2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPTM-6.4
Paper Title Grid Optimization for Matrix-based Source Localization under Inhomogeneous Sensor Topology
Authors Hao Sun, Junting Chen, The Chinese University of Hong Kong, Shenzhen, China
SessionSPTM-6: Sampling, Multirate Signal Processing and Digital Signal Processing 2
LocationGather.Town
Session Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Time:Tuesday, 08 June, 16:30 - 17:15
Presentation Poster
Topic Signal Processing Theory and Methods: [SMDSP] Sampling, Multirate Signal Processing and Digital Signal Processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Herein, the problem of non-parametric source localization based on signal strength measured at different sensor locations is examined. A recently developed matrix-based method is considered. This method first arranges the measurements into an observation matrix based on a uniform grid defined in the target area and the sensor locations, and then exploits sparse matrix processing techniques to localize the source. This paper finds that the localization performance degrades when the spatial pattern of the sensors is highly non-uniform, and the uniform grid formation is only a suboptimal solution. Rather, the grid should be optimized according to the specific sensor topology. With the insight from the Cramer-Rao bound (CRB) analysis of matrix completion, a clustering problem is formulated to optimize the grid. It is demonstrated that with grid optimization, both the matrix completion and the source localization performance can be significantly improved. The proposed strategy is robust under inhomogeneous sensor topology and substantially outperforms weighted centroid localization (WCL) algorithms.