Maximum correntropy Kalman filter for GNSS／INS tightly-coupled integration
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摘要: 针对实际环境中量测噪声易被野值污染而呈现非高斯分布,进而导致传统卡尔曼滤波(KF)算法性能降低的问题,提出了最大熵卡尔曼滤波(MCKF)算法. 该算法基于最大熵准则(MCC)和M估计的思想推导得到. 与KF相比,所提算法能够给异常量测值分配较小的权重以减轻其对于状态估计的影响,与基于Huber函数的卡尔曼滤波(HKF)算法相比,其能够更有效地利用量测信息,因此所提算法相比于KF和HKF而言更加鲁棒. 在全球卫星导航系统(GNSS)与惯性导航系统(INS)的紧组合模式下进行车载实测实验,由于GNSS的伪距与伪距率等原始量测信息质量不佳,因此KF和HKF的性能均受到影响,而所提MCKF算法能够有效地抑制异常量测值的影响,能够更快地收敛且得到更高的估计精度.Abstract: In real application, the measurement noise is easily affected by gross errors and becomes nonGaussian 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) carmounted 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.
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Key words:
- Kalman filtering /
- GNSS／INS /
- tightly-coupled /
- M-estimation /
- maximum correntropy criterion
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