Fuzzy set information fusion in landmine detection

A robust method of performing information fusion in processing ground penetrating radar (GPR) sensor data in landmine detection will be described. The method involves running multiple automatic target recognition algorithms (ATRs) in parallel on the GPR data. The outputs from each of the ATRs are spatially correlated and a feature set for each potential radar target is automatically generated. The feature set is provided as input to Mamdani style fuzzy inference systems. The fuzzy inference systems' output is a mine confidence value that is evolved from the feature set input. Final mine/clutter classification decisions are based on the confidence value. The major advantage of this technique is that it provides consistent mine detection performance independent of road type, GPR hardware settings, and ATR setup parameters. This paper will first describe the individual ATRs and the process of spatially correlating target reports and generating a feature set. This will be followed by a description of the fuzzy inference systems used for target classification. The paper will conclude with test results from various Fort A.P. Hill calibration mine lanes.