But there are, in certain contexts, some very important compensating benefits for the moving average filter.
One class is referred to a non-recursive or moving average filter.
The measurement result is processed by the method of moving average filter.
The moving average filter is used to de-noising the GPS observations.
And the basic idea with a moving average filter is something that perhaps you're somewhat familiar with intuitively.
However, upon the application of a moving average filter, fault conditions can be more straightforwardly detected and diagnosed.
The proposed controller utilizes moving average filter (MAF) for the extraction of positive sequence fundamental component of distorted supply voltage.
A new integration Kalman filter is derived to compensate the time delay caused by the moving average filter.
The measurement of the integration Kalman filter is generated from a moving average filter in the proposed method.
Basically, what happens is that for the moving average filter, for a given set a filter specifications, there are many more multiplications required than for a recursive filter.
Now, let's increase the length of the moving average filter much more rapidly and watch how the output is more and more smooth in relation to the input.
