Dirichlet process crescent-signal mixture model for ground-penetrating radar signals

There are many pipes buried underground in urban areas. An installation of signal posts requires information about actual underground conditions in order not to crack any pipes. However, some areas do not have the accurate pipe layouts because the constructions make a gap between the layouts and the results. Using a ground-penetrating radar (GPR) is a solution for preventing damage to the pipes regardless of the accuracy of layouts. Our goal is to propose an automatic pipes-detection method using the GPR signals. The achievement provides the following two things: streamlining a survey of the underground without expert's experience and reducing a burden on the users. In this paper, we propose a new detection method based on the Dirichlet process mixture (DPM) model. This paper aims at examining the estimation accuracy of our method. First of all, the method of our previous work reduces noises of the GPR signals. Secondly, the two-dimensional Gabor wavelet (2D-GWT) is applied for the denoised signals. Next, samples are drawn from the 2D-GWT result which we regard as a probability distribution. Finally, we obtain the partition of samples by using the fixed DPM model to be proposed. We call it the Dirichlet Process Crescent-signal Mixture model. We estimate the positions of buried pipes from the partitions. Some estimated positions are close to the true ones. However, the estimated depths tend to be greater than the true ones because the relative permittivity of underground is apt to increase. We find that the constant relative permittivity is an erroneous assumption. This issue for precise estimation will be addressed in our future research.