Target Motion Analysis via Hard and Soft Data Fusion

Target Motion Analysis (TMA) requires the online fusion of multiple hard and soft data sources for target tracking. This paper proposes a Bayesian filtering solution for multisource fusion with hard and soft data. Appropriate models for various types of hard and soft data are developed so that they can be fused in a consistent manner under the Bayesian framework. The resulting Bayes filter is highly non-linear and non-Gaussian. Hence, a parallel particle filter is developed to facilitate a user adjustable trade-off between computation time and tracking accuracy. Numerical studies on realistic scenarios are also presented.