No abstract

Strong front wall clutter has serious impacts on the target detection and imaging in the through-wall radar (TWR) system. A method of robust wall clutter suppression based on the entropy of an expanded antenna source for ultra-wide-band through-wall radar is presented in this paper. The model of TWR scenario consists of four layers. Assume that the first and the third layers are air space, while the second and the fourth layers are composed of uniform flat concrete wall. The circular target, assumed to be a perfect electric conductor, is located in the third layer. Along the measurement line which is parallel to the front wall, the transceiver antenna scans uniformly. The echo signals that come from the target and walls are processed into discrete data at first, so that the calculation of probability space is subsequently implemented and the discrete data are expanded as well. And then the entropy of the expanded data that contain robust wall clutter and echo of target is calculated. Taking into consideration the amplitude of target signal varying in each scan, while that of clutter signal is not, it is evident that the entropy can be utilized to discriminate the signals between the target and wall. According to the difference between the entropy of the wall clutter and that of the target, a certain threshold can be set and the optimum tolerance threshold is adaptively selected on the basis of target-to-clutter ratio. With the optimum tolerance threshold, process of clutter suppression is conducted. Finally, back projection is employed for imaging of target. In this paper, data of through-wall radar for simulation are provided by GprMax2D/3D, based on the finite difference-time domain methsd. The clutter suppression and imaging are separately conducted by the method based on data entropy and the method proposed in this paper. Comparing the results of simulations, it is shown that the gain of target-to-clutter ratio for the former is 15.51 dB, and that for the latter is 19.74 dB. It is obvious that the proposed method can provide imaging with higher quality for the same measurement, and it requires fewer scans with the same quality of imaging as well. Computational complexity of the proposed method and the method based on entropy can be expressed as O(M NL) and O(M N) , respectively