Volume 13 Issue 4
Aug.  2024
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Article Navigation >  Journal of Radars  > 2024  >  13(4): 838-851
Zhao Junxiang, Liang Xingdong, Li Yanlei. Change Detection in SAR CCD Based on the Likelihood Change Statistics[J]. Journal of Radars, 2017, 6(2): 186-194. doi: 10.12000/JR16065
Citation: OUYANG Fangping, CAO Jiaxuan, and DING Yipeng. A through-wall target location algorithm combing Hough transform and SVR in multi-view detection mode[J]. Journal of Radars, 2024, 13(4): 838–851. doi: 10.12000/JR23236
shu

A Through-wall Target Location Algorithm Combing Hough Transform and SVR in Multi-view Detection Mode

DOI: 10.12000/JR23236
  • 1.

    School of Physics, Central South University, Changsha 410012, China

  • 2.

    School of Electronic Information, Central South University, Changsha 410004, China

Funds:  The Natural Science Foundation of Hunan Province (2022JJ30749), The Fundamental Research Funds for the Central Universities of Central South University (2023ZZTS0398), The National Natural Science Foundation of China (52073308) and the Special Foundation for Hunan Innovation Province Construction (2020RC3004)
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  • Corresponding author: DING Yipeng, dingyipeng@sina.com
  • Received Date: 2023-11-30
  • Rev Recd Date: 2024-01-21
    • Available Online: 2024-01-23
  • Publish Date: 2024-01-31
    Abstract
  • Doppler through-wall radar faces two challenges when locating targets concealed behind walls: (1) precisely determining the instantaneous frequency of the target within the frequency aliasing region and (2) reducing the impact of the wall on positioning by determining accurate wall parameters. To address these issues, this paper introduces a target localization algorithm that combines the Hough transform and support vector regression-BP neural network. First, a multiview fusion model framework is proposed for through-wall target detection, which enables the auxiliary estimation of wall parameter information by acquiring target positions from different perspectives. Second, a high-precision extraction and estimation algorithm for the instantaneous frequency curve of the target is proposed by combining the differential evolutionary algorithm and Chebyshev interpolation polynomials. Finally, a target motion trajectory compensation algorithm based on the Back Propagation (BP) neural network is proposed using the estimated wall parameter information, which suppresses the distorting effect of obstacles on target localization results and achieves the accurate localization of the target behind a wall. Experimental results indicate that compared with the conventional short-time Fourier method, the developed algorithm can accurately extract target instantaneous frequency curves within the time-frequency aliasing region. Moreover, it successfully reduces the impact caused by walls, facilitating the precise localization of multiple targets behind walls, and the overall localization accuracy is improved ～85%.

     

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