引入速率量测的滤波算法与初始估计研究

Research on a Kalman filtering adopting velocity and initial estimation

滤波器在滤波开始阶段的滤波效果在一定程度上取决于初始状态,选取的初始状态估计越精确,滤波的效果越好。首先对可以观测距变率和角变率的雷达观测系统提出了在三维空间中引入距变率(径向速度)和角变率(角速度)的伪线性卡尔曼滤波算法;其次针对初始状态选取的问题,以CV和CA模型为例,利用前2个时刻的测量值确定系统的初始状态,并讨论了初始状态估计和不同算法对滤波效果的改善情况。仿真试验证明,改善初始状态估计会使滤波算法收敛速度加快,收敛精度提高,改善跟踪性能。

At the beginning filtering stage the result is decided by the selection to initial estimation,the selection of the initial estimation more accurate,the result of the filtering better.In the three-dimensional space,a new method for the pseudo Kalman filtering using the radial velocity is presented for radar awareness system,which can detect target radial velocity and target angular velocity.Aiming at the problem that the initial estimation selection,this paper takes CV,CA model for example,using two measurements to determine initial estimation,discuss the influence of filtering caused by initial estimation and different filtering.Simulation results indicate that the improvement of initial estimation can increase the convergent precision,accelerate the convergent.