一种基于渐消因子的区间卡尔曼滤波器方法

发布时间：2018-06-12 05:33

  本文选题：超紧耦合 + 锁相环　； 参考：《武汉大学学报(信息科学版)》2017年12期

【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 the noise environment, the steady-state abrupt change of moving target will reduce the filtering performance of Kalman filter, which will lead to the decrease of the reliability of integrated navigation and the anti-jamming ability of navigation system, which will affect the accuracy of navigation. In order to improve the performance of Kalman filter and improve the ability of anti-jamming and navigation precision, a new algorithm of interval Kalman filter based on fading factor is proposed by using the ultra-tight coupling based on Kalman filter. The filter gain matrix is adjusted in real time by introducing fading factor and interval matrix, and all kinds of error sources are constrained to the intersection interval by the intersection operation in interval operation. Furthermore, the completeness and optimization of fidelity set mappings in interval operations are ensured. The results show that the algorithm can overcome the defects of the original filter algorithm, improve the tracking ability of the steady state abrupt target in the noise environment, and improve the filter effect in the noise, and the computational complexity of the algorithm is not obviously increased.
【作者单位】： 南京理工大学电子工程与光电技术学院;中国工程物理研究院电子工程研究所;
【基金】：国家自然科学基金委员会与中国工程物理研究院联合基金(U1330133)~~
【分类号】：P228.4
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本文编号：2008555