Deep Learning based Estimation of Weaving Target Maneuvers

TL;DR

This paper explores using deep neural networks to estimate weaving target frequencies in tracking scenarios, demonstrating improved accuracy and efficiency over traditional Kalman-based methods.

Contribution

It introduces neural network architectures for frequency estimation in target tracking, replacing classical Kalman filter approaches with a more accurate and resource-efficient method.

Findings

Neural networks outperform multiple model adaptive estimation in accuracy.

Deep neural networks require fewer measurements to converge.

Proposed architectures are computationally efficient.

Abstract

In target tracking, the estimation of an unknown weaving target frequency is crucial for improving the miss distance. The estimation process is commonly carried out in a Kalman framework. The objective of this paper is to examine the potential of using neural networks in target tracking applications. To that end, we propose estimating the weaving frequency using deep neural networks, instead of classical Kalman framework based estimation. Particularly, we focus on the case where a set of possible constant target frequencies is known. Several neural network architectures, requiring low computational resources were designed to estimate the unknown frequency out of the known set of frequencies. The proposed approach performance is compared with the multiple model adaptive estimation algorithm. Simulation results show that in the examined scenarios, deep neural network outperforms multiple model adaptive estimation in terms of accuracy and the amount of required measurements to convergence.