| 摘要: |
| 卫星量测观测值容易受到钟跳、接收机噪声及多径效应等因素的影响，产生异常值和非高斯噪声，导致传统扩展卡尔曼滤波失准且稳定性下降。针对此问题，提出了一种自适应核宽的最大相关熵平方根容积卡尔曼滤波算法，并将其应用于RTK定位。该方法使用高斯核函数作为代价函数，并通过近似状态预测值和测量值重新构造测量噪声协方差矩阵。同时，依据新息对核宽进行自适应调整，提高了算法的收敛速度及对非高斯噪声和粗差的处理能力。基于实测数据的实验结果表明，所述方法可以提升RTK定位精度和固定率。 |
| 关键词: RTK 平方根容积卡尔曼滤波 最大相关熵 |
| DOI： |
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| 基金项目: |
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| Application of maximum correntropy square root volume Kalman filter in GNSS positioning |
| DAI Jie,ZHENG Pei,ZHANG Aijun,LENG Jiaqi,CHENG Yang |
| (School of Mechanical Engineering,Nanjing University of Science and Technology, Nanjing 210094, China) |
| Abstract: |
| Satellite measurement observations are easily affected by factors such as clock jumps, receiver noise, multipath effects and other factors, resulting in outliers and non-Gaussian noise. This leads to inaccurate and unstable traditional extended Kalman filters. To address this problem, an adaptive kernel width maximum correntropy square root cubature Kalman filter algorithm is proposed and applied to RTK positioning. This method uses a Gaussian kernel function as the cost function and reconstructs the measurement noise covariance matrix by approximating the predicted state values and the measured values. At the same time, the kernel width is adaptively adjusted based on new information, improving the convergence speed of the algorithm and its ability to handle non-Gaussian noise and gross errors. Experimental results based on measured data show that the method can improve the accuracy and fixation rate of RTK positioning. |
| Key words: Real time kinematic (RTK) Square root cubature Kalman filter Maximum correntropy |
