Multisensor fusion classification with a multilayer perceptron

The problem of fusing information from multiple sensors to perform pattern recognition is addressed. A technique is proposed for performing target/nontarget discrimination using information from absolute range and forward looking infrared (FLIR) sensors. A multilayer perceptron is used to perform the feature-level fusion and is compared to a k-nearest-neighbor classifier as well as a nonparametric Bayesian classifier using Parzen windows. All three classifiers show statistically significant improvement from the fusion process; however, the multilayer perceptron uses far fewer free parameters, which should improve generalization capability. Also, the multilayer perceptron requires much less computation than the traditional classifiers