多传感器系统的偏差补偿和状态估计

Bias compensation and state estimation for multi-sensor systems

在多传感器目标跟踪系统中,偏差补偿对于正确的数据融合至关重要,消除异步多传感器的偏差是多传感器精确数据融合的基础。针对量测存在时间偏差和系统偏差的多传感器系统,提出一种新的多传感器误差偏移估计方法。首先,推导并计算了时间偏移伪测量方程,以获得相对时间偏移估计;其次将传感器系统偏差与目标状态相结合以获得增强状态向量,并建立了增强状态模型;最后,设计了一种基于扩展卡尔曼滤波的目标状态与传感器系统偏差扩维联合估计算法。实验结果表明,在量测存在时间偏差和系统偏差的情况下,所提算法可以获得相对时间偏移的无偏估计,同时能够有效地解决带有系统误差的状态估计问题。

In multisensor target tracking system,deviation compensation was very important for correct data fusion.Eliminating the deviation of asynchronous multisensor was the basis of accurate multisensor data fusion.A new method of multisensor error offset estimation was proposed for multisensor systems with time bias and system bias.Firstly,the pseudo measurement equation of time migration was derived and calculated to obtain the estimation of relative time migration;Secondly,the sensor system bias was combined with the target state to obtain the enhanced state vector,and the enhanced state model was established.Finally,a joint estimation algorithm of target state and sensor system deviation based on extended Kalman filter was designed.Experimental results showed that the proposed algorithm could obtain unbiased estimation of relative time offset and effectively solve the problem of state estimation with systematic error in the presence of time bias and system bias in the measurement.