Enhancing Predicted Distributions for Constant Acceleration and Turn Rate Motion Models: A Deep Learning Approach
Ole Halvard Sætran, Sigmund Rolfsjord · 2024
Gating and association are key components of target tracking, most commonly using the predicted distribution of the track to evaluate new measurements. While a Gaussian predicted distribution is widely used, it is not optimal for some groups of targets, such as fixed-wing aircraft and surface vessels. This article introduces the Constant Acceleration and Turn Rate Neural Network (CAT-NN) method, which uses a neural network to calculate the predicted distribution for such targets. Our work can be seen as an expansion of the CAT distribution by correcting for track uncertainty, thereby unlocking CAT distributions for a much wider range of tracking applications. Simulations show that the CAT-NN predicted distribution is a better match for the true predicted distribution than both the CAT and Gaussian distributions for a fixed-wing aircraft. It also outperforms the Gaussian distribution in a simulation tracking scenario with a single fixed-wing aircraft in heavy clutter. The CAT-NN model is runtime efficient and implemented as a custom hypothesiser component for the Stone Soup tracking framework.