An order insensitive sequential fast covariance intersection fusion algorithm

•The proposed SFCI fusion algorithm can handle the problem of unknown cross-correlation in local estimation errors.•The fusion accuracy of the proposed algorithm is not relevant to the fusion orders, i.e., all fusion nodes can acquire identical result.•The fusion coefficients are straightforward to calculate, owing to not require optimizing nonlinear cost function.•The proposed algorithm is computationally efficient and is therefore applicable for use in real-time fusion systems. 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.