Localization of Near-Field Signals Based on Linear Prediction and Oblique Projection Operator. Liu, W., Zuo, W., Xin, J., Zheng, N., & Sano, A. In 2018 26th European Signal Processing Conference (EUSIPCO), pages 341-345, Sep., 2018.
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Paper doi abstract bibtex
Paper doi abstract bibtex Recently many subspace-based localization methods were developed for estimating the directions of arrivals (DOAs) and ranges of multiple narrowband signals in near-field. However, most of them usually encounter “saturation behavior” in estimation performance regardless of the signal-to-noise ratio (SNR) when the number of array snapshots is not sufficiently large enough. In this paper, we investigate the problem of localizing multiple narrowband near-field signals impinging on a symmetrical uniform linear array (ULA). Firstly, by exploiting the anti-diagonal elements of the array covariance matrix, a new linear prediction approach with truncated singular value decomposition (SVD) is proposed to estimate the location parameters (i.e., DOA and range) of the incident signals. Secondly, as a measure against the impact of finite array data, an alternating iterative scheme is presented to improve the estimation accuracy of the location parameters, where the “saturation behavior” encountered in most of localization methods is solved effectively. Furthermore, the statistical analysis of the proposed method is studied, and the asymptotic mean-squared-error (MSE) expressions of the estimation errors are derived for two location parameters. Finally, the effectiveness and the theoretical analysis are substantiated through numerical examples.
@InProceedings{8553601,
author = {W. Liu and W. Zuo and J. Xin and N. Zheng and A. Sano},
booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
title = {Localization of Near-Field Signals Based on Linear Prediction and Oblique Projection Operator},
year = {2018},
pages = {341-345},
abstract = {Recently many subspace-based localization methods were developed for estimating the directions of arrivals (DOAs) and ranges of multiple narrowband signals in near-field. However, most of them usually encounter “saturation behavior” in estimation performance regardless of the signal-to-noise ratio (SNR) when the number of array snapshots is not sufficiently large enough. In this paper, we investigate the problem of localizing multiple narrowband near-field signals impinging on a symmetrical uniform linear array (ULA). Firstly, by exploiting the anti-diagonal elements of the array covariance matrix, a new linear prediction approach with truncated singular value decomposition (SVD) is proposed to estimate the location parameters (i.e., DOA and range) of the incident signals. Secondly, as a measure against the impact of finite array data, an alternating iterative scheme is presented to improve the estimation accuracy of the location parameters, where the “saturation behavior” encountered in most of localization methods is solved effectively. Furthermore, the statistical analysis of the proposed method is studied, and the asymptotic mean-squared-error (MSE) expressions of the estimation errors are derived for two location parameters. Finally, the effectiveness and the theoretical analysis are substantiated through numerical examples.},
keywords = {array signal processing;covariance matrices;direction-of-arrival estimation;iterative methods;mean square error methods;singular value decomposition;statistical analysis;oblique projection operator;subspace-based localization methods;DOA;multiple narrowband signals;saturation behavior;estimation performance;signal-to-noise ratio;array snapshots;multiple narrowband near-field signals;symmetrical uniform linear array;anti-diagonal elements;array covariance matrix;linear prediction approach;truncated singular value decomposition;location parameters;incident signals;finite array data;alternating iterative scheme;estimation accuracy;estimation errors;Arrays;Direction-of-arrival estimation;Signal to noise ratio;Covariance matrices;Narrowband;Estimation;Sensors},
doi = {10.23919/EUSIPCO.2018.8553601},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437077.pdf},
}Downloads: 0
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However, most of them usually encounter “saturation behavior” in estimation performance regardless of the signal-to-noise ratio (SNR) when the number of array snapshots is not sufficiently large enough. In this paper, we investigate the problem of localizing multiple narrowband near-field signals impinging on a symmetrical uniform linear array (ULA). Firstly, by exploiting the anti-diagonal elements of the array covariance matrix, a new linear prediction approach with truncated singular value decomposition (SVD) is proposed to estimate the location parameters (i.e., DOA and range) of the incident signals. Secondly, as a measure against the impact of finite array data, an alternating iterative scheme is presented to improve the estimation accuracy of the location parameters, where the “saturation behavior” encountered in most of localization methods is solved effectively. 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