Two new features are presented to improve the detection of Anti-Tank (AT) landmines using Ground Penetrating Radar (GPR). A simplified three dimensial physics based model is used as the basis for the features. We combine these features with the results of an algorithm known as LMS. We present promising feature detection algorithms known as Rings N' Things (RNT) and Cross Diagonal Enhancement Processing (CDEP) and our approach to combining the new features with the LMS features using logistic regression techniques. Test results from data gathered at multiple sites covering hundreds of mines and thousands of square meters is analyzed and presented.
KEYWORDS: Roads, Detection and tracking algorithms, Data modeling, Sensors, Image processing, Mining, Algorithm development, Expectation maximization algorithms, Land mines, General packet radio service
A Bayesian classification algorithm is presented for discriminating buried land mines from buried and surface clutter in Ground Penetrating Radar (GPR) signals. This algorithm is based on multivariate normal (MVN) clustering, where feature vectors are used to identify populations (clusters) of mines and clutter objects. The features are extracted from two-dimensional images created from ground penetrating radar scans. MVN clustering is used to determine the number of clusters in the data and to create probability density models for target and clutter populations, producing the MVN clustering classifier (MVNCC). The Bayesian Information Criteria (BIC) is used to evaluate each model to determine the number of clusters in the data. An extension of the MVNCC allows the model to adapt to local clutter distributions by treating each of the MVN cluster components as a Poisson process and adaptively estimating the intensity parameters. The algorithm is developed using data collected by the Mine Hunter/Killer Close-In Detector (MH/K CID) at prepared mine lanes. The Mine Hunter/Killer is a prototype mine detecting and neutralizing vehicle developed for the U.S. Army to clear roads of anti-tank mines.
The Close-In Detector (CID) is the vehicle-mounted multi-sensor anti-tank landmine detection technology for the Army CECOM Night Vision Electronic Sensors Directorate (NVESD) Mine Hunter-Killer (MH/K) Program. The CID includes two down-looking sensor arrays: a 20-antenna ground-penetrating radar (GPR) and a 16-coil metal detector (MD). These arrays span 3-meters in front of a high mobility, multipurpose wheeled vehicle (HMMWV). The CID also includes a roof-mounted, forward looking infrared (FLIR) camera that images a trapezoidal area of the road ahead of the vehicle. Signals from each of the three sensors are processed separately to detect and localize objects of interest. Features of candidate objects are integrated in a processor that uses them to discriminates between anti-tank (AT) mines and clutter and produces a list of suspected mine locations which are passed to the neutralization subsystem of MH/K. This paper reviews the current design and performance of the CID based on field test results on dirt and gravel mine test lanes. Improvements in CID performance for probability of detection, false alarm rate, target positional accuracy and system rate of advance over the past year and a half that meet most of the program goals are described. Sensor performances are compared, and the effectiveness of six different sensor fusion approaches are measured and compared.
This paper investigates the fusion of the confidence outputs of the Energy Based Processing (EBP) algorithm from the BAE Systems and the HMM GPR algorithm from the Univ. of Missouri to increase the performance of the Mine Hunter/Killer (MH/K) vehicle mounted landmine detection system. The EBP algorithm is based on the energy changes in GPR signal for detection. The HMM algorithm, on the other hand, is a feature based technique that relies on hyperbolic signatures to detect landmines. When fusing the detection confidences of the two algorithms properly, the performance is improved dramatically. The detection performance after fusion is demonstrated using data measured at a prepare test site during February and June 2000. Similar diagonal features used in HMM have been implemented and fused with EBP algorithm. Official offline scoring shows that the MH/K exit criteria of 92 percent Pd at 0.013/m2 FAR is met.
The Mine Hunter/Killer Close-In Detector (MH/K CID) uses Ground Penetrating Radar (GPR) as it's primary sensor. The GPR processor requires a sensitive detection algorithm to detect anomalies that may indicate the presence of a buried land mine. A general formula for a statistical detector is presented, consisting of a median filter to eliminate outliers, a local mean estimator using a Blackman window and a local covariance estimator. Advanced methods for robust estimation of the covariance matrix are presented and evaluated using data collected by the CID over buried land mines. This GPR detector is used as a preprocessor for image processing and mine classification algorithms that are used by a sensor fusion processor to determine when to activate the 'Killer' mechanism to neutralize the buried mine.
This survey paper presents several different methods for combining multiple mine detection sensors on a vehicle. There are many methods of classifier combination that have been proposed recently that may, more generally, be applied to the problem of sensor fusion. These sensor fusion algorithms include the majority vote, unanimous consensus, thresholded voting, polling methods which utilize heuristic decision rules, the averaged Bayes classifier, applying logistic regression to the outputs of each classifier, and using Dempster-Shafer theory to derive weights for each sensor's vote.