基于自适应迭代UKF的纯距离目标定位算法

Range-only target location algorithm based on adaptive IUKF

针对迭代无迹卡尔曼滤波(IUKF)需要人工设定迭代次数的问题,引入遗传算法中适应度函数的概念,提出一种自适应迭代卡尔曼滤波的跟踪算法(AIUKF)。该算法利用观测预测值与实际观测值、系统采样点与实际观测值的适应度函数作为评价标准,根据适应度函数的比值自适应确定是否进行迭代。仿真结果表明:新算法适用于纯距离系统,可以有效解决IUKF人工设定迭代数的问题,且算法性能与IUKF性能相当,均优于UKF性能。

Since the iterated unscented Kalman filter（IUKF） has the problem of setting iterative times, according to the fitness function from genetic algorithm, an adaptive iterated unscented Kalman filter（AIUKF） was proposed. The new algorithm calculated the fitness of the predicted values and the observed values, the fitness of the sampling points and the observed values, then adaptive adjust whether iteration or not based on the ratio of fitness functions. The simulation results indicate that the AIUKF has better performance than standard UKF in range-only target motion analysis, and can solve the problem of setting iterative times in IUKF.