Image and Signal Processing Methods

Modified linear predictive coding approach for moving target tracking by Doppler radar

[+] Author Affiliations
Yipeng Ding, Xiaoyi Lin, Ke-Hui Sun, Xue-Mei Xu

Central South University, School of Physics and Electronics, No. 405, Xiaoxiang Middle Road, Yuelu Zone, Changsha 410083, China

Xi-Yao Liu

Central South University, School of Information Science and Engineering, No. 405, Xiaoxiang Middle Road, Yuelu Zone, Changsha 410083, China

J. Appl. Remote Sens. 10(3), 035020 (Sep 08, 2016). doi:10.1117/1.JRS.10.035020
History: Received June 1, 2016; Accepted August 18, 2016
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Abstract.  Doppler radar is a cost-effective tool for moving target tracking, which can support a large range of civilian and military applications. A modified linear predictive coding (LPC) approach is proposed to increase the target localization accuracy of the Doppler radar. Based on the time-frequency analysis of the received echo, the proposed approach first real-time estimates the noise statistical parameters and constructs an adaptive filter to intelligently suppress the noise interference. Then, a linear predictive model is applied to extend the available data, which can help improve the resolution of the target localization result. Compared with the traditional LPC method, which empirically decides the extension data length, the proposed approach develops an error array to evaluate the prediction accuracy and thus, adjust the optimum extension data length intelligently. Finally, the prediction error array is superimposed with the predictor output to correct the prediction error. A series of experiments are conducted to illustrate the validity and performance of the proposed techniques.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Yipeng Ding ; Xiaoyi Lin ; Ke-Hui Sun ; Xue-Mei Xu and Xi-Yao Liu
"Modified linear predictive coding approach for moving target tracking by Doppler radar", J. Appl. Remote Sens. 10(3), 035020 (Sep 08, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.035020


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