Improving UXO/Clutter Discrimination Performance Through Adaptive Processing

Many cart- and vehicular-based unexploded ordnance UXO detection systems employ global positioning system GPS receivers to accurately determine the system s position. However, the unevenness of the terrain often causes the system to tilt during the data collection, introducing errors in the GPS measurements. In this work, three approaches are considered to correct the errors in the GPS measurements caused by the tilting of the system low-pass filtering LPF, linear predictive filtering, and adaptive filtering using a hidden Markov model HMM. The LPF smooths the data collection path recorded by the GPS receiver. Although this filter does not explicitly model the system motion, it does remove dramatic, and unrealistic, jumps in the GPS measurements. In contrast, the movement of the system can be explicitly modeled by an HMM. The HMM characterizes the cart motion so that the subsequent filtering is appropriate for the type of motion encountered. The error correction techniques are first applied to simulated data, in which both the sources of error and the ground truth are known so that the performance of the algorithms can be compared. The algorithms are then applied to measured data collected with a cart-based system to evaluate the robustness of their performance. Although the HMM approach is found to represent an improvement over the LPF, robustness issues remain with this approach mainly through ambiguities in the cart orientation. A quaternion formulation is therefore proposed as a possible efficient means to track and process the cart orientation as it moves over the uneven terrain.