测绘通报 ›› 2018, Vol. 0 ›› Issue (4): 92-95.doi: 10.13474/j.cnki.11-2246.2018.0117

• 行业观察 • 上一篇    下一篇

多系统GNSS RTK的改进卡尔曼滤波算法

范红平1, 周志峰1, 王永泉2   

  1. 1. 上海工程技术大学机械工程学院, 上海 201620;
    2. 上海司南卫星导航技术股份有限公司, 上海 201801
  • 收稿日期:2017-07-27 修回日期:2018-01-21 出版日期:2018-04-25 发布日期:2018-05-03
  • 作者简介:范红平(1991-),女,硕士生,主要研究方向为GNSS与激光雷达组合导航。E-mail:M010215127@sues.edu.cn
  • 基金资助:

    上海市科技攻关计划(15511103300)

Improved Kalman Filtering Algorithm for GNSS RTK Multi-system

FAN Hongping1, ZHOU Zhifeng1, WANG Yongquan2   

  1. 1. College of Mechanical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China;
    2. Shanghai Compass Satellite Navigation Technology Co., Shanghai 201801, China
  • Received:2017-07-27 Revised:2018-01-21 Online:2018-04-25 Published:2018-05-03

摘要:

针对多系统GNSS RTK特点,本文引入了对整周模糊度的求解,但随着可见卫星的增加,状态向量的维数急剧增加,这导致Kalman滤波方程乘法次数增加。而协方差计算量和存储量约占整个滤波进程时间的70%。为提高运算效率,本文提出了一个稀疏状态转移矩阵的改进Kalman计算方法,主要研究协方差矩阵的求解。并在理论上利用矩阵分块和对称化将乘法次数降低至通常算法的10%以下。此外,通过试验证明,改进方法的CPU处理器耗时同样低于通常算法的10%。此改进方法实现了高效性,对多卫星情况下求解整周模糊度有一定参考价值。

关键词: 多系统, GNSS RTK, 协方差阵, 迭代, 矩阵乘法

Abstract:

For the multi-system GNSS RTK feature,the integer ambiguity is introduced in this paper.However,due to the increase of visible satellite,the dimension of the state vector will increases sharply,which leads to a large number of multi-dimensional matrix multiplication.And the covariance calculation and storage capacity account for 70% of the total Kalman filtering process time.In order to improve the efficiency of the algorithm,an improved Kalman method for sparse state transition matrices is proposed.This paper mainly discusses the solution of covariance matrix and uses matrix block units with symmetry to reduce the multiplication times to less than 10% of the general algorithm.The experimental result is that the improved method of CPU processor takes less than 10% of the general algorithm.This improved method achieves high efficiency,which also has certain reference value for other filtering algorithms.

Key words: multi-system, GNSS RTK, covariance matrix, iteration, matrix multiplication

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