Abstract:

The Nyquist sampling theorem must be satisfied in traditional data acquisition of the ground penetrating radar (GPR), which degrades the imaging efficiency dramatically. However, the theory of compressive sensing (CS) shows that sparse signals can be precisely reconstructed by solving a convex l₁ minimization problem at a rate significantly below the Nyquist rate, and it can overcome the shortcomings of traditional data acquisition. The CS theory is applied into the GPR imaging, and the effects on imaging results caused by the dimension of measurement matrix, signal to noise ratio (SNR), incomplete data and compactness of targets are analyzed systematically through the simulated data. Experimental results show that compared with the traditional GPR imaging algorithm, the proposed algorithm has higher precision and fewer false alarms. This algorithm is also robust to noise and incomplete data, and saves the resources of data storage and acquisition.