A New Track Association Model Based on Uncertainty Distribution

Aiming at the problem of degradation of correlation quality caused by excessive idealization of noise in the traditional track correlation model, this paper introduces the uncertainty theory in the track correlation process, and uses the uncertainty normal distribution in the theory to describe the noise in the track correlation, thus proposing a new track correlation model based on the uncertainty distribution. The motion equation and observation equation are reconstructed under three motion states uniform velocity, uniform acceleration, and variable acceleration. At the same time, this paper also uses a variety of traditional noises (including Gaussian noise) to simulate various complex noise environments, and uses a variety of algorithms to solve the model in this paper and the traditional model, and finally compares the performance of the two models under the same conditions, thus verifying the superiority and applicability of the uncertain normal correlation model from a variety of angles to the greatest extent.