An order insensitive sequential fast covariance intersection fusion algorithm

In this article, data cross-correlation in distributed sensor system is investigated via an order insensitive sequential fast covariance intersection fusion algorithm. Among the existing approaches, the common drawbacks are that the fusion results are sensitive to fusion orders and the computational burden is tremendous due to the optimization of multi-dimensional nonlinear cost function. In order to overcome these drawbacks, a sequential fast covariance intersection (SFCI) algorithm is presented. The new fusion coefficients can be calculated straightforward by taking the reciprocal of the trace of the inverse variances as local fusion coefficients and using an iterative process for fusion step to revise the coefficient weight. Note that the proposed fusion algorithm is consistent, and its accuracy is unrelated to the fusion order of the distributed system. Finally, real radar data and simulation examples are provided to verify the effectiveness of the proposed algorithm.