Performance analysis of two EM-based measurement bias estimation processes for tracking systems

针对目标跟踪的两种基于最大期望算法测量值偏差估计过程的性能分析（英文）

In target tracking, the measurements collected by sensors can be biased in some real scenarios, e.g., due to systematic error. To accurately estimate the target trajectory, it is essential that the measurement bias be identified in the first place. We investigate the iterative bias estimation process based on the expectation-maximization（EM）algorithm, for cases where sufficiently large numbers of measurements are at hand. With the assistance of extended Kalman filtering and smoothing, we derive two EM estimation processes to estimate the measurement bias which is formulated as a random variable in one state-space model and a constant value in another. More importantly,we theoretically derive the global convergence result of the EM-based measurement bias estimation and reveal the link between the two proposed EM estimation processes in the respective state-space models. It is found that the bias estimate in the second state-space model is more accurate and of less complexity. Furthermore, the EM-based iterative estimation converges faster in the second state-space model than in the first one. As a byproduct, the target trajectory can be simultaneously estimated with the measurement bias, after processing a batch of measurements.These results are confirmed by our simulations.

在现实目标跟踪问题中,传感器采集的测量值,比如系统缺陷,可能存在一定程度偏差。为正确估计目标的运动轨迹,测量值偏差的识别与估计将成为首要任务。首先,提出两种状态空间模型,分别把测量值偏差视为随机变量和常数。其次,假设测量值样本数足够大,基于最大期望算法,利用扩展卡尔曼滤波和平滑,针对两种模型提出不同的循环迭代偏差估计过程。最后,分析这两种估计过程的全局收敛结果,揭示其内在联系。结果表明,第二种估计过程比第一种估计过程更简单准确,并且收敛速度更快。批量处理测量值样本后,可同时获得目标运动轨迹和测量值偏差的估计结果。