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上世纪60年代由kalman(匈牙利)所研发的卡尔曼滤波(KF)算法是一种误差较小的线性估算法,此种算法所占数据量较小,设计简单,实用性强,具有良好的抗干扰性能,是一种较为常用的信号处理算法。但传统的卡尔曼滤波算法需要得知自身系统以及被测目标的噪声统计特性,受一些因素的影响,难以精确得知被测目标的噪声统计特性,这就对卡尔曼算法造成了一定制约,基于此,本文就以此为切入点,提出了卡尔曼滤波算法的优化途径,并对优化卡尔曼率比算法的目标函数选择进行解析。
Kalman filter (KF) algorithm developed by kalman (Hungary) in 1960s is a kind of linear estimation method with small error. This algorithm takes up a small amount of data, has simple design, strong practicability and good Anti-interference performance, is a more commonly used signal processing algorithms. However, the traditional Kalman filter algorithm needs to know the statistical characteristics of its own system and the noise of the measured target. Due to some factors, it is difficult to know the statistical characteristics of the noise of the measured target accurately. This restricts the Kalman algorithm, Based on this, this paper takes this as the starting point, puts forward the optimization method of Kalman filter algorithm, and analyzes the selection of the objective function of the optimized Kalman ratio algorithm.