Generalized causal moving average (GCMA) smoothing filter for real-time applications

Ramesh Rajagopalan

doi:10.1117/12.509703

24 December 2003 Generalized causal moving average (GCMA) smoothing filter for real-time applications

Ramesh Rajagopalan

Proceedings Volume 5205, Advanced Signal Processing Algorithms, Architectures, and Implementations XIII; (2003) https://doi.org/10.1117/12.509703
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

Abstract

Moving average filters are commonly used in industries for real-time processing of noisy data. Though they perform well in filtering out the noise, they introduce significant lag in the signal. The resulting peak value of the filtered signal at the operating point is likely to be lower due to averaging of higher and lower peak signals in the averaging interval. The generalized moving average smoothing filter by Golay-Savitzky preserves the higher moments and does not suffer from the limitations imposed by the conventional moving average filter. The smoothing strategy is derived from least squares fitting of a lower order polynomial to a number of consecutive points. Due to polynomial curve fitting as opposed to a line fitting in the case of conventional moving average filter, this filter preserves the higher frequency components of the signal and their line width. This paper presents a generalized casual moving average filter deduced using the concepts in Golay-Savitzky smoothing filter for real-time applications. Golay-Savitzky filter is non-casual, relies on the future data that is not available, hence not suitable for real-time applications. Further, the designed casual filter makes use of the filtered data as opposed to the original data in the case of Golay-Savitzky. This approach allows us to conduct frequency response studies to evaluate the quality and the applicability of the filter for various signals in the aircraft engines and other engineering applications. Frequency response studies cannot carried out using the Golay-Savitzky filter. This paper also investigates the performance of various polynomial orders in reproducing the signal from a noisy data. Some of the performance measures used are bandwidth, overshoots, and lags introduced by the filter. The mathematical technique to extract the signal and deduce the coefficients in off-line is also presented.

Citation Download Citation

Ramesh Rajagopalan "Generalized causal moving average (GCMA) smoothing filter for real-time applications", Proc. SPIE 5205, Advanced Signal Processing Algorithms, Architectures, and Implementations XIII, (24 December 2003); https://doi.org/10.1117/12.509703

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