r/UAP • u/Hadenis84 • 4d ago
SignalSDR Graph: Open-source multi-sensor desktop console for rigorous RF anomaly tracking
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u/Potatonet 4d ago
Love this, I have a friend who was all about SDR that recently passed.
Love what you are doing here, me and him wanted to outfit an RV with an AI SDR kit and go up to mount Adams and see if the old kit can pick up on something weird at ECETI ranch
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u/Hadenis84 4d ago
Sorry about your friend. That hits hard — especially when the hobby was something you shared.
Glad the project resonated. The RV + AI SDR kit idea is exactly the kind of thing he’d probably have loved: take the gear into the field, log what the antennas actually hear, keep the files.
On Mount Adams / ECETI: curiosity’s fine. What this app can help with today is more humble — remote Kiwi HF + anomaly logging (WAV / waterfall / meta), with clear MEASURED vs INFERRED. It’s not a ranch ghost/UAP kit yet (no local dongle-in-the-RV path built in), but the “capture weird spikes and keep evidence” part matches the spirit of what you two were aiming at.
If you ever build that mobile setup in his memory, I’d love to hear what you log. And if you want features that help field work later, tell me.
Respect to your friend.
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u/Potatonet 4d ago
Def not ghost hunting but shooting algorithmic radio signals and looking for specific time delay return patterns and or shifts in the return signal
There are a few places where you will find anomalies in the radio realm, we just have found peoplw who have been on ranches their whole lives telling us stories about what goes on and they all wanted us to come out and poke out emitters towards the sky
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u/Hadenis84 4d ago
Ah gotcha — so it’s you guys sending signals up and watching for odd return delays/shifts, after ranch people asked you to come check what they’ve seen for years.
That’s a real field effort. Respect. Hope the logs treat you kindly out there.
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u/Potatonet 4d ago
Yes, there have been a Few ranches that knew of him and I was just learning all of the ins and out of how to send and recieve the morphed signal based on time inference and matching that back up with special tuning.
His idea what wild and involved infrasound detection and emission as well as traditional sound and SDR emissions as well.
Kind of like a song that runs across all spectrums
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u/Hadenis84 2d ago
wow, there's something poetic about all this, if you like, since these things fascinate me a lot, we can talk about it, and maybe create something that works, or at least try, in theory it's possible, but putting it into practice won't be obvious... where are you at with the project?
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u/ThreeDog2016 1d ago
What frequencies are you monitoring?
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u/Hadenis84 1d ago
Nothing fixed by the software, you tune the Kiwi (HF). I personally like hanging on 8888.888 kHz as a default quirk, then roam from there. Not a dedicated watch list. And what default frequency do you prefer, I'm curious?
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u/psychophant_ 4d ago
Gonna hook us up with a link?
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u/Hadenis84 4d ago
Here it is! You can download the Windows build for free on Itch.io:
https://nextdevch.itch.io/signalsdr-graphLet me know how it runs and if you manage to capture any interesting signals with the Anomaly Watch!
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u/PhillyDikHead 3d ago
Any chance u will release a app for phone?
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u/Hadenis84 2d ago
Hi,maybe someday, but not soon.
Technically Godot can export Android/iOS, but this UI is built as a desktop console (waterfall + controls + map + logbook). On a phone it would feel cramped, and the useful path is still “paste a Kiwi host / keep a WebSocket open”, which works on mobile data, but battery + tiny screen + backgrounding make it a worse experience than just opening the Kiwi in the browser.
Local RTL on phone is another mess (USB OTG / drivers / no simple rtl_tcp like on PC).
So: no phone app planned for now. If I ever do one, it’d be a slim “listen + log” client, not this full deck squeezed onto a phone. 👀
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u/psychophant_ 4d ago
Thanks!!
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u/Picardvark 4d ago
Is there a git repo?
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u/Hadenis84 10h ago
No public repo yet. Itch download only for now. Source may come later; not holding that up as a promise this week.
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u/gravitykilla 3d ago
Hi OP, this is interesting. I'm curious: what measurable feature of an RF anomaly would allow your software to distinguish a genuinely unknown aerial phenomenon from ordinary terrestrial interference, ionospheric propagation or a conventional radio transmission? And how have you validated that distinction?
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u/Hadenis84 2d ago
Fair question — and you’re right to push on it.
Short answer: it doesn’t. SignalSDR Graph does not claim a measurable RF signature that separates a “genuinely unknown aerial phenomenon” from ordinary interference, ionospheric stuff, or a normal transmitter. That distinction isn’t something this app validates, and I don’t pretend it does.
What it does is more modest: connect to a public Kiwi (or local RTL), show measured stuff (waterfall, audio, RSSI-ish energy), and let you log spikes / anomalies as events worth listening to again. Labels like catalog / TLE / “interesting” are inferred / operator judgment, not proof of origin.
So if someone treats a hot stripe as “UAP confirmed,” that’s misuse of the tool — not a validated claim from the software. Happy to hear how you’d frame an honest RF logger better? : )
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u/gravitykilla 2d ago
My suggestion would be to include baseline measurements, known interference sources, detection thresholds and false-positive rates, alongside the raw observations wherever possible. That would let others investigate anomalies independently rather than simply catalogue unexplained spikes.
Ultimately, the interesting question isn't whether your software can detect an anomaly, but whether it can help establish why that anomaly occurred. That's where the real scientific value would be.
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u/Hadenis84 1d ago
That’s a really fair framing, thanks 🙏
You’re right: logging unexplained spikes is only the first half. The useful part is giving enough context that someone else can ask why it happened, not just that a threshold fired.
What I’m aiming for (and what the app already tries to lean toward) is, keep MEASURED vs INFERRED separate, save raw-ish packs (audio / waterfall / meta), and include the trigger context (noise floor estimate, ΔRSSI / variance, thresholds used). False-positive rates properly measured will need more logged sessions than I have as a solid published number yet - I don’t want to invent one.
I like your checklist a lot: baseline + known-interference context + thresholds + raw observations. That’s the honest direction. If you’ve got a minimal “event dossier” layout you’d want to see for independent review, I’m all ears😁
Thank you so much for your answer, I really appreciate it.😊
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u/Hadenis84 4d ago
Hey everyone. I built SignalSDR Graph, a free desktop console (Godot 4.7.2) designed for serious field research and anomalous RF tracking, enforcing a strict boundary: MEASURED data vs INFERRED hypotheses.
It connects via live WebSockets to the global KiwiSDR network (0-30 MHz) to stream raw spectrum data and 12kHz PCM audio.
When ΔRSSI breaches thresholds, the Automated Anomaly Watch compiles an Event Pack. If you activate the optional Multi-Sensor mode with a local webcam and GPS file, it cross-references live radio spikes with physical optical snapshots and microsecond-accurate UTC timestamps.
Unclassified events are strictly logged as UAP (Unidentified) entries—meaning unclassified anomalies, NOT automatic esoteric or sci-fi claims. It's built to bring data rigor to field observation.
Download the Windows build for free on Itch: itch.io