Abstract
Missile guidance systems using the Proportional Navigation (PN) guidance law is limited in performance in supporting wide class of engagement scenarios with varying mission and target parameters. For surpassing this limitation, an Artificial Neural Network (ANN) to substitute the PN guidance is proposed by the authors. The ANN based system enables learning, adaptation, and faster throughput and thus equips the guidance system with capability akin to intelligent biological organisms. This improvement could remove the barrier of limitations with allowable mission scope. In this paper, a Multi-Layer Perceptron (MLP) has been selected to implement the ANN based approach for replacing PN guidance. Attempts to replace PN guidance using MLP are limited in the literature and warrant greater attention due to significant theoretical development with the MLP field in recent times. It is shown in this paper, that the MLP based guidance law can effectively substitute PN for a wide range of engagement scenarios with variations in initial conditions. A foundational argument to justify using an MLP for substituting PN is provided. Besides this, the design, training and simulation based testing approach for an MLP to replace PN has been devised and described. The potential for faster throughput is possible as the MLP nodes process information in parallel when generating PN like guidance commands. The results clearly demonstrate the potential of MLP in future applications to effectively replace and thus upgrade a wide spectrum of modern missile guidance laws.
Highlights
A staple missile guidance law that has remained robust for most homing missile engagements is Proportional Navigation (PN).[1]
Missile guidance systems using the Proportional Navigation (PN) guidance law is limited in performance in supporting wide class of engagement scenarios with varying mission and target parameters
An Artificial Neural Network (ANN) to substitute the PN guidance is proposed by the authors
Summary
A staple missile guidance law that has remained robust for most homing missile engagements is Proportional Navigation (PN).[1]. One approach to achieve these upgrades is to enable the missile guidance system to perform automatic learning, adaptation, and improve throughput when generating guidance law commands This has led to research and design effort being directed towards the discipline of Intelligent Systems techniques.[4] This discipline has been advancing in order to up-. 2015, Vol 4, No 1 select the training data for all the scenarios tested because some choices would result in the MLP guidance only being able to substitute PN for data in the neighborhood of training data collected, rather than match correctly across the entire set of input-output patterns that processing using PN allows for This was briefly investigated with some limited solutions described by referring to literature such as.[13].
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