徐韩, 曾超, 黄清华. 一种基于渐消因子的区间卡尔曼滤波器方法[J]. 武汉大学学报 ( 信息科学版), 2017, 42(12): 1826-1833, 1839. DOI: 10.13203/j.whugis20150567
引用本文: 徐韩, 曾超, 黄清华. 一种基于渐消因子的区间卡尔曼滤波器方法[J]. 武汉大学学报 ( 信息科学版), 2017, 42(12): 1826-1833, 1839. DOI: 10.13203/j.whugis20150567
XU Han, ZENG Chao, HUANG Qinghua. Kalman Filtering for Ultra-Tight Coupled from Stable State to Catastrophe State in Noise Condition[J]. Geomatics and Information Science of Wuhan University, 2017, 42(12): 1826-1833, 1839. DOI: 10.13203/j.whugis20150567
Citation: XU Han, ZENG Chao, HUANG Qinghua. Kalman Filtering for Ultra-Tight Coupled from Stable State to Catastrophe State in Noise Condition[J]. Geomatics and Information Science of Wuhan University, 2017, 42(12): 1826-1833, 1839. DOI: 10.13203/j.whugis20150567

一种基于渐消因子的区间卡尔曼滤波器方法

Kalman Filtering for Ultra-Tight Coupled from Stable State to Catastrophe State in Noise Condition

  • 摘要: 在噪声环境中,运动目标发生稳态突变会降低卡尔曼滤波器的滤波性能,进而导致组合导航的可靠性降低,导航系统抗干扰能力下降,影响导航的精确度。为了提高卡尔曼滤波器性能,提高抗干扰能力和导航精度,在采用基于卡尔曼滤波器的超紧耦合同时,提出一种新型的基于渐消因子的区间卡尔曼滤波器算法。该算法通过引入渐消因子和区间矩阵对滤波器增益矩阵进行实时调整,并利用区间运算中的交集运算将各种误差源约束到交集区间,进而保证在区间运算中保真集合映射的完备性并取得最优化。结果显示,该算法能够克服原有滤波器算法的缺陷,在噪声环境中提升对稳态突变目标的跟踪能力,且在噪声中滤波器效果提高,算法计算量没有明显增加。

     

    Abstract: In noise condition, high dynamic aerocraft change from stable state to catastrophe state will lead reduction of performance of Kalman filter, and then reduce reliability of GPS/INS integration, which influence anti-jamming of navigation system and reduce accuracy of navigation integration. In order to improve Kalman filter's performance, enhance performance of anti-jamming of navigation system and increase accuracy of navigation integration, a new fading interval Quadrature Kalman filtering algorithm was proposed in this paper. The algorithm could adjust its filtering gain matrix on line and interval operation can not only ensure the completeness of set mapping but also can be the optimization by introducing a time-varying fading factor and interval matrix. The simulation performance statistics show that the new Kalman filtering algorithm can overcome limitation of QKF algorithm, which can improve performance of tracking. Complexity of algorithm was not increased acutely.

     

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