An efficient method based on machine learning for estimation of the wall parameters in through-the-wall imaging

ABSTRACT

The estimation of the wall parameters is important in through-the-wall radar imaging (TWRI). Ambiguities in the wall characteristics, including wall thickness, permittivity, and conductivity, will distort the imaging and shift the target position. To obtain a quick and accurate estimation of wall parameters, an efficient method based on machine learning is proposed. The estimation problem is converted to a regression problem. A map between wall parameters and the received signals is established and is regressed as a linear formulation after machine learning; in this manner, the wall parameters can be estimated in few seconds. The measurement results demonstrate that the estimated approach has the advantages of high precision and low computational time. The influence of the size, the location, the number of the targets and the length of the wall, the sampling interval, and noise on the estimation problems is discussed, and the image entropy is given to verify the effectiveness of the estimation values. The results based on support vector machines and least-square support vector machines (LS-SVMs), which are both machine-learning approaches, are compared. The comparison results reveal that the LS-SVM-based method can provide comparable performances in terms of accuracy and convenience but poor performances in terms of generalization and robustness.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 61372045]; State Key Laboratory of Millimeter Waves, Southeast University [grant number K201616]; University Science Research Project of Jiangsu Province [grant number TJ213012]; the Scientific Research Foundation of Nanjing University of Posts and Telecommunications [grant number NY215165].