Multiple Extended Target Tracking Using PHD Filter and Convolutional Conditional Neural Process

Abstract

In the extended target tracking using Gaussian process (GP) or other methods to estimate the extension state, all of them is necessary to obtain the measurement model, and the deviation from the true kinematic state affects the extension result easily. Meanwhile, with the growth of the shape complexity, the corresponding covariance matrix dimension increases, leading to a very high computational burden. To this end, we present an extended target tracking algorithm based on deep learning, which adopts Gaussian mixture (GM) probability hypothesis density (PHD) filter for extended target (ET) tracking, called the ET-GM-PHD filter, to estimate the kinematic state, and uses convolutional conditional neural process (ConvCNP) to estimate the extension state (i.e., the shape of the target) avoiding the complex computation and dependence on priors. Therefore, the proposed algorithm is named as the ConvCNP-GM-PHD filter. In our algorithm, ConvCNP lies on the neural network (NN) and GP, where the former can learn the rules from a large amount of data without any priors, while the latter uses the covariance function to express the distribution over possible functions according to a small number of context observations. Due the proposed algorithm can effectively combine the advantages of both, it is reliable to get a model via training which is able to realize function regression flexibly. Thus, ConvCNP is able to estimate the shape function by putting the angles and radial value between the kinematic state and associated measurements into the pre-trained model. The results show that the accuracy of the extension state estimation of the proposed ConvCNP-GM-PHD filter is improved and its computational complexity is also reduced. It implies that the proposed algorithm is more real-time and effective, and has a good application prospect.