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CUDA Range-Doppler Processing

This project provides CUDA-based source code to process radar data from an FMCW-radar into range and range-Doppler maps. Furthermore, the project includes various benchmarks that measure the execution time of the present algorithms, the used CUDA-kernel, 1D FFTs based on the cufft library and memory bandwidth.

Getting Started

To get started with the project some requirements are necessary to install.

Requirements

  • Just tested in Ubuntu 18.04 LTS
  • CUDA 9.1 or later versions and a CUDA capable GPU - installation guide for the latest version
  • GCC/G++ Compiler 6.3 or later versions - installation guide
  • CSNET GPUJPEG library - source code and installation guide can be found here

Installing

If the requirements are met, the project can be installed as follows.

1.) If the project is not downloaded yet, open a terminal and point to a desired folder ($DIR), otherwise proceed with step 3.)

cd $DIR

2.) clone the latest release from github

git clone https://github.com/NiclasEsser1/CUDARangeDopplerProcessing.git

3.) Point the console to the root folder of the project

cd CUDARangeDopplerProcessing

4.) Compile & build

make

The project should be compiled. If not, take a closer look into makefile and verify that the paths of libraries and header are adjusted to your system.

Running

The project provides an example for offline processing bin/offline_processing. If the file is executed, processing configurations must be passed to the program. First, a raw data file must be specified. To verify the installation process, a sample file is given.

$DIR/samples/raw_sample.dat

After you passed the remaining processing arguments, the algorithm is executed on the GPU. You can find the results of the processing in $DIR/results/img/

Read-out your own raw data

The raw data file corresponds to the default format shown in the Figure below. Binary file for offline processing

If your raw data does not match this format, feel free to add your own read-out method (e.g SignalGenerator::loadMyFile).

Benchmarks

The project also contains different benchmarks:

  • Benchmark the algorithm (results are displayed within the terminal)
$DIR/bin/benchmark_algorithm
  • Benchmark single CUDA kernels (results can be found in $DIR/results/benchmarks/benchmark_kernels.csv)
$DIR/bin/benchmark_kernels
  • Benchmark memory bandwidth (results can be found in $DIR/results/benchmarks/benchmark_memory_bandwidth.csv)
$DIR/bin/benchmark_memory_bandwidth
  • Benchmark 1D FFTs (results can be found in $DIR/results/benchmarks/benchmark_fft.csv)
$DIR/bin/benchmark_fft

Algorithms Results (tested on Geforce 960m GTX)

width height Range-Doppler Range
256 256 2.98 ms 2.90 ms
512 512 3.85 ms 3.02 ms
1024 1024 7.17 ms 4.9 ms
2048 2048 17.92 ms 10.34 ms
4096 4096 68.56 ms 40.192 ms
8192 4096 140.84 ms 82.14 ms

More Information

More information can be found in the header files.

Authors

See also the list of contributors who participated in this project.

License

No license

TODOs

  • Implement a test class which verifies calculations of GPU-Kernels
  • Optimize CudaBase::mapColors, CudaBase::fftshift, CudaBase::absolute and CudaBase::window
  • Add optimized matrix-vector and matrix-matrix multiplication
  • Commenting all classes and functions