oe-gnss-sdr-manifest

New release of the Geniux Warrior manifest. It contains a list of Git repositories and their respective commit hashes that allow building images and SDKs defined by the meta-gnss-sdr OpenEmbedded layer

The name Geniux comes from <ins>**G**</ins>NSS-SDR for <ins>**E**</ins>mbedded G<ins>**N**</ins>U/L<ins>**i**</ins>n<ins>**ux**</ins>.

Geniux Warrior v22.06 is a customized embedded Linux distribution based on the Yocto Project version 2.7.4. Main features:

• Development tools: Automake v1.16.1, CMake v3.14.1, GCC v8.3.0 (+ libgfortran), make v4.2.1, ninja v1.9.0, Python v2.7.18 and v3.7.7.
• Goodies for signal processing:
  • SDR framework: GNU Radio v3.8.2.0.
  • Number crunching libraries: Armadillo v10.8.0, FFTW v3.3.9, Lapack v3.7.0, VOLK v2.3.0.
  • C++ supporting libraries: Boost v1.69.0, gflags v2.2.2, glog v0.5.0, googletest v1.11.0, Matio v1.5.23, Protocol Buffers v3.6.1, Pugixml v1.11.4.
  • Graphical representation: gnss-sdr-monitor v1.0, Gnuplot v5.2.2.
• Software drivers and tools for RF front-ends: UHD v3.15.LTS (+ gr-uhd), gr-osmosdr v0.2.3 (+ rtl-sdr and hackrf), gr-iio v0.3, libiio v0.23, libad9361-iio v0.2, iio-oscilloscope v0.14.
• GNSS-SDR v0.0.17-git-342d378

More info at https://gnss-sdr.org/docs/tutorials/cross-compiling/

Changes with respect to Geniux Warrior 22.02

• Update matio to v1.5.23.
• Added dma-proxy and dma-proxy test recipes for Xilinx boards with aarch64 architecture (e.g., zcu102-zynqmp machine).
• Update Googletest to v1.12.0.
• Update GNSS-SDR to v0.0.17-git-342d378

How to build images and the SDK

With Docker already installed on your system, build the SDK and images for your preferred machine:

$ git clone https://github.com/carlesfernandez/yocto-geniux
$ cd yocto-geniux
$ ./geniux-builder.sh warrior 22.06 zedboard-zynq7

The generated yocto-geniux image also provides an interactive mode that allows users to make changes, experiment, fine-tune, and generate their own custom images and SDKs according to their specific requirements in a virtualized environment. Please check the README.md file of that repository for instructions.

Copyright and Licence

Copyright: © 2016-2022 Carles Fernández-Prades, CTTC. All rights reserved.

The content of this repository is released under the MIT licence.

Acknowledgements

This work was partially supported by the Spanish Ministry of Science, Innovation, and Universities through the Statistical Learning and Inference for Large Dimensional Communication Systems (ARISTIDES, RTI2018-099722-B-I00) project.