![]() When the filter has overshoot, it becomes impossible to determine whether the signal is being distorted by the system that generates it, or by the filter. It is a distortion that should be eliminated if possible because it could mask critical system performance information. Overshoot is a filter-generated distortion of the time domain information, which shows up as ripples at the edges of the output step. Because time domain filters are typically used to help identify events in the signal (such as a bit boundary), the faster the risetime the better the filter. Risetime is the number of output samples between 10% of the output change and 90% of the change. Step responses have three important parameters: risetime, overshoot, and phase linearity.įigure 1: Step response is the filter's output given an abrupt change in the input signal. The step response, important in time domain applications, is the filter's output given an abrupt change in the input signal as illustrated in Figure 1. Filters are most easily understood from either their step response or their frequency response. With these two considerations in mind, you can begin evaluating filter characteristics against your application. They also suffer from performance limitations because finite word length arithmetic restricts the maximum IIR filter order that can be implemented. IIRs do, however, exhibit a non-linear phase response unless specifically designed for zero phase. This makes them both easier to implement and an order of magnitude faster in execution as a DSP algorithm. Recursive filters, also called Infinite Impulse Response (IIR) filters, can be implemented with far fewer resources than a corresponding FIR. High-order FIR filters can be readily implemented and will achieve extremely high performance, although they may require many resources to implement. There is a delay, of course, but all incoming samples receive the same treatment so that signal phase relationships are preserved. Convolution filters, also called Finite Impulse Response (FIR) filters, have the attribute of exhibiting no phase distortion. Robin, digital filters come in two types: convolution and recursive. As described in Digital Filters: an Introduction, by Iain A. The result of normal filtering followed by reverse filtering, however, is a signal with zero phase delay.Ī second consideration is the type of filter implementation you want to use. You have to wait as many samples as the filter order before you can calculate the first output value. Since you already have all of the past data values stored from the initial filter calculation, you simply run the filter using later values instead of prior values. This sounds like impossibility, but in the digital world it is quite simple. For example, a single sample from each element in an array of strain sensors on an aircraft ring yields a “signal” that can be processed using time-domain filters. The term time domain is somewhat misleading, however, in that not all such signals relate to time. Information may also lie in the time domain, where the amplitude and phase of the signal is of interest. It may lie in the frequency domain, where the spectral content of the signal is of interest. ![]() Information typically comes from one of two domains. The place to start is by knowing type of information a signal contains. What you need is a good handle on the basics of filter design to get the tools jump-started. The wide range of digital signal processing (DSP) design tools available can handle many of the details. Designing digital filters can seem a daunting task, however, because of its seemingly endless range of implementation choices. Compared to their analog counterparts, digital filters offer outstanding performance and flexibility. ![]()
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