Real-Time Optimization of Single Pole Low Pass Filter using Signal-to-Noise Ratio Maximization

In this paper, further studies have been made on improving the output performance optimization of the exponentially weighted moving average filter for real-time system applications. It has been shown in prior research that the filter is an effective, low-cost filtering algorithm; however, its output performance is dependent on the calibration of its gain parameter. As a result, the filter will produce sub-optimal outputs if not calibrated correctly. A method is sought that can provide an appropriate gain parameter for maximizing the signal-to-noise ratio of the filter's resulting output, thereby increasing the effectiveness of the filter for real-time system applications. A trial-and-error experimental approach was used to find the filter's optimal parameter gain that maximizes the filter's output signal to noise ratio for a known signal that is sampled using a Gaussian distribution model. It was found that the output signal performance of the complementary filter is highly dependent on its chosen parameter gain, and using a static parameter gain value is unsuitable. An equation was found that approximates an optimal parameter gain to produce a filtered output with the best signal to noise ratio and applied to a real-world application.