In target tracking systems involving data fusion it is common to encounter sensor measurement biases that contribute to the tracking errors. There is extensive research into estimating sensor biases, but very little research into bias estimation in the dynamic case, meaning that biases that change over time are addressed. This paper investigates the means for and necessity of estimating bias rates of change in addition to constant sensor biases to reduce the errors in the state estimates. This is explored by comparing the Cramér–Rao lower bound and root-mean-square error of simultaneous target state and bias estimates for rotational biases with three-dimensional passive sensors with roll, pitch, and yaw biases. The present work models the dynamic biases as linearly varying over time. The iterated least squares method is used for the search of the maximum likelihood estimate, and is shown to be statistically efficient via hypothesis testing.

中文翻译：

---

用于估计无源传感器中随时间变化的旋转偏差的 CRLB

在涉及数据融合的目标跟踪系统中，经常会遇到导致跟踪误差的传感器测量偏差。对估计传感器偏差有广泛的研究，但对动态情况下的偏差估计的研究很少，这意味着随时间变化的偏差得到解决。本文研究了在恒定传感器偏差之外估计变化偏差率的方法和必要性，以减少状态估计中的误差。这是通过比较 Cramér-Rao 下界和均方根误差的同时目标状态和偏差估计与具有滚转、俯仰和偏航偏差的三维无源传感器的旋转偏差进行的。目前的工作将动态偏差建模为随时间线性变化。