摘要
为解决加性噪声模型无法准确刻画实际观测模型的问题,采用带有乘性噪声系统模型进行建模。在实际系统中,由于多传感器网络的应用使得传统乘性噪声的滤波算法已无法满足实际需求,该文分别提出带有乘性噪声的有反馈分布式和序贯式多传感器强跟踪滤波融合方法,以有效解决复杂环境下的非线性系统最优状态估计问题。计算机仿真实验表明,新算法具有很好的估计精度,在多传感器目标跟踪应用中有较好的应用前景。
In this paper, a multiplicative noise model is established to solve the problem that additive noise model cannot precisely describe the observed model. In actual systems, the filter algorithm of traditional multiplicative noise can no longer meet actual requirements owing to the application of multi-sensor network. Therefore, this paper puts forward respectively distributed and sequential multi-sensor strong tracking filter（STF） data fusion methods with multiplicative noise and feedback, in order to solve the optimal state estimation of nonlinear system in complex environment. Computer simulation experiments show that the new algorithm has good estimation accuracy, indicating a promising future application of multi-sensor target tracking.
作者
张虎龙
ZHANG Hulong(Chinese Flight Test Establishment,Xi＇an 710089,China)
出处
《中国测试》
北大核心
2017年第5期101-104,共4页
China Measurement & Test
基金
航空科学基金(2015ZD30002)
关键词
数据融合
传感器网络
强跟踪滤波
乘性噪声
data fusion
sensor network
strong tracking filter
multiplicative noise
