Electromagnetic Sensor to Detect Objects by Classification and Neural Networks

Abstract

The backscatter signal analysis, as the landmine material could vary, has to be as much advanced as possible. One major problem with the conventional methods is that they are not able to detect new plastic landmines. In the recent research, the classification techniques and neural networks (NNs) were exploited for detection. In NNs-based method, a network is trained based on the feature extracted from the data, which leads to landmine detection. Other conventional classification methods, attempts to classify the objects sharing common characteristics. In this letter, an algorithm is introduced based on classification, data reduction and neural networks. Indeed, this algorithm employs neural network and classification method, simultaneously. The simple methods using either neural network or classification separately usually suffer from high rate of risk. In this letter, a novel classifier is proposed such that the data is classified based on similarity. It will be shown that the similarity between signals in a class is more than 90%, which proves the method's efficiency. Moreover, the scattering parameter, having magnitude and phase parts, is used to create an algorithm with parallel process.