Feature extraction is of significant importance for final results of the through-wall detection procedure. High resolution range profile (HRRP) is related to target reflectivity coefficients which can be used as a new feature for object detection. Compressive sensing (CS) is an emerging technique which enables a sparse signal to be recovered using much fewer measurements. This method can provide a novel way for achieving the HRRP since the target reflectivity coefficients are often known to be sparsely distributed in range cells. In this paper, after a set of input-output patterns that consist of target position and HRRP are obtained, through-wall detection problem is reformulated into a nonlinear regression one, which can be solved by support vector machine (SVM). Numerical simulations demonstrate that the prediction accuracy of target position is related to the number of range cells, the number of observations, and signal-to-noise ratio (SNR). Furthermore, the proposed method performs better than the one using signal amplitude as a feature in terms of smaller estimation error and shows better robustness against noise.
2. Lu, B. Y., Q. Song, Z. M. Zhou, and X. Zhang, "Detection of human beings in motion behind the wall using SAR interferogram," IEEE Geoscience and Remote Sensing Letters, Vol. 9, 968-971, Sep. 2012.
3. Li, H. Q., G. L. Cui, L. J. Kong, G. H. Chen, M. Y. Wang, and S. S. Guo, "Robust human targets tracking for MIMO through-wall radar via multi-algorithm fusion," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 12, 1154-1164, Apr. 2019.
4. Guo, S. S., G. L. Cui, L. J. Kong, and X. B. Yang, "An imaging dictionary based multipath suppression algorithm for through-wall radar imaging," IEEE Transactions on Aerospace and Electronic Systems, Vol. 54, 269-283, Feb. 2018.
5. Qu, L. L., X. Cheng, Y. P. Sun, and T. H. Yang, "Compressive sensing-based two-dimensional diffraction tomographic algorithm for through-the-wall radar imaging," 2018 Progress In Electromagnetics Research Symposium (PIERS - Toyama), 2384-2388, Japan, Aug. 1-4, 2018.
6. Wang, F. F., Y. R. Zhang, and H. M. Zhang, "Joint wall clutter mitigation and data-driven model for through-wall detection," International Journal of Remote Sensing, Vol. 37, 4486-4499, 2016.
7. Wang, F. F., Y. R. Zhang, and H. M. Zhang, "Through-wall detection with LS-SVMunder unknown wall characteristics," International Journal of Antennas and Propagation, 2016.
8. Du, L., P. H. Wang, H. W. Liu, M. Pan, F. Chen, and Z. Bao, "Bayesian spatiotemporal multitask learning for radar HRRP target recognition," IEEE Transactions on Signal Processing, Vol. 59, 3182-3196, Jul. 2011.
9. Du, C., B. Chen, B. Xu, D. D. Guo, and H. W. Liu, "Factorized discriminative conditional variational auto-encoder for radar HRRP target recognition," Signal Processing, Vol. 158, 176-189, May 2019.
10. Zheng, C., G. Li, X. G. Xia, and X. Wang, "Weighted l(2,1) minimisation for high resolution range profile with stepped frequency radar," Electronics Letters, Vol. 48, 1155-U190, Aug. 30, 2012.
11. Li, H. T., C. Y. Wang, K. Wang, Y. P. He, and X. H. Zhu, "High resolution range profile of compressive sensing radar with low computational complexity," IET Radar Sonar and Navigation, Vol. 9, 984-990, Oct. 2015.
12. Yang, L., L. F. Zhao, S. Zhou, and G. A. Bi, "Sparsity-driven SAR imaging for highly maneuvering ground target by the combination of time-frequency analysis and parametric Bayesian learning," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10, 1443-1455, Apr. 2017.
13. Baraniuk, R. G., "Compressive sensing," IEEE Signal Processing Magazine, Vol. 24, 118, Jul. 2007.
14. Candes, E. J. and M. B. Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, Vol. 25, 21-30, Mar. 2008.
15. Tropp, J. A. and A. C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit," IEEE Transactions on Information Theory, Vol. 53, 4655-4666, Dec. 2007.