earticle

영어

Compressed sensing (CS) provides a new solution for the problems of requiring large amount of measurements data and long data acquisition time in radar application, and both issues also exist in ground penetrating radar (GPR). Aiming at this problem, we adopt impulse radar with CS framework, and transform the GPR imaging into sparse constraint optimization problem performed on time-domain sub-sampling in this paper. Specifically, it focuses on the impulse GPR imaging method based on CS under double underground targets condition containing noise and abundant clutter. Furthermore, the performance of matching reconstruction algorithms under the different signal to noise ratios (SNR), measurement dimensions and sparseness values is also presented. The experimental results show that CS algorithms based on matching reconstruction can obviously reduce measurement data, improve the image quality and make a better anti-noise performance. When SNR of measurement data is 1dB, the probability of accurate imaging can still reach 95%. So we may reasonably conclude that the regularized orthogonal matching pursuit algorithm has a better performance than the other matching algorithms.