Influence of the Sensor Local Track Covariance on the Track-to-Track Sensor Fusion

Abstract

The information fusion of the processed sensory tracks is carried out using track-to-track fusion algorithms. The performance analysis of a selected track-to-track fusion algorithms under different sensory track covariance configurations are carried out in this paper. This is the first paper that does the study on the influence of sensory track covariance on the performance of three important algorithms for track-to-track fusion. A simulation setup with known system parameters and an optimal centralized measurement fuser based on the Kalman estimator as the benchmark is used to numerically evaluate the different algorithms with different sensory track covariance configurations. The results of this experiment shows that sensory track covariance plays an important role in achieving a consistent fused estimate in a track-to-track fusion problem. It is difficult to obtain this vital information at the fusion center in a real world system due to certain practical limitations. It is necessary to compensate this loss of information by estimating the respective sensor's local track covariance. Some practical solutions based on the available information at the fusion center, which could be used to carry out this compensation is proposed in this paper.