Month: March 2023

Blog Notes and Reader Poll

Final Update on “Future Posts” Poll:

So among the CSP Blog readers that voted, I think the consensus is to produce more “on brand” posts on CSP and the Signal-Processing ToolKit. Also, there is significant interest in doing CSP with GNU Radio, which I have considerable experience with, and so I’ll likely be posting some flowgraph ideas and results at some point in 2023.

Thanks everybody! (But I’ll still rant and rave from time to time; sorry!)

Update June 25, 2023: When I said you can vote multiple times, I didn’t mean to ‘spam’ the poll (as my kids would say). Someone just voted for one of the responses ten times in a row (same IP address ten votes within one minute). I meant you can vote for several different items in the poll! So I did remove some of those identical votes. I’ll close the poll at the end of the day June 30.

Update May 11, 2023: Please vote in the Reader Poll below (multiple times if you’d like) soon! As of today, CSP Applications and Signal Processing ToolKit are in the lead, with Rants and Datasets at the bottom.

The CSP Blog is rolling along here in 2023!

March 2023 broke a record for pageviews in a calendar month with over 7,000 as of this writing early in the day on March 31.

Let’s note some other milestones and introduce a poll.

Milestones

What a month! We’re at about 7,145 views right now, and the previous monthly record is 6,482.

2023 was the year that a CSP Blog post crossed the 20,000-view milestone: The Spectral Correlation Function. The Cyclic Autocorrelation Function is not far behind.

About 84,000 visitors have been counted over the years since the CSP Blog launched in 2015, with 5,500 this year already. I believe this is just a count of the unique IP addresses that have accessed a page. But the number of subscribers is only 198! You can subscribe (“Follow”) to the CSP Blog by entering an email address in the “Follow Blog via Email” box on the right edge of any viewed page, near the top of the page. You’ll get notified through that email address whenever there is a new post. CSP Blog readers cannot see that email address, just as they cannot see the email address associated with any comment, unless there is an associated gravatar.

Reader Poll

I’m planning to have more time available to devote to improving and extending the CSP Blog over the next few months. If you want to have input into that process, consider voting in the poll below.

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Future Posts

What type of CSP Blog post do you most want to see in 2023?

The poll has expired!

Thanks so much to all my readers!

The Altar of Optimality

Danger Will Robinson! Non-technical post approaching!

When I was a wee engineer, I’d sometimes clash with other engineers that sneered at technical approaches that didn’t set up a linear-algebraic optimization problem as the first step. Never mind that I’ve been relentlessly focused on single-sensor problems, rather than array-processing problems, and so the naturalness of the linear-algebraic mathematical setting was debatable–however there were still ways to fashion matrices and compute those lovely eigenvalues. The real issue wasn’t the dimensionality of the data model, it was that I didn’t have a handy crank I could turn and pop out a provably optimal solution to the posed problem. Therefore I could be safely ignored. And if nobody could actually write down an optimization problem for, say, general radio-frequency scene analysis, then that problem just wasn’t worth pursuing.

Those critical engineers worship at the altar of optimality. Time for another rant.

Continue reading “The Altar of Optimality”

SPTK Addendum: Problems with resampling using MATLAB’s resample.m

Sometimes MATLAB’s resample.m gives results that can be trouble for subsequent CSP.

Previous SPTK Post: Echo Detection Next SPTK Post: The Laplace Transform

In this brief Signal Processing Toolkit note, I warn you about relying on resample.m to increase the sampling rate of your data. It works fine a lot of the time, but when the signal has significant energy near the band edges, it does not.

Continue reading “SPTK Addendum: Problems with resampling using MATLAB’s resample.m”

Is Radio-Frequency Scene Analysis a Wicked Problem?

‘By the pricking of my thumbs, something wicked this way comes …’ Macbeth by W. Shakespeare

I attended a conference on dynamic spectrum access in 2017 and participated in a session on automatic modulation recognition. The session was connected to a live competition within the conference where participants would attempt to apply their modulation-recognition system to signals transmitted in the conference center by the conference organizers. Like a grand modulation-recognition challenge but confined to the temporal, spectral, and spatial constraints imposed by the short-duration conference.

What I didn’t know going in was the level of frustration on the part of the machine-learner organizers regarding the seeming inability of signal-processing and machine-learning researchers to solve the radio-frequency scene analysis problem once and for all. The basic attitude was ‘if the image-processors can have the AlexNet image-recognition solution, and thereby abandon their decades-long attempt at developing serious mathematics-based image-processing theory and practice, why haven’t we solved the RFSA problem yet?’

Continue reading “Is Radio-Frequency Scene Analysis a Wicked Problem?”

Frequency Shift (FRESH) Filtering for Single-Sensor Cochannel Signal Separation

CSP can be used to separate cochannel contemporaneous signals. The involved signal-processing structure is linear but periodically time-varying.

In most of the posts on the CSP Blog we’ve applied the theory and tools of CSP to parameter estimation of one sort or another: cycle-frequency estimation, time-delay estimation, synchronization-parameter estimation, and of course estimation of the spectral correlation, spectral coherence, cyclic cumulant, and cyclic polyspectral functions.

In this post, we’ll switch gears a bit and look at the problem of waveform estimation. This comes up in two situations for me: single-sensor processing and array (multi-sensor) processing. At some point, I’ll write a post on array processing for waveform estimation (using, say, the SCORE algorithm The Literature [R102]), but here we restrict our attention to the case of waveform estimation using only a single sensor (a single antenna connected to a single receiver). We just have one observed sampled waveform to work with. There are also waveform estimation methods that are multi-sensor but not typically referred to as array processing, such as the blind source separation problem in acoustic scene analysis, which is often solved by principal component analysis (PCA), independent component analysis (ICA), and their variants.

The signal model consists of the noisy sum of two or more modulated waveforms that overlap in both time and frequency. If the signals do not overlap in time, then we can separate them by time gating, and if they do not overlap in frequency, we can separate them using linear time-invariant systems (filters).

Relevant FRESH filtering publications include My Papers [45, 46] and The Literature [R6].

Continue reading “Frequency Shift (FRESH) Filtering for Single-Sensor Cochannel Signal Separation”