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 direction finding is considered. The main idea of our approach is to reduce the number of outliers in the DOA estimates using a previously proposed joint estimation strategy (JES). For this purpose, multiple different DOA estimators are calculated in a parallel manner for the same batch of data (i.e. for a single data record). Employing these estimators simultaneously, the JES improves the threshold performance because it removes outliers and exploits only “successful” estimators that are sorted out using a hypothesis testing procedure. We consider an efficient modification of the JES with application to the pseudo-randomly generated eigenstructure estimator banks based on secondand higher order statistics. Weighted MUSIC estimators based on the covariance and contracted quadricovariance matrices are chosen as appropriate underlying techniques for the second- and fourth-order estimator banks, respectively. Computer simulations with uncorrelated sources verify the dramatic improvements of threshold performance as compared with the conventional second- and fourth-order MUSIC algorithms. Simulations also show that in the second-order case, the threshold performance of our technique is close to that of the WSF method and stochastic/deterministic ML methods, which are known today as the most powerful (in the sense of estimation performance) and, at the same time, as the most computationally expensive DOA estimation techniques. The computational cost of our algorithm is much lower than that of the WSF and ML techniques because no multidimensional optimization is required