A least mean square approach to buried object detection in ground penetrating radar

Ground Penetrating Radar (GPR) is one of the most popular subsurface sensing devices and has a wide range of applications, e.g., buried object detection. In this study, Least Mean Square (LMS) approach is used to solve buried object detection problem. Point of interest located in each depth location of 2D GPR signal is estimated from previous samples by using separate 1D LMS algorithms and prediction errors defined as the difference between the measured and estimated values are aggregated. If calculated error exceeded a predefined threshold, it is decided that a buried object exists at that location. The proposed approach is tested with a realistic data set simulated by using a new version of gprMax electromagnetic modeling software. The data set consists of several different soil types, objects, different burial depths and surface types. Resulting Receiver Operating Characteristic (ROC) curves demonstrate the performance of the proposed method.