Indeed, and I find the 1st order lowpass to be the simplest, and it gets the most use. Kalman Filter is powerful, but overrated. I'd love to get there eventually, but sadly I don't have time for videos these days
*Abstract:* The video provides a comprehensive overview of the Exponential Moving Average (EMA) filter, a fundamental tool in signal processing. The presentation covers the filter's basic properties, implementation, and applications. *Key Points:* * *Definition and History:* The EMA filter is a first-order, low-pass filter with origins in 19th-century mathematics. It calculates the output (y) by recursively combining the current input (u) and previous output (y) with complementary coefficients (alpha and 1-alpha). * *Tuning:* The time constant (tau), which is the inverse of alpha, is a more intuitive parameter for tuning the filter's behavior. * *Implementation:* The EMA filter can be easily implemented in code due to its recursive nature. * *Properties:* * *Delay:* Causes a delay between 1 and 3 time constants. * *Cutoff Frequency:* The cutoff frequency is the inverse of the time constant (tau). * *Noise Reduction:* Reduces white noise with a factor related to the time constant. * *Comparison to Simple Moving Average (SMA):* While both filters offer similar noise reduction and delay properties, the EMA has advantages in memory usage, ease of implementation, and adaptability. i used gemini
Terrific explanation. Would love a series where you go over other filters like you mentioned with the iceberg.
Great video! Very well made
It would be interesting to review the different windowing options we can apply to discrete time sampled data :)
Do you mean the Hamming window and the like?
Was going to suggest Kalman but then i saw it at the bottom of the ocean. I think Filters that are easy to implement are the best in most scenarios
Indeed, and I find the 1st order lowpass to be the simplest, and it gets the most use. Kalman Filter is powerful, but overrated. I'd love to get there eventually, but sadly I don't have time for videos these days
*Abstract:*
The video provides a comprehensive overview of the Exponential Moving Average (EMA) filter, a fundamental tool in signal processing. The presentation covers the filter's basic properties, implementation, and applications.
*Key Points:*
* *Definition and History:* The EMA filter is a first-order, low-pass filter with origins in 19th-century mathematics. It calculates the output (y) by recursively combining the current input (u) and previous output (y) with complementary coefficients (alpha and 1-alpha).
* *Tuning:* The time constant (tau), which is the inverse of alpha, is a more intuitive parameter for tuning the filter's behavior.
* *Implementation:* The EMA filter can be easily implemented in code due to its recursive nature.
* *Properties:*
* *Delay:* Causes a delay between 1 and 3 time constants.
* *Cutoff Frequency:* The cutoff frequency is the inverse of the time constant (tau).
* *Noise Reduction:* Reduces white noise with a factor related to the time constant.
* *Comparison to Simple Moving Average (SMA):* While both filters offer similar noise reduction and delay properties, the EMA has advantages in memory usage, ease of implementation, and adaptability.
i used gemini