基于多传感器信息融合技术的SCI-AUFK滤波器  

SCI-AUFK Filter Based on Multi-Sensor Information Fusion Technology

作　　者：万珊珊 孙青[1] 翁明善 于学呈 郑桂妹[1] WAN Shanshan;SUN Qing;WENG Mingshan;YU Xuecheng;ZHENG Guimei(Air Force Engineering University,Xi'an 710051,China;Unit 93159 of PLA,Dalian 116033,China)

机构地区：[1]空军工程大学防空反导学院,陕西西安710051 [2]解放军93159部队,辽宁大连116033

出　　处：《探测与控制学报》2021年第6期68-72,共5页Journal of Detection & Control

基　　金：国家自然科学基金面上项目资助(61971438)。

摘　　要：针对带有不确定噪声方差的多传感器网络系统中,传统的融合估计算法产生较大的计算复杂度的问题,提出快速序列协方差交叉融合自适应无迹卡尔曼滤波器(SCI-AUKF)。该滤波器解决了多个一维非线性代价函数的优化问题,同时也是一个递归两传感器的滤波器,其精度高于各局部估计器,且将有效觖决不确定噪声方差下的状态估计问题。将该滤波方法与其他滤波方法进行比较,仿真验证结果表明,其精度显著提高,该算法在雷达网中的应用实例表明了SCI-AUKF滤波方法的优越性和有效性。The traditional fusion estimation algorithm causes larger computational complexity in the multi-sensor network system with uncertain noise variance.A fast sequential covariance cross-fusion adaptive unscented Kalman filter algorithm(SCI-AUKF)was proposed in this paper,which mainly solved the optimization problem of multiple one-dimensional nonlinear cost functions.Its accuracy was higher than that of local estimators,and it could effectively solve the problem of state estimation under uncertain noise variance.Comparing this filtering method with other filtering methods,simulation verification results showed that its accuracy was significantly improved.An application example of the algorithm in radar nets was given,which showed the superiority and effectiveness of the SCI-AUKF filtering method.

关 键 词：多传感器信息融合 状态估计 协方差交叉 雷达网 

分 类 号：TP732[自动化与计算机技术—检测技术与自动化装置]