多传感器非线性系统序贯观测融合二阶扩展卡尔曼滤波器
Sequential measurement fusion second-order extended Kalman filter for multi-sensor nonlinear systems
姜吉鹏 1孙书利1
作者信息
- 1. 黑龙江大学 电子工程学院,哈尔滨 150080
- 折叠
摘要
对多传感器非线性系统提出了一种序贯观测融合二阶扩展卡尔曼滤波(Second-order Extended Kalman Filter,SOEKF)算法.该算法的中心思想是根据传感器观测数据到达融合中心的先后次序依次进行处理.研究表明,提出的序贯观测融合SOEKF算法比集中式观测融合EKF算法具有更高的精度;与集中式观测融合SOEKF算法精度相当,且具有更低的计算复杂度.目标跟踪系统的仿真验证了算法的有效性.
Abstract
A sequential measurement fusion second-order extended Kalman filter(SOEKF)algorithm is proposed for multi-sensor nonlinear systems.The main idea of the algorithm is to successively process the data according to the sequence of the data of the sensor arriving at the fusion center.The studied results show that the proposed sequential measurement fusion SOEKF algorithm has higher accuracy than the centralized measurement fusion EKF algorithm,and is comparable to the centralized measurement fusion SOEKF algorithm,with a lower computational complexity.The simulation of a target tracking system verifies the effectiveness of the algorithm.
关键词
多传感器信息融合/序贯观测融合/非线性系统/二阶扩展卡尔曼滤波器Key words
multi-sensor information fusion/sequential measurement fusion/nonlinear system/second-order extended Kalman filter引用本文复制引用
基金项目
国家自然科学基金(61573132)
黑龙江省自然科学基金(ZD2021F003)
出版年
2024
NETL
NSTL
万方数据