Self-Calibration from Measurements of Targets with Known Dynamics

摘要

In this paper, the problem of estimating sensor biases (e.g., range and bearing biases) from measurements of targets with deterministic dynamics but uncertain initial conditions is considered. The known dynamics are exploited by a single sensor to self-calibrate or determine unknown sensor biases. The concept of bias state tracklet fusion from tracks of multiple trajectories is discussed. The effectiveness of this concept is demonstrated, and the performance sensitivity to geometry variations and the number of available targets is examined. For comparison, the bias state tracklet estimator is compared to a nonlinear least squares (NLS) estimator.