Track stitching and approximate track association on a pairwise-likelihood graph

Lingji Chen; Sarah E. Rumbley

doi:10.1117/12.2304484

27 April 2018 Track stitching and approximate track association on a pairwise-likelihood graph

Lingji Chen, Sarah E. Rumbley

Proceedings Volume 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII; 1064602 (2018) https://doi.org/10.1117/12.2304484
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

Single-sensor track stitching is a path cover problem on a graph with pairwise log likelihoods. This paper provides a theoretical justification for pursuing track association on such a graph by using a sum of pairwise log likelihoods in place of the multi-sensor log likelihood. It outlines solution strategies through clique cover, cotemporal subgraph decomposition, and super-node stitching

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Citation Download Citation

Lingji Chen and Sarah E. Rumbley "Track stitching and approximate track association on a pairwise-likelihood graph", Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 1064602 (27 April 2018); https://doi.org/10.1117/12.2304484

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