Bias estimation for collocated sensors with a target of opportunity and measurement fusion

Kaipei Yang; Yaakov Bar-Shalom; Peter Willett; Hiroshi Inou

doi:10.1117/12.2557830

22 April 2020 Bias estimation for collocated sensors with a target of opportunity and measurement fusion

Kaipei Yang, Yaakov Bar-Shalom, Peter Willett, Hiroshi Inou

Proceedings Volume 11423, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX; 1142303 (2020) https://doi.org/10.1117/12.2557830
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

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 from the target state estimation. The biases’ observability condition is met when the two sensors’ biases are Ornstein-Uhlenbeck stochastic processes with different time constants. 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.

Conference Presentation

Citation Download Citation

Kaipei Yang, Yaakov Bar-Shalom, Peter Willett, and Hiroshi Inou "Bias estimation for collocated sensors with a target of opportunity and measurement fusion", Proc. SPIE 11423, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX, 1142303 (22 April 2020); https://doi.org/10.1117/12.2557830

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