Building a GPS receiver

Hacker News

Hi everyone! Shortly after publishing my iOS 4 jailbreak last October[1], I got to work on my next hobby project: a from-scratch homebrew GPS receiver, which can solve the user’s location solely from billions of radio antenna samples. I took a commodity SDR (alongside the Python standard library and numpy) and built a signal processing pipeline that can detect and track GPS satellites over many minutes, drop and pick up satellites as they come in and out of view, and precisely determine the user’s position and clock inaccuracy. All told, gypsum can go from a cold start to a fix on the user’s position, and the precise time, in less than a minute of listening to the antenna. I went on a journey of learning how to detect and track satellite signals that are literally too quiet to hear, and I hope that some of the magic comes through in the posts! After implementing this myself and walking the long road of getting it working, I’m left completely stunned by the brilliance of GPS, across so many axes. I hope you enjoy the read! On a more personal note, I’ll be starting a new job next week which isn’t as amenable to publishing side projects, and therefore this will be my last publicly-published project for some time. I’ve had great experiences making and sharing projects on here, and I’m really grateful for the positive feedback that’s been shared! [1]: https://news.ycombinator.com/item?id=37736318

Visit product View on Hacker NewsApr 15, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: ios, personal · Missing: mobile apps, entrepreneurs, apps

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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