We developed an automatic data processing algorithm for a ground-probing radar which is essential in analyzing a large amount of data by a non-expert. Its aim is to obtain an optimum result that the conventional technique can give, without the assistance of an experienced operator. The algorithm is general except that it postulates the existence of at least one isolated target in the radar image. The raw images of underground objects are compressed in the vertical and the horizontal directions by using a pulse-compression filter and the aperture synthesis technique, respectively. The test function needed to configure the compression filter is automatically selected from the given image. The sensitivity of the compression filter is adjusted to minimize the magnitude of spurious responses. The propagation velocity needed to perform the aperture synthesis is determined by fitting a hyperbola to the selected echo trace. We verified the algorithm by applying it to the data obtained at two test sites with different magnitude of clutter echoes.

Radar imaging technique is one of the most powerful tool for underground detection. However, performance of conventional methods is not sufficiently high when the observational direction or the aperture size is restricted. In the present paper, an image reconstruction method based on a model fitting with nonlinear least-squares has been developed, which is applicable to arbitrarily arranged arrays. Reconstruction is executed on the assumption that targets consist of discrete point scatterers embedded in a homogeneous medium. Model fitting is iterated as the number of point target in the assumed model is increased, until the residual in fitting becomes unchanged or small enough. A penalty function is used in nonlinear least-squares to make the algorithm stable. Fundamental characteristics of the method revealed with computer simulation are described. This method focuses a much sharper image than that obtained by the conventional aperture synthesis technique.