时滞和相关性观测条件下的红外目标状态估计方法

着重对红外跟踪系统目标状态估计过程中存在的观测量滞后和相关性问题进行研究。在目标跟踪过程中,脱靶量信号的滞后导致目标量测位置和真实值间是有误差的,进而通过状态估计得到的目标参数也是不准确的,另外观测量的相关性也降低了估计效果的准确性。对于以上问题,首先利用观测重组技术对时滞观测序列进行重组,将其转化为变结构无时滞观测系统,并在此基础上提出了一种时滞椭球集员估计算法(Delayed ellipsoidal set filter,DESF)。仿真实验结果表明,DESF算法可以有效克服观测序列相关性的影响,同时系统跟踪性能较不考虑观测延迟的情况也有显著提高。

Research on estimation for infrared target state with observation of delay time and pertinence sequences

The delay and pertinence of observation sequence in the infrared tracking system affect estimation to the target estate. In the course of tracking, delay of observation sequence induces the error between the measurement and truth, and then estimation based on the measurement is inaccurate, at the same time, pertinence of observation sequence affects the estimation result too. To assure the veracity of prediction, firstly, the system model was modified by ellipsoidal set form to overcome the pertinence of observation sequence, and at the same time, the innovation re-organization technique was introduced to eliminate the observation delay. With the innovation re-organization, the system structure was changed and a model without delay was obtained. At last, a delayed ellipsoidal set filter(DESF) was presented. Result of simulation shows that, DESF algorithm could overcome the effect of pertinence of observation sequence, and compared with filter designed without innovation re-organization, the tracking precision is better.