libacars is a library for decoding ACARS message contents. Supports FANS-1/A ADS-C (Automatic Dependent Surveillance - Contract), FANS-1/A CPDLC (Controller-Pilot Data Link Communications), MIAM (Media Independent Aircraft Messaging), Media Advisory (Status of data links: VDL2, HF, Satcom, VHF ACARS), and OHMA (diagnostic messages exchanged with Boeing 737MAX aircraft) messages.
Comes with a couple of sample CLI utilities for exercising the library.
Acarsdec is a multi-channels acars decoder with built-in rtl_sdr, airspy front end or sdrplay device. Since 3.0, It comes with a database backend called acarsserv to store received acars messages.
Can decode up to 8 channels simultaneously. Does error detection and correction. Can take its input from rtl_sdr, airspy, or sdrplay software defined radios. Logs data over UDP in planeplotter or acarsserv formats to store data in a SQLite database, or JSON for custom processing. Can decode ARINC-622 ATS applications (ADS-C, CPDLC) via libacars library.
Multi-channel decoding is particularly useful with broadband devices such as the RTLSDR dongle, the AIRspy and the SDRplay device. It allows the user to directly monitor to up to 8 different frequencies simultaneously with very low cost hardware.
Looks like it interacts with the SDR directly because it has to control the frequencies it's listening on, so you can't piggyback it on, say, an existing ADS-B node.
Requires libusb, librtlsdr, libairspy, libmirsdrapi-rsp, and libacars (optional).
Airframes is a transportation (aviation, marine, etc) data aggregation service that receives ACARS, VDL, HFDL, SATCOM, and AIS data from volunteers around the world. This is similar to other efforts to collect, process, and display aircraft data like ADS-B, but with a focus on more interesting information, such as diagnostic, maintenance, and operational messages. It is under very active development and you will notice changes from day to day.
Contributing your feed allows us to make ground developing new decoders and make important statistical observations. It also benefits users of the service so that they can see more about flights as they traverse covered territories.
They're working on a REST API for participants.
Github: https://github.com/airframesio