A robust Bayesian approach to target detection applied to explosive threat detection in handheld ground penetrating radar data

Target detection algorithms for ground penetrating radar (GPR) data typically calculate local statistics for the background data surrounding a test sample as a means to assess changes in the data from background. To ensure that the local statistics are indicative of only the background data and not the data due to a potential target, a guard-band is employed to prohibit the data near the test sample from being used in the calculations. The selection of the guard-band can greatly impact performance, and the value chosen should be based on the expected size of a target response, which is a challenging task when the target population varies greatly. This work develops a robust Bayesian approach to target detection that does not require selection of a guard-band. By modeling the data using Students t distribution rather than a Gaussian distribution, an inference algorithm is developed that automatically identifies outliers from the background and excludes them while calculating the local statistics. The algorithm that was developed is applied to handheld GPR data, where it is shown to provide improved performance over any particular selection of a guard-band. © 2014 SPIE.