Filters are some of the most common tools used in modern technology, and find numerous applications in robotics in both signal processing and controls. Understanding the notion of a filter is crucial to understanding the utility of the various types of filters provided by WPILib.
מה זה פילטר?
For the sake of this article, we will assume all data are single-dimensional time-series data. Obviously, the concepts involved are more general than this - but a full/rigorous discussion of signals and filtering is out of the scope of this documentation.
So, what exactly is a filter, then? Simply put, a filter is a mapping from a stream of inputs to a stream of outputs. That is to say, the value output by a filter (in principle) can depend not only on the current value of the input, but on the entire set of past and future values (of course, in practice, the filters provided by WPILib are implementable in real-time on streaming data; accordingly, they can only depend on the past values of the input, and not on future values). This is an important concept, because generally we use filters to remove/mitigate unwanted dynamics from a signal. When we filter a signal, we’re interested in modifying how the signal changes over time.
השפעות השימוש בפילטר
One of the most typical uses of a filter is for noise reduction. A filter that reduces noise is called a low-pass filter (because it allows low frequencies to ”pass through,“ while blocking high-frequencies). Most of the filters currently included in WPILib are effectively low-pass filters.
Filters are also commonly used to reduce the rate at which a signal can change. This is closely related to noise reduction, and filters that reduce noise also tend to limit the rate of change of their output.
The counterpart to the low-pass filter is the high-pass filter, which only permits high frequencies to pass through to the output. High-pass filters can be somewhat tricky to build intuition for, but a common usage for a high-pass filter is edge-detection - since high-pass filters will reflect sudden changes in the input while ignoring slower changes, they are useful for determining the location of sharp discontinuities in the signal.
An unavoidable negative effect of a real-time low-pass filter is the introduction of ”phase lag.“ Since, as mentioned earlier, a real-time filter can only depend on past values of the signal (we cannot time-travel to obtain the future values), the filtered value takes some time to ”catch up“ when the input starts changing. The greater the noise-reduction, the greater the introduced delay. This is, in many ways, the fundamental trade-off of real-time filtering, and should be the primary driving factor of your filter design.
Interestingly, high-pass filters introduce a phase lead, as opposed to a phase lag, as they exacerbate local changes to the value of the input.