섭동 입력을 사용한 구배 값 추정용 센서 신호 처리
Sensor Signal Processing for Estimating Gradient Values using Perturbation Input
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초록
According to recent studies by scientists about how to search for food, homes and the mates, it is found that the gradient information plays a key role. From cells to insects and large animals, they mostly either have special sensing organism or use a strategy to measure the gradient. Use of a perturbation as an additional input is introduced for sensor signal processing in order to get the gradient information. Different from typical approach, which calculates the gradient from differentiation, the proposed processing is done by a form of integration, thus it is very robust to noise. Discrete time domain analyses are given for one, two and three input functions for the estimation of the gradients. The amplitude and the frequency of the per- turbation are two important parameters for this approach. A quantitative index to measure the effects of the amplitude is developed based on the linear regression analysis. The frequency of the perturbation is to be selected high enough to finish one period of the perturbation before the property is changed significantly with respect to time. Another quantitative index is proposed for guiding the selection of the frequency.
Abstract
According to recent studies by scientists about how to search for food, homes and the mates, it is found that the gradient information plays a key role. From cells to insects and large animals, they mostly either have special sensing organism or use a strategy to measure the gradient. Use of a perturbation as an additional input is introduced for sensor signal processing in order to get the gradient information. Different from typical approach, which calculates the gradient from differentiation, the proposed processing is done by a form of integration, thus it is very robust to noise. Discrete time domain analyses are given for one, two and three input functions for the estimation of the gradients. The amplitude and the frequency of the per- turbation are two important parameters for this approach. A quantitative index to measure the effects of the amplitude is developed based on the linear regression analysis. The frequency of the perturbation is to be selected high enough to finish one period of the perturbation before the property is changed significantly with respect to time. Another quantitative index is proposed for guiding the selection of the frequency.
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