Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/106676
Title: | A decoupled approach for near-field source localization using a single acoustic vector sensor | Authors: | Hari, V. N. Premkumar, A. B. Zhong, X. |
Keywords: | DRNTU::Engineering::Computer science and engineering | Issue Date: | 2012 | Source: | Hari, V. N., Premkumar, A. B., & Zhong, X. (2013). A Decoupled Approach for Near-Field Source Localization Using a Single Acoustic Vector Sensor. Circuits, Systems, and Signal Processing, 32(2), 843-859. | Series/Report no.: | Circuits, systems, and signal processing | Abstract: | This paper considers the problem of three-dimensional (3-D, azimuth, elevation, and range) localization of a single source in the near-field using a single acoustic vector sensor (AVS). The existing multiple signal classification (MUSIC) or maximum likelihood estimation (MLE) methods, which require a 3-D search over the location parameter space, are computationally very expensive. A computationally simple method previously developed by Wu and Wong (IEEE Trans. Aerosp. Electron. Syst. 48(1):159–169, 2012), which we refer to as Eigen-value decomposition and Received Signal strength Indicator-based method (Eigen-RSSI), was able to estimate 3-D location parameters of a single source efficiently. However, it can only be applied to an extended AVS which consists of a pressure sensor separated from the velocity sensors by a certain distance. In this paper, we propose a uni-AVS MUSIC (U-MUSIC) approach for 3-D location parameter estimation based on a compact AVS structure. We decouple the 3-D localization problem into step-by-step estimation of azimuth, elevation, and range and derive closed-form solutions for these parameter estimates by which a complex 3-D search for the parameters can be avoided. We show that the proposed approach outperforms the existing Eigen-RSSI method when the sensor system is required to be mounted in a confined space. | URI: | https://hdl.handle.net/10356/106676 http://hdl.handle.net/10220/16641 |
DOI: | 10.1007/s00034-012-9508-9 | Schools: | School of Computer Engineering | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | SCSE Journal Articles |
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