Application of Bayesian networks to multitarget tracking
Michael A. Kovacich · 1990
This paper describes an application of Bayesian Networks, or Influence Diagrams, to the multitarget tracking problem of a single, angles only, scanning sensor. The Bayesian Network combines the continuous track state vectors and discrete report-to-track association hypotheses into one network which is then used to perform track state vector prediction and update, and to generate, score and prune association hypotheses. The advantages of operating on the network are discussed via an example in which a track resolves into two tracks. The example demonstrates that the network operations provide a highly flexible, numerically stable, computationally efficient. mechanism for calculating the state vectors, covariances and intertrack correlations of the resolved tracks. It is shown that these intertrack correlations, which are somewhat cumbersome to maintain in the usual track filter formulations, are automatically maintained in the network formulation and can improve track accuracy and resolution.