An all-in-one Docker container for ADS-B data collection, visualization, and multi-feeder aggregation with built-in MLAT hub.
ADSB-Ultrafeeder is an all-in-one Docker container that collects and processes ADS-B data from Software Defined Radios (SDRs) or external sources. It decodes aircraft signals, displays them on a local web map with performance graphs, and forwards data to multiple flight tracking aggregators while handling MLAT (Multilateration) calculations. It solves the complexity of setting up and managing separate ADS-B decoding, visualization, and feeding tools by bundling them into a single, configurable package.
Aviation enthusiasts, hobbyists, and developers who operate ADS-B ground stations, want to visualize local air traffic, and contribute data to flight tracking networks. It's also suitable for those needing a self-hosted, customizable alternative to proprietary feeder software.
Developers choose ADSB-Ultrafeeder for its comprehensive feature set in a single container, eliminating the need to manage multiple dependencies. Its built-in MLAT hub, support for numerous aggregators, and extensive configuration options provide flexibility and control unmatched by simpler feeders.
ADSB-Ultrafeeder is an all-in-one ADSB container with readsb, tar1090, graphs1090, autogain, multi-feeder, and mlat-hub built in
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Bundles readsb for decoding, tar1090 for real-time mapping, graphs1090 for metrics, and an MLAT client into a single container, eliminating dependency management as noted in the README's architecture overview.
Simultaneously forwards ADS-B and MLAT data to numerous services like ADSB.lol and ADSB Exchange using the ULTRAFEEDER_CONFIG parameter, supporting community-driven networks.
Consolidates MLAT results from various sources into Beast or SBS formats, making it easy to integrate external data and display unified positions on the tar1090 map.
Automatically adjusts SDR gain using built-in or legacy algorithms, with configurable parameters for signal strength optimization, as detailed in the AutoGain section.
Requires setting numerous environment variables and understanding protocols like Beast and SBS, with the README admitting that accurate GPS and NTP sync are critical for MLAT, adding to setup difficulty.
Locked into containerized environments, making it unsuitable for systems without Docker or docker-compose, and introducing potential overhead on resource-constrained devices like Raspberry Pi.
The README warns that MLAT often fails on virtual machines or with incorrect clock sync, requiring troubleshooting steps that may deter less technical users.