CAJ | 학술논문

给出了带输入估计的卡尔曼滤波算法,比较了带输入估计的卡尔曼滤波算法和标准卡尔曼滤波算法的差别,在此基础上提出了一种最优加权的数据融合方法,分析了最优权值的分配原则,给出了最优加权下的多传感器融合算法.该算法首先计算出各个传感器当前的滤波精度,依据各传感器当前时刻的滤波精度分配权值,同时测量方差的时变特性使得每次测量信息得到充分的利用,通过仿真比较了该方法与平均分配权值方法效果的差别,实验结果表明了该算法的有效性.
급출료대수입고계적잡이만려파산법,비교료대수입고계적잡이만려파산법화표준잡이만려파산법적차별,재차기출상제출료일충최우가권적수거융합방법,분석료최우권치적분배원칙,급출료최우가권하적다전감기융합산법.해산법수선계산출각개전감기당전적려파정도,의거각전감기당전시각적려파정도분배권치,동시측량방차적시변특성사득매차측량신식득도충분적이용,통과방진비교료해방법여평균분배권치방법효과적차별,실험결과표명료해산법적유효성.
An unbiased input Kalman filter is advanced, and the difference between unbiased input Kalman filter and standards kalman filter is compared. An optimal weighted method in data fusion is presented. The distribution principle is analyzed for optimal weighted value. The algorithm reasonably distributes weight value according to the filter accuracy renewed of each sensor, meanwhile, the specific property of time-varying in measurement variance makes innovation obtained each time sufficiently utilized. Simulation com-pared the average value method and the method. And the simulation results show the effective of the new method.