Deep Learning based Estimation of Weaving Target Maneuvers
This work addresses target tracking for applications like defense or aerospace, but it is incremental as it applies existing deep learning techniques to a known problem with specific constraints.
The paper tackled the problem of estimating unknown weaving target frequencies in target tracking to reduce miss distance, proposing deep neural networks as an alternative to Kalman-based methods. Simulation results showed that deep neural networks outperformed multiple model adaptive estimation in accuracy and required fewer measurements for convergence.
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.