You probably have at least a nodding familiarity with the Fourier transform, a mathematical process for transforming a time-domain signal into a frequency domain signal. In particular, for computers, we don’t really have a nice equation so we use the discrete version of the transform which takes a series of measurements at regular intervals. If you need to understand the entire frequency spectrum of a signal or you want to filter portions of the signal, this is definitely the tool for the job. However, sometimes it is more than you need.
For example, consider tuning a guitar string. You only need to know if one frequency is present or if it isn’t. If you are decoding TouchTones, you only need to know if two of eight frequencies are present. You don’t care about anything else.
A Fourier transform can do either of those jobs. But if you go that route you are going to do a lot of math to compute things you don’t care about just so you can pick out the one or two pieces you do care about. That’s the idea behind the Goertzel. It is essentially a fast Fourier transform algorithm stripped down to compute just one frequency band of interest. The math is much easier and you can usually implement it faster and smaller than a full transform, even on small CPUs.
Continue reading “DSP Spreadsheet: The Goertzel Algorithm Is Fourier’s Simpler Cousin”
We’ve done quite a bit with Google Sheets and signal processing: we’ve generated signals, created filters, and computed quadrature signals. We can pull all that together into an educational model for two SDRs talking to each other, but it’s going to require two parts: modulation and demodulation. Guess what? We can do that with a spreadsheet.
The first step is to generate a reference clock for the carrier. You’ll need a cosine wave (I) and sine wave (Q). Of course, you also need the time base. That’s columns A-C in the spreadsheet and works like other signal generation we’ve seen.
Continue reading “DSP Spreadsheet: Talking To Yourself Using IQ”
In previous installments of DSP Spreadsheet, we’ve looked at generating signals, mixing them, and filtering them. If you start trying to work with DSP, though, you’ll find a topic that always rears its head: IQ signals. It turns out, these aren’t as hard as they appear at first and, as usual, we’ll tackle them in a spreadsheet.
What does IQ stand for? The I stands for “in phase” and the Q stands for quadrature. By convention, the I signal is a cosine wave and the Q signal is a sine wave. Another way to say that is that the I and Q signals are 90 degrees out of phase. By manipulating the amplitude of I and Q, you can create complex modulation or, conversely, demodulate signals. We’ll see a spreadsheet that shows that completely next time.
Continue reading “DSP Spreadsheet: IQ Diagrams”
Circuit simulation and software workbooks like Matlab and Jupyter are great for being able to build things without a lot of overhead. But these all have some learning curve and often use clever tricks, abstractions, or library calls to obscure what’s really happening. Sometimes it is clearer to build math models in a spreadsheet.
You might think that spreadsheets aren’t built for doing frequency calculation and visualization but you’re wrong. That’s exactly what they’re made for — performing simple but repetative math and helping make sense of the results.
In this installment of the DSP Spreadsheet series, I’m going to talk about two simple yet fundamental things you’ll need to create mathematical models of signals: generating signals and mixing them. Since it is ubiquitous, I’ll use Google Sheets. Most of these examples will work on any spreadsheet, but at least everyone can share a Google Sheets document. Along the way, we’ll see a neat spreadsheet trick I should probably use more often.
Continue reading “DSP Spreadsheet: Frequency Mixing”
There’s an old saying: Tell me and I forget, teach me and I may remember, involve me and I learn. I’m guilty of this in a big way — I was never much on classroom learning. But if I build something or write some code, I’m more likely to understand how it works and why.
Circuit simulation and software workbooks like Matlab and Jupyter are great for being able to build things without a lot of overhead. But these all have some learning curve and often use clever tricks, abstractions, or library calls to obscure what’s really happening. Sometimes it is easier to build something in a spreadsheet. In fact, I often do little circuit design spreadsheets or even digital design because it forces me to create a mathematical model which, in turn, helps me understand what’s really going on.
In this article I’m going to use Google Sheets — although you could do the same tricks in just about any spreadsheet — to generate some data and apply a finite impulse response (FIR) filter to it. Of course, if you had a spreadsheet of data from an instrument, this same technique would work, too.
Continue reading “DSP Spreadsheet: FIR Filtering”