Maximum correntropy Kalman filter for GNSS／INS tightly-coupled integration

In real application, the measurement noise is easily affected by gross errors and becomes nonGaussian distribution, resulting in the performance of the traditional Kalman filter (KF) being degraded significantly. In order to deal with this problem, the maximum correntropy Kalman filter (MCKF) is proposed based on the maximum correntropy criterion (MCC) and M-estimation. Compared with KF, the proposed filter can assign less weight to the abnormal measurements to reduce its influence on the state estimation, and compared with the Huber-based Kalman filter (HKF), it can make more effective use of measurement information, thereby the proposed filter is more robust. The tightly coupled GNSS／INS (global navigation satellite system／inertial navigation system) carmounted experiments were carried out to verify the performance of the proposed filter. The results show that the KF and HKF achieve bad estimation accuracy due to the poor quality of the original measurements of the GNSS such as the pseudorange and pseudorange rate. And the proposed MCKF can effectively suppress the influence of abnormal measurements, resulting in faster convergence and higher estimation accuracy than existing filters.