Probabilistic Information Matrix Fusion in a Multi-Object Environment

Abstract

In distributed sensor fusion systems, each of the local sensors has its own tracker processing local measurements for measurement-to-track association and state estimation. Only the processed data, local tracks (LT) comprising state vector estimates and their covariance matrices are transmitted to the fusion center (FC). In this work, a multi-object hybrid probabilistic information matrix fusion (MO-HPIMF) is derived taking into account all association hypotheses. In MO-HPIMF, the association carried out is between the FC track states (prediction) and the LT state estimates from local sensors. When having a large number of objects and sensors in fusion, only the m-best FC-track-to-LT association hypotheses should be incorporated in MO-HPIMF to reduce the computational complexity. A Sequential m-best 2-D method is used for solving the multidimensional assignment problem in this work. It is shown in the simulations that MO-HPIMF can successfully track all targets of interest and is superior to track-to-track fusion (T2TF, a commonly used approach in distributed sensor fusion system) which relies on hard association decisions.