This paper presents a method for real-time identification of sensor statistics especially aimed for low-cost automotive-grade sensors. Based on recent developments in adaptive particle filtering (PF) and under the assumption of Gaussian distributed noise, our method identifies the slowly time-varying sensor offsets and variances jointly with the vehicle state, and it extends to banked roads. While the method is primarily focused on learning the noise characteristics of the sensors, it also produces an estimate of the vehicle state. This can then be used in driver-assistance systems, either as a direct input to the control system or indirectly to aid other sensor-fusion methods. The paper contains verification against several simulation and experimental data sets. The results indicate that our method is capable of bias-free estimation of both the bias and the variance of each sensor, that the estimation results are consistent over different data sets, and that the computational load is feasible for implementation on computationally limited embedded hardware typical of automotive applications.
Noise-Statistics Learning of Automotive-Grade Sensors Using Adaptive Marginalized Particle Filtering
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received July 5, 2018; final manuscript received January 22, 2019; published online February 21, 2019. Assoc. Editor: Jongeun Choi.
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Berntorp, K., and Di Cairano, S. (February 21, 2019). "Noise-Statistics Learning of Automotive-Grade Sensors Using Adaptive Marginalized Particle Filtering." ASME. J. Dyn. Sys., Meas., Control. June 2019; 141(6): 061009. https://doi.org/10.1115/1.4042673
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