Improvements to the tracking process

Abstract

Accurate target tracking is a fundamental requirement of modern automated systems. An accurate tracker must correctly associate new observations to existing tracks and update those tracks to reflect the new information. An accurate tracker is one which predicts assignments and measurement distributions closely matching the ground truth. This work will show that aspects of the GNP algorithm and IMM filter require amendments and renewed investigation. To aid the framing of the solutions in the context of tracking, some general background will be presented first. More specific background will be given prior to the corresponding contributions. Modern sensor networks require the alignment of track pictures from multiple sensors (sometimes called sensor registration). This issue was described in the 1990s and termed the global nearest pattern problem in the early 2000s. The following work presents a correction and extension of the solution to the global nearest pattern problem with a heuristic error estimation algorithm. Its use for sensor calibration is demonstrated. Once measurements have been associated to tracks, there still remain several choices that define the tracking algorithm, one being the filtering algorithm which updates the track state. One common solution for filtering is the interacting multiple model filter which was originally developed in the 1980s. This is essentially a bank of Kalman filters which are weighted and mixed based on a predefined Markov chain. The validity of the assumptions on that Markov chain will be discussed and recommendation for replacing those assumptions with neural networks will be proposed and assessed. Finally, following association of two tracks for a single target, it is necessary to combine their information while respecting the lack of knowledge about correlations between the tracks. Covariance intersection was developed in the 1990s and 2000s for track-to-track fusion when tracks are assumed Gaussian. A generalization of covariance intersection, Chernoff fusion, was developed in the 2000s for handling general track states. A connection made in the literature which allows for direct analysis of the error of Chernoff fusion is used to evaluate the effectiveness of Fibonacci lattices for quasi-Monte Carlo integration solutions required by Chernoff fusion.