✈️ Building a Real-Time Aircraft Tracker in My Homelab⌗

Turning a Raspberry Pi and some containers into a live aviation dashboard⌗

🧠 Introduction⌗

One of the things I enjoy most about running a homelab is taking random pieces of technology and turning them into something genuinely useful (or at least really cool).

Recently, I set out to build a system that could answer a simple question:

“What aircraft is closest to my house right now?”

That question turned into a full-blown project involving:

  • a Raspberry Pi pulling real-time aircraft data

  • a time-series database

  • custom data transformations

  • and a Grafana dashboard showing live results

Along the way, I also improved my overall homelab setup—working with Docker, reverse proxies, DNS, and system monitoring.

🏠 The Homelab Foundation⌗

My setup is built around an older desktop that I repurposed as a home server. It’s not cutting-edge hardware, but it’s more than capable of running multiple services using Docker.

On top of that, I’ve layered a few core components:

  • Pi-hole for network-wide ad blocking and DNS control

  • Nginx as a reverse proxy for clean internal service URLs

  • Uptime Kuma for monitoring and alerting

  • SSH with key-based authentication for secure remote access

This gives me a flexible platform to spin up new services and experiment freely.

📡 Capturing Aircraft Data (ADS-B)⌗

The heart of the project is a Raspberry Pi running ADS-B software.

Aircraft continuously broadcast telemetry such as:

  • position (latitude/longitude)

  • altitude

  • speed

  • identification codes

Using an antenna and ADS-B feeder software, I’m able to capture this data locally and expose it via:

http://<pi-ip>:8080/data/aircraft.json

This endpoint returns a constantly updating JSON feed of nearby aircraft.

📈 Building the Data Pipeline⌗

Once I had the data source, the next step was figuring out how to store and analyze it.

I ended up with this pipeline:

ADS-B (Pi) → Telegraf → InfluxDB → Grafana

InfluxDB⌗

I used InfluxDB as a time-series database to store aircraft telemetry over time.

Getting this working involved:

  • configuring organizations and buckets

  • generating API tokens

  • debugging authentication issues (plenty of 401 errors…)

Telegraf⌗

Telegraf pulls the JSON feed and writes structured data into InfluxDB.

This step was crucial for turning raw JSON into something queryable.

🧠 Making Sense of the Data (Flux)⌗

Raw data isn’t very useful on its own. The real value came from writing Flux queries to transform it.

Some of the key things I implemented:

✈️ Aircraft Count⌗

How many aircraft are nearby at any given moment.

🛫 Altitude Filtering⌗

Filtering out high-altitude aircraft to focus on more interesting local traffic.

🔄 Deduplication⌗

Each aircraft sends multiple messages, so I had to:

  • group by aircraft (hex)

  • select only the most recent data point

📍 Distance Calculation⌗

This was the most interesting part.

Using my home’s latitude and longitude, I calculated an approximate distance to each aircraft and sorted them by proximity.

📊 Visualizing Everything with Grafana⌗

With the data flowing and queries working, I built out dashboards in Grafana.

Key Panels⌗

✈️ Aircraft Count⌗

A simple stat panel showing how many planes are nearby.

📈 Aircraft Over Time⌗

A time-series graph showing traffic patterns.

🛫 Low Flying Aircraft⌗

A filtered view highlighting planes below a certain altitude.

🔥 Closest Aircraft (My Favorite)⌗

A stat panel that shows:

  • the closest aircraft to my house

  • its distance in miles

  • its identifier (hex code for now)

Example:

Aircraft: AC60EF  
Distance: 0.26 mi

🧹 Cleaning Up the Dashboard⌗

A surprising amount of work went into making the dashboard readable:

  • fixing messy field names (_value, etc.)

  • removing duplicate rows

  • reordering columns

  • rounding values

  • adding units and thresholds

Grafana is powerful, but getting clean visuals takes some effort.

⚠️ Challenges Along the Way⌗

This project definitely wasn’t plug-and-play.

💻 Resource Limits⌗

Running a full ROM library scan (RomM) at the same time as this project:

  • maxed out CPU

  • filled RAM

  • triggered alerts

Lesson learned: resource management matters, especially on older hardware.

🔐 InfluxDB Authentication⌗

I ran into multiple issues with:

  • incorrect tokens

  • mismatched organization names

Result: lots of 401 Unauthorized errors until everything lined up exactly.

🧮 Flux Query Quirks⌗

Flux is powerful—but also finicky.

I ran into:

  • grouping issues

  • time column problems

  • math expression parsing errors

A lot of the work was figuring out how to structure queries correctly.

🚀 What I Built⌗

At the end of all this, I now have:

A self-hosted system that tracks aircraft in real time,
analyzes their proximity to my location,
and displays the results in a live dashboard.

It’s one of those projects that’s both:

  • technically valuable

  • and just plain fun to watch

🔮 What’s Next⌗

There’s plenty of room to expand this:

  • Replace hex codes with real flight numbers / airline names

  • Add a map visualization of aircraft

  • Trigger alerts when planes get very close

  • Upgrade my server hardware

  • Improve my network stack

😄 Final Thoughts⌗

This project is a great example of why I enjoy running a homelab:

You start with a simple idea,
and it turns into a full system involving networking,
data engineering, and visualization.

And at the end of it, you get something genuinely cool:

A live view of the sky above your house.