Modified federated Kalman filter for INS/GNSS/CNS integration

Abstract

This paper presents a modified version of federated Kalman filter (FKF) for INS/GNSS/CNS integration by improving the computational efficiency involved in the FKF's master filter. During the master filtering process, the modified federated Kalman filter (MFKF) firstly decomposes the global state vector into three sub-states according to the characteristics of INS/GNSS/CNS integration. Subsequently, it fuses the sub-state estimations from INS/GNSS and INS/CNS subsystems with the corresponding ones from the time-update solution of the master filter, respectively. Eventually, the fused sub-state estimations are recombined to yield the global state estimation. The proposed MFKF provides the capability of distributed and parallel data processing for the global state fusion to reduce the computational load involved in the master filtering process of the FKF. Experimental results and comparison analysis demonstrate the effectiveness of the proposed MFKF.