Fusion of acoustic/seismic and GPR detection algorithms

A variety of sensors have been investigated for the purpose of detecting buried landmines in outdoor environments. Mines with little or no metal are very difficult to detect with traditional mine detection systems. Ground Penetrating Radar (GPR) sensors have shown great promise in detecting low metal mines and can easily detect metal mines. Unfortunately, it can still be difficult to detect low-metal mines with GPR due to very low contrast between the mine and the surrounding medium. Acoustic-seismic systems were proposed by Sabatier et.al.^1-5 and have also shown great promise in detecting low metal mines. There are now a wealth of references that discuss these systems and algorithms for processing data from these systems^1-11. Therefore, they will not be discussed in detail here. In fact, low-metal mines are easier to detect than metal mines with this acoustic-seismic systems. Low metal mines that are difficult for a GPR to detect can be quite easy to detect with acoustic-seismic approaches. Conversely, metal mines may actually be difficult for acoustic-seismic systems. Sensor fusion with these sensors is of interest since together they can find a broader range of mines with relative ease. The algorithmic challenge is to determine a strategy for combining the multi-sensor information in a way that can increase the probability of detection without increasing the false alarm rate significantly. In this paper, we investigate fusion of information obtained from GPR and acoustic-seismic sensors on real data measured from a mine lane containing three types of buried landmines and also areas containing no landmines. Algorithms are applied to data acquired from each sensor and confidence values are assigned to each location at which a measurement is made by each sensor. The GPR is used as a primary sensor. At each location at which the GPR algorithm declares an alarm, a modified likelihood-based approach is used to increase the GPR derived confidence if the likelihood that a mine is present, defined by the acoustic-based confidence, is larger than the likelihood that no mine is present. If the acoustic-derived confidence is very high, then a declaration is made even if there is no GPR declaration. The experiments were conducted using data acquired from the sensors at different times. The acoustic-seismic system collected data over a subset of the region at which the GPR collected data. Results are given only over those regions for which both sensors collected data.