Extended target tracking using neural network and Gaussian process

Abstract

In extended target tracking, Gaussian Process (GP) is utilized to model unknown contour functions based on the model-predicted target center and contour measurements. However, model prediction relies on accurate prior knowledge. When the model-predicted target center is inaccurate, it will affect the modelling of the measurement model. To address issue, this letter introduces a hybrid-driven approach that combines extended Kalman filter using GP with neural network; proposes an extended target tracking algorithm using neural network and GP. The algorithm predicts the target center according to the neural network and the target's kinematic model, and takes the prediction center and the contour measurements at the current moment as the input of the neural network, which in turn provides real-time estimates for the predicted center compensation. The simulation results show that the algorithm has a significant improvement in tracking performance and better accuracy in estimating the center position and extent state of the target.