Neural algorithms for EMI based landmine detection

Landmines are a major problem facing the world today. There are millions of these deadly weapons still buried in various countries around the world. Humanitarian organizations dedicate an immeasurable amount of time, effort, and money to find and remove as many of these mines as possible. Over the past decade the US Government has become involved and has encouraged much research into improving landmine sensor technologies such as Ground Penetrating Radar, Infrared Cameras, Electro-Magnetic Induction sensors, and a variety of other technologies. The major goal of this research has been two-fold; it is important to improve the probability of detection of landmines, and, equally important, to reduce the probability of false alarms. The major cost of de-mining is incurred in the efforts to safely remove suspected landmines from the ground. The technicians have to carefully dig up the object, treating it as a live mine or piece of unexploded ordinance. Unfortunately, landmines can be made out of fairly common materials such as metal, wood, and plastic, which can confuse the sensor and cause it to erroneously report normal material in the field as mines. In an effort to reduce the number of false alarms, researchers have investigated the use of computers to analyze the raw data coming from the sensor. These computers could process the raw data and decide whether or not a certain location contains a mine. One popular avenue in this field of research is using neural networks. This thesis takes a look at a variety of neural network approaches to mine detection and looks specifically at the use of an artificial neural network (ANN) with data that has been pre-processed with the 8-technique and S-Statistic. It is shown that an ANN that uses the 8-technique and S-Statistic as inputs will achieve an acceptably high probability of detection with a low probability of false alarms. It is also shown that the pre-processing is responsible for most of the performance gain, as the Back Propagation Neural Network (BPNN) and Random Neural Network (RNN) models achieve similar probabilities of detection. The BPNN, however, does consistently perform better than the RNN by a small margin