes outl iers and exploits only ''successful'' estimators that are sorted out u sing hypothesis testing procedure. We consider an efficient modificati on of JES with application to the pseudo-randomly generated eigenstruc ture estimator banks based on second-and higher order statistics. Weig hted MUSIC estimators based on the covariance and contracted quadricov ariance matrices are chosen as appropriate underlying techniques for t he second-and fourth-order estimator banks, respectively. Computer sim ulations with uncorrelated sources verify dramatic improvements of thr eshold performance as compared with the conventional second-and fourth -order MUSIC algorithms. Simulations also show that in the second-orde r case, the threshold performance of our technique is close to that of the WSF method and stochas-tic/deterministic ML methods, which are kn own today as the most powerful tin the sense of estimation performance ) and, at the same time, as the most computationally expensive DOA est imation techniques, The computational cost of our algorithm is much lo wer than that of the WSF and ML techniques because no multidimensional optimization is required.