Proportional Navigation-Benchmarked Guidance Strategy using Artificial Neural Networks

Abstract

Pursuit of an aerial target by interceptor has become a problem of paramount importance in the last several decades. Proportional Navigation (PN) guidance law is most widely used because of its simplicity, ease in implementation and robustness. Therefore, by benchmarking PN guidance, this paper obtains effective guidance strategies leveraging artificial neural networks (ANNs). Suitable input variables that can ensure good mathematical approximation, and optimal training strategy that can ensure sufficient training accuracy are obtained. Performances of ANNs with corresponding input variables are found to be equivalently effective in comparison with PN guidance in most of the engagement geometries.