Correlation of Gaussian Mixture Tracks

In this paper, methods are developed and evaluated for the correlation of Gaussian mixture tracks from two sensors. The hypothesis likelihoods for the case of a single target are given using the minimum mean square error and the maximum likelihood estimates of common origin between two Gaussian mixtures. A correlation test is developed as a likelihood ratio of the single target hypothesis to the hypothesis of two separate targets. The negative log likelihood cost is formulated and used in an optimal assignment method to perform track-to-track correlation for multiple targets between two sensors. Simulations were performed to compare the minimum mean square error and maximum likelihood approaches with Gaussian mixture tracks to a baseline method using unbiased converted measurements for sensors with a given probability of detection and bias significance. Results are shown to compare the performance of the correlation methods with respect to probability of correct correlation and root mean squared error versus track density for several different aspect angles between two sensors.