Cooperative Localization Based on Interval Split Covariance Intersection Filter in MSMV System

In the Multi-sensor multi-vehicle (MSMV) cooperative localization system, the data incest problem generates inter-estimate correlation, leading to over-convergence problems. For the purpose of solving the data incest problem, we propose a method named interval split covariance intersection filter (ISCIF). First, the consistency proof of ISCIF is provided. Then, ISCIF is applied to achieve vehicle cooperative localization, including both absolute and relative positioning stage. Moreover, in order to reduce the computational overhead and address pessimistic estimation problems caused by multiple relative position estimates in the MSMV cooperative localization system, the interval constraint propagation (ICP) method is employed for data preprocessing before relative positioning using ISCIF. Finally, two different simulation experiment scenarios are designed to validate localization accuracy. Simulation results demonstrate that our proposed method, compared to the traditional split covariance intersection filter (SCIF) method, reduces the root mean square error (RMSE) by 13% and 21% in two different experimental scenarios, respectively.