{"id":40102,"date":"2025-12-15T10:48:00","date_gmt":"2025-12-15T15:48:00","guid":{"rendered":"https:\/\/www.dmcinfo.com\/?p=40102"},"modified":"2025-12-26T15:26:10","modified_gmt":"2025-12-26T20:26:10","slug":"simplifying-dsp-filters-for-embedded-systems","status":"publish","type":"post","link":"https:\/\/www.dmcinfo.com\/blog\/40102\/simplifying-dsp-filters-for-embedded-systems\/","title":{"rendered":"Simplifying DSP Filters for Embedded Systems"},"content":{"rendered":"\n

Acronyms are generally scary, and DSP is no exception. Digital Signal Processing (DSP)<\/a> is the backbone of many embedded systems, yet it\u2019s often misunderstood or over-complicated. But it doesn\u2019t have to be, and in a few paragraphs, I\u2019ll show you how to implement an extremely simple but functional low-pass digital filter.<\/p>\n\n\n\n

Low-Pass Signal Filtering<\/h2>\n\n\n\n

When working with embedded systems and reading data from sources prone to noisy measurements (e.g., temperature sensors), it\u2019s common to need a way to reduce high-frequency noise and reveal the slower, meaningful signal underneath. One effective approach is using a low-pass filter, which allows lower frequencies through while reducing higher frequencies. Low-pass filters are especially useful for cleaning up signals from ADCs (Analog-to-Digital Converters).<\/p>\n\n\n\n

Now, let’s explore how to implement a digital version of this filtering technique.<\/p>\n\n\n\n

Low-Pass IIR Filter<\/h2>\n\n\n\n

There are multiple types of digital filters available, but we\u2019ll focus on implementing an IIR (Infinite Impulse Response) filter<\/a>. Without diving too deeply into specifics, these filters incorporate feedback from the output into the input, effectively creating a recursive filter. Such filters are typically less complex, resulting in strong performance that is particularly valuable for embedded systems. It should be noted, however, that the feedback mechanism may impact stability, although we\u2019ll ignore this potential issue in this blog post.<\/p>\n\n\n\n

The actual filter calculation is very simple, we have to run this formula from Wikipedia<\/a> on every input sample to calculate the output: y[i] = \u03b1 * x[i] + (1 \u2013 \u03b1) * y[i-1]<\/p>\n\n\n\n

Let\u2019s not get confused by all these variables yet, but simplify this formula by rearranging it a bit, primarily to minimize the number of operations to improve performance: \u200by[i] = y[i-1] + \u03b1 * (x[i] – y[i-1])<\/p>\n\n\n\n

We can now explain these variables:<\/p>\n\n\n\n