Source localization and sensing: a nonparametric iterative adaptive approach based on weighted least squares
Name
Yardibi-2010-Source localization and sensing a nonparametric iterative adaptive approach based on weighted least squares.pdf
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3.02 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Yardibi, Tarik
Li, Jian
Stoica, Petre
Xue, Ming
Baggeroer, Arthur B.
Date Issued
February 2010
Journal
IEEE Transactions on Aerospace and Electronic Systems
Publisher
Institute of Electrical and Electronics Engineers
Citation
Yardibi, T. et al. “Source Localization and Sensing: A Nonparametric Iterative Adaptive Approach Based on Weighted Least Squares.” Aerospace and Electronic Systems, IEEE Transactions on 46.1 (2010): 425-443. © 2010 Institute of Electrical and Electronics Engineers.
Version
Final published version
Abstract
Array processing is widely used in sensing applications for estimating the locations and waveforms of the sources in a given field. In the absence of a large number of snapshots, which is the case in numerous practical applications, such as underwater array processing, it becomes challenging to estimate the source parameters accurately. This paper presents a nonparametric and hyperparameter, free-weighted, least squares-based iterative adaptive approach for amplitude and phase estimation (IAA-APES) in array processing. IAA-APES can work well with few snapshots (even one), uncorrelated, partially correlated, and coherent sources, and arbitrary array geometries. IAA-APES is extended to give sparse results via a model-order selection tool, the Bayesian information criterion (BIC). Moreover, it is shown that further improvements in resolution and accuracy can be achieved by applying the parametric relaxation-based cyclic approach (RELAX) to refine the IAA-APES&BIC estimates if desired. IAA-APES can also be applied to active sensing applications, including single-input single-output (SISO) radar/sonar range-Doppler imaging and multi-input single-output (MISO) channel estimation for communications. Simulation results are presented to evaluate the performance of IAA-APES for all of these applications, and IAA-APES is shown to outperform a number of existing approaches.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TAES.2010.5417172
