Distributed consensus-based cooperative passive positioning with multi-source information fusion

To address the issue of difference estimates from multi-source sensors in distributed cooperative passive localization and tracking, this paper proposes a distributed consensus algorithm based on fast covariance intersection. First, a multi-sensor temporal alignment model is constructed using Taylor formula and adaptive density clustering theory to resolve temporal asynchrony caused by multi-source asynchronous sampling. Subsequently, each sensor performs local estimation by integrating information contributions from neighboring sensors. Next, a theoretical relationship between the iteration number and the consensus error is derived based on a given steady-state consensus error, ensuring that the information group converges to consensus within a finite number of iterations. On this basis, each sensor only needs to perform one global fast covariance intersection to complete information fusion, significantly improving the efficiency and accuracy of fusion. Finally, the algorithm is applied to a multi-source distributed collaborative passive positioning system. Simulation results demonstrate that the proposed method can effectively achieve consensus fusion positioning within a finite number of iterations while simultaneously completing temporal alignment.