Three-dimensional localization of multiple acoustic sources in shallow ocean with non-Gaussian noise

• Published in: Digital signal processing 2014-09, Vol.32, p.85-99
• Main Authors: Madadi, Z., Anand, G.V., Premkumar, A.B.
• Format: Article
• Language: English
• Subjects: 3-D multiple source localization; Algorithms; Cramér–Rao bound; Generalized expectation maximization (SAGE); Hybrid array; Localization; Mathematical models; Noise; Non-Gaussian noise; Oceans; Position (location); Searching; Shallow ocean; Three dimensional
• ISSN: 1051-2004; 1095-4333
• DOI: 10.1016/j.dsp.2014.05.002

In this paper, a low-complexity algorithm SAGE-USL is presented for 3-dimensional (3-D) localization of multiple acoustic sources in a shallow ocean with non-Gaussian ambient noise, using a vertical and a horizontal linear array of sensors. In the proposed method, noise is modeled as a Gaussian mixture. Initial estimates of the unknown parameters (source coordinates, signal waveforms and noise parameters) are obtained by known/conventional methods, and a generalized expectation maximization algorithm is used to update the initial estimates iteratively. Simulation results indicate that convergence is reached in a small number of (≤10) iterations. Initialization requires one 2-D search and one 1-D search, and the iterative updates require a sequence of 1-D searches. Therefore the computational complexity of the SAGE-USL algorithm is lower than that of conventional techniques such as 3-D MUSIC by several orders of magnitude. We also derive the Cramér–Rao Bound (CRB) for 3-D localization of multiple sources in a range-independent ocean. Simulation results are presented to show that the root-mean-square localization errors of SAGE-USL are close to the corresponding CRBs and significantly lower than those of 3-D MUSIC. •3-dimensional source localization in ocean with non-Gaussian noise.•Noise is modeled as a Gaussian mixture with unknown parameters.•Iterative updating of initial parameter estimates using EM based algorithm.•Convergence achieved in a small number of iterations.•Significantly lower errors and complexity compared to existing methods.