A Source Counting Method Using Acoustic Vector Sensor Based on Sparse Modeling of DOA Histogram

Abstract

The number of sources present in a mixture is crucial information often assumed to be known or detected by source counting. The exiting methods for source counting in underdetermined blind speech separation (UBSS) suffer from the overlapping between sources with low W-disjoint orthogonality (WDO). To address this issue, we propose to fit the direction of arrival (DOA) histogram with multiple von-Mises density (VM) functions directly and form a sparse recovery problem, where all the source clusters and the sidelobes in the DOA histogram are fitted with VM functions of different spatial parameters. We also developed a formula to perform the source counting taking advantage of the values of the sparse source vector to reduce the influence of sidelobes. Experiments are carried out to evaluate the proposed source counting method and the results show that the proposed method outperforms two well-known baseline methods.

Single Snapshot DOA Estimation by Minimizing the Fraction Function in Sparse Recovery

“…Mathematical Problems in Engineering Before, we prove the equivalence between (20) and (21), some lemmas are needed. e following lemma is easy to get by the definition of ‖ • ‖ 0 , and we leave the proof to the readers.…”

“…In order to extend of application of g p (•) in sparse recovery, we consider the following models, F 0 (r)-minimization and F g p (r)-minimization. Furthermore, (20) and (21) can be treated as special cases of these two models:…”

“…□ Corollary 1. For the noiseless cases and the measurement matrix A(Ω) ∈ C M×n defined in (10), if Θ * ⊆Ω and K < ((M + 1)/2), then there exists a constant p * (A, y) such that the real solution Θ * of DOA estimation can be recovered by both model (20) and model (21) whenever 0 < p < p * (A, y).…”

“…where ‖ • ‖ 0 stands for the number of nonzero elements. In recent years, many sparse algorithms such as OMP and l 1 -minimization [18][19][20][21][22] have been applied to solve this problem. Although a lot of work has given the rationality of sparse recovery algorithms [23][24][25], these conditions not only require the measurement matrix to meet the RIP condition, but also the corresponding RIC constant to meet certain conditions.…”

RCS promotion in orbital angular momentum imaging radar with rotational antenna

“…In addition, is assumed to be uniformly distributed from to . The orthogonal matching pursuit algorithm is utilised to measure the azimuth [25, 26]. Moreover, the RMSE of the azimuth is obtained by the expectation of 10,000 simulations.…”