Improved techniques for detection and localization of underwater acoustic sources using acoustic vector sensors

Abstract

approximation to g m (ϕ) V′′(ϕ) : N-dimensional approximate signal subspace Q(.) : right-tail probability function of the standard normal distribution 2 D  : chi square distribution with D degrees of freedom   2 ' D  : non-central chi square distribution with D degrees of freedom, non-centrality parameter λ F D1,D2 : F-distribution with (D1, D2) degrees of freedom F′ D1, D2 (λ): noncentral F-distribution with (D1, D2) degrees of freedom, noncentrality parameter λ xx Gaussian noise. We explore how detection performance in non-Gaussian noise can be enhanced by using an SSR preprocessor and show that this provides a simple and near-optimal solution to detection in a wide range of non-Gaussian noise pdfs. Source localization algorithms in an ocean environment are severely affected by low SNR and perform poorly because they do not exploit the known fact that the environmental noise is non-Gaussian in nature. We present a method to improve the performance of azimuth estimation algorithms by combining the SSR phenomenon with conventional azimuth estimation methods. This offers better performance with relatively no increase in complexity. We also develop a MUSIC-based approach for three-dimensional localization of a single source in the near-field using a single AVS. This method yields closed-form expressions for the location estimates thus allowing 3D source localization with low computational complexity.