Measurement Bias Estimation in the Problem of Target Tracking

Abstract

In the problem of target tracking, different types of biases can enter into the measurement collected by sensors due to various reasons. In order to accurately track the target, it is essential to estimate and correct the measurement bias. Considering practical backgrounds, the bias is assumed to be locally stationary Gaussian distributed and an iterative estimation algorithm is proposed. Firstly, a mechanism is established to detect whether the bias switches between different Gaussian distributions. Secondly, the expectation maximization algorithm with the assistance of extended Kalman filtering and smoothing is proposed to iteratively estimate the bias and target state in an offline manner. Simulations show the proposed algorithm can suppress the impact of the measurement bias on target tracking.

Investigating the application of Kalman Filters for real-time accountancy in fusion fuel cycles

“…This would essentially make the Kalman Filter usel ess; however, it is used very successfully in tracking, forecasting, self-driving cars, groundwater, and others. This is because there are ways to either correct the measurements prio r to applying the Kalman Filter o r improve upon the Kalman Filter to estimate th e different random erro rs and biases [17,18]. Th e bias may be treated s eparately through a two -step Kalman Filter and can either be fed b ack into the model where it is used to update the bi ased state estimation, or it is not fed back into the model [19].…”