Comparative investigation into Viterbi based and multiple hypothesis based track stitching

Abstract

In this study a comparative analysis is performed between a novel Viterbi based and multiple hypothesis based track stitching algorithms. The track fragments in the Viterbi based track stitching algorithm are modelled as nodes in a trellis structure. A sequential Viterbi data association algorithm is then used to solve the trellis and associate track fragments with each other. A Kalman filter is used to determine the possible associations as well as the probabilities of the associations between the track fragments. In the multiple hypothesis track stitching algorithm, the hypothesis based multiple hypothesis tracking (MHT) algorithm is extended to perform track fragment to track fragment associations, rather than associating observations to tracks. Aspects of the developed multiple hypothesis algorithm are compared with implementations of a similar nature. Novel aspects of this research include the modification of the sequential Viterbi algorithm, as well as the extension of the MHT algorithm to solve the track stitching problem. It was found that the sequential Viterbi track stitching algorithm performed somewhat better than the multiple hypothesis track stitching algorithm for similar execution times. The Viterbi based track stitching algorithm is also shown to produce more consistently acceptable results.