센서 시스템의 매개변수 교정을 위한 데이터 기반 일괄 처리 방법
Data-Driven Batch Processing for Parameter Calibration of a Sensor System
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초록
When modeling a sensor system mathematically, we assume that the sensor noise is Gaussian and white to simplify the model. Ifthis assumption fails, the performance of the sensor model-based controller or estimator degrades due to incorrect modeling. In practice,non-Gaussian or non-white noise sources often arise in many digital sensor systems. Additionally, the noise parameters of the sensormodel are not known in advance without additional noise statistical information. Moreover, disturbances or high nonlinearities oftencause unknown sensor modeling errors. To estimate the uncertain noise and model parameters of a sensor system, this paper proposesan iterative batch calibration method using data-driven machine learning. Our simulation results validate the calibration performance ofthe proposed approach.
Abstract
When modeling a sensor system mathematically, we assume that the sensor noise is Gaussian and white to simplify the model. Ifthis assumption fails, the performance of the sensor model-based controller or estimator degrades due to incorrect modeling. In practice,non-Gaussian or non-white noise sources often arise in many digital sensor systems. Additionally, the noise parameters of the sensormodel are not known in advance without additional noise statistical information. Moreover, disturbances or high nonlinearities oftencause unknown sensor modeling errors. To estimate the uncertain noise and model parameters of a sensor system, this paper proposesan iterative batch calibration method using data-driven machine learning. Our simulation results validate the calibration performance ofthe proposed approach.
참고문헌 (12)
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