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.

Our goal is to use a single passive moving sensor to determine the locations of multiple radio stations. The conventional method uses only direction-of-arrival (DOA) measurements, and its performance is poor when emitters are located closely in the lateral direction, even if they are not close in the range direction, or in the far field from the moving sensor, resulting in similar DOAs for several emitters. This paper proposes a new method that uses the power of the received signals as well as DOA. The received signal power is a function of the inverse of the squared distance between an emitter and the moving sensor. This has the advantage of providing additional information in the range direction; therefore, it can be used for data association as additional information when emitter ranges are different from each other. Simulations showed that the success rate of the conventional method is 73%, whereas that of the proposed method is 97%, an overall 24-percentage-point improvement. The localization error of the proposed method is also reduced to half that of the conventional method. We further investigated its performance with different emitter and sensor configurations. In all cases, the proposed method proved superior to the conventional method.