Bias Estimation for Collocated Sensors: Model Identification and Measurement Fusion

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1 December 2020
Kaipei Yang, Yaakov Bar-Shalom, Peter Willett, Hiroshi Inou

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The sensor bias estimation problem is crucial in autonomous driving systems for perception and target tracking. This work considers the bias estimation for two collocated synchronized sensors with slowly varying additive biases. The differences between the two sensors’ observations are used to eliminate the target state. Consequently, the bias estimation is independent of the target-state estimation. The biases’ observability condition is met when the two sensors’ biases are Ornstein–Uhlenbeck stochastic processes with different time constants. The bias models, including the time constants and measurement noises, can be identified based on a sample autocorrelation or using the maximum-likelihood estimation technique. A maximum-likelihood measurement fusion technique is introduced for the bias-compensated observations. Simulation results, for several scenarios with various bias model parameters, prove the consistency of the estimator. It is shown that the uncertainties of biases are significantly reduced by the estimation algorithm presented. The sensitivity of the proposed algorithm is also tested with mismatched filters as well as the estimated bias models. Finally, the benefits of bias estimation in measurement fusion are evaluated.