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| 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
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A Through-wall Target Location Algorithm Combing Hough Transform and SVR in Multi-view Detection Mode
DOI: 10.12000/JR23236
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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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References
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- Figure 1. Traditional Doppler radar target detection model
- Figure 2. Doppler through-wall radar localization schematic
- Figure 3. Multi-view fusion through-wall target detection model
- Figure 4. BPNN structure diagram
- Figure 5. Flowchart of Hough transform instantaneous frequency estimation algorithm based on Chebyshev’s multi-interpolation term formulation and SVR-BPNN based wall thickness estimation and motion trajectory compensation algorithm
- Figure 6. The target motion trajectories used for training and testing
- Figure 7. The experimental equipment and scenarios
- Figure 8. The estimated results of wall thickness in the simulated experiments
- Figure 9. Target frequency estimation and localization results in no wall scenes
- Figure 10. The localization results of targets behind the wall