Removing the outliers in root-MUSIC via pseudo-noise resampling and conventional beamformer

Two-step reliability test based unitary root-MUSIC for direction-of-arrival estimation

“…The key of the proposed method is to test the following hypothesis H for each DOA estimator [15][16][17][18], which enables us to separate the normal and abnormal DOA estimators.…”

“…Although this technique provides a considerable threshold improvement for the MUSIC algorithm, it seems to be too specific in that it cannot be applied in the same way to a variety of existing DOA estimation techniques. Motivated by the success of modern resampling schemes (e.g., bootstrap [12,13]), pseudo-noise resampling (PR) technique has been presented in [14][15][16][17][18]. Its underlying idea is to utilize synthetically generated pseudo-noise to perturb the original noise in such a way that the outliers will be removed.…”

“…Nevertheless, it cannot eliminate the effect caused by those DOA estimates deviated far away from the true DOAs. Vasylyshyn [16] has proposed a variant of the PR based root-MUSIC algorithm which combines the PR technique with the conventional [17]. The main contribution of [17] is the distance detection strategy (DDS), which is used to determine the final DOA estimates when all the DOA estimators fail to pass the reliability test.…”

“…The existing PR based algorithms such as [14][15][16][17][18] have a common drawback that as long as there is an unsuccessful DOA estimate in the DOA estimator, the successful DOA estimates will be regarded as the unsuccessful one and they will be ruled out along with the whole estimator. Here, the successful DOA estimate denotes the DOA estimate localized within its DOA sector, while the unsuccessful DOA estimate is out of its DOA sector.…”

“…Here, P is the number of sources. After PR processing, unlike the methods in [14][15][16][17], we propose the TSRT to help select successful DOA estimates. After dividing the DOA estimator bank into P DOA subsets, with each subset corresponding to a common DOA, the TSRTs between those subsets are statistically independent, which allows us to use fewer PR runs to collect enough successful DOA estimates that can be used to determine the final DOA estimates.…”