FMCW_Radar_Target_Simulator

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FMCW_Radar_Target_Simulator

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This Matlab code simulates FMCW radar baseband signals for measurements in urban traffic scenarios with multiple targets (bicycles, pedestrians and cars). The output can be used as labelled, COCO formated input for deep learning radar target detection.

FMCW_Radar_Target_Simulator 최신버젼 다운로드

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# FMCW Radar Target Simulator

This repository is developed to simulate automotive FMCW radar signals urban traffic scenarios with pedestrians, bicyclists and cars. Multiple targets are placed and moved arbitrarily in a simulated noisy radar environment. A baseband signal is calculated for each target reflector point in the model and the signals are added at the output to simulate the radar's baseband signal by superposition. Random static clutter and noise are added for realistic results. From the baseband signal, the 3D radar cube can be displayed. With its corresponding labels, the data can be utilized for detection algorithm validation or the training of deep learning radar detection algorithms.
For more information about the developed models, read my [IEEE paper](https://ieeexplore.ieee.org/document/9827284).

![MultiTargetRDmap](https://user-images.githubusercontent.com/61163489/185382976-a1b6dd88-14c2-49d6-98fa-0e98971f600d.PNG)

## Getting Started

Download this repository to your local machine and run MATLAB in the unzipped folder. Make sure that you have installed a MATLAB version from 2020 or later. If you want to run the software on GNU Octave, navigate to the [Octave](https://github.com/thomaswengerter/FMCW_Radar_Target_Simulator/tree/Octave) branch. The [Phased Array Toolbox](https://de.mathworks.com/products/phased-array.html) is required for this simulation. Download it from MATLAB's built-in toolbox archive.

The main funtion which needs to be run to initiate the simulation is [SimulateTargetList.m](SimulateTargetList.m) to generate a sequence of multi-target scenarios. Alternatively, [TargetSimulation.m](TargetSimulation.m) generates a single measurement of one target. Simulation options are: 
* No targets (outputs noisy spectrum with static targets)
* Pedestrian with MATLAB's [backscatterPedestrian](https://de.mathworks.com/help/phased/ref/backscatterpedestrian.html)
* Manned bicycle from class [Bicyclist.m](Bicyclist.m)
* Car from class [Car.m](Car.m)
* Single point reflectors from function [simulateSignal.m](simulateSignal.m) (only in [TargetSimulation.m](TargetSimulation.m))

Initialize the desired target list in the main function and use the random initializations for positions, velocities and specific target properties or set them manually. If you desire to simulate targets for your own radar device, change the radar properties in the class [FMCWradar.m](FMCWradar.m). If targets need to follow certain paths, initialize your own trajectories as in [TrajectoryPlanner.m](TrajectoryPlanner.m).

### Prerequisites

* MATLAB version >= 2020
* MATLAB's phased array toolbox

### Deployment

Let's illustrate the functionality of the programm in a small example. After the repository download the file and unzip it to a local folder.

Open [FMCWradar.m](FMCWradar.m) to check the radar settings. All parameters like operating frequency, chirp shape and antenna characteristics are initialized in the properties. If you desire to print information about the added gaussian noise level and SNRs, you can set the variable **printNoiseCharacteristics** to *true*.

- *Single Target Measurement*:
    Open [TargetSimulation.m](TargetSimulation.m). All available target types are listed at the start of the function. Change the number of desired targets/measurements to create a training dataset. The generator initializes random positions and velocities for each target within the radars field of view. However, specific positions can simply be set for each target type to generate special szenarios of interest. If you want to output a plot of the RDmap, add the index of one or more RX antennas in the variable **plotAntennas**.
	The simulation saves the generated 3D range-doppler-azimuth radar cubes in*SimulationData/TARGETNAME/TARGETNAME_X* and the labels in *SimulationData/Szenario123/Szenario123_Label_X.mat*, where *X* denotes the measurement cycle. 
	Target labels have the following format:
	**[Range, velocity, azimuth, RadarVelocity, xPosition, yPosition, width, height, heading]**

- *Multi-Target Szenario*:
    Open [SimulateTargetList.m](SimulateTargetList.m). All available target types are listed at the first **for** loop. Change the **rand** sampling parameters to set the probability for the individual targets occuring randomly in the measurements. The generator initializes random positions, headings and velocities for each target within the radars field of view. After every measurement, the targets are moved one step along the generated trajectory. To setup special szenarios of interest, the trajectory has to be initialized in the [TrajectoryPlanner.m](TrajectoryPlanner.m). If you want to output a plot of the RDmap, add the index of one or more RX antennas in the variable **plotAntennas**.
	The simulation saves the generated 3D range-doppler-azimuth radar cubes in*SimulationData/Szenario123/Szenario123_X.mat* and the labels in *SimulationData/Szenario123/Szenario123_Label_X.mat*, where *X* denotes the measurement cycle. 	
	Target labels have the following format:
	**[Range, velocity, azimuth, RadarVelocity, xPosition, yPosition, width, height, heading, obstruction]**

The origin of the coordinate system is always with the radar device in (0,0,0), looking in direction of the x-axis. The simulated radar cubes are saved in decibel.

### Conversion to COCO annotation format
To use the simulated data for training or testing of neural networks, the conversion to the COCO annotation format might be helpful. The python function [JSONCoco.py](JSONCoco.py) generates the json annotation format from simulated scenarios. To match the annotation format of images, the azimuth dimension is reduced to the maximum over all azimuth channels. Thus, the COCO annotations can be used to train a radar target detection network on range-Doppler maps.

Before running the code, set the correct path to a *./SimulationData* folder and define if the dataset is saved as the training set with name **trainvalname='train2017'** or validation set with **trainvalname='val2017'**.

To feed sequences of 3 radar measurements as one input image to the network, [JSONCoco_3SeqRGB.py](JSONCoco_3SeqRGB.py) overlays 3 measurements in the RGB channels and every 3rd label to the json file.

## Hints

For the final training data generation, a GPU can be utilized to accelerate the simulation process. In [SimulateTargetList.m](SimulateTargetList.m), GPU acceleration is already integrated with **parfor** in line 234. To use GPU accelerated processing in [TargetSimulation.m](TargetSimulation.m), change the expressions **for** *target = 1:Pedestrians* ... **end** to **parfor** *target = 1:Pedestrians* ... **end**.

## Authors

* **Thomas Wengerter** - *Initial work* - [thomaswengerter](https://github.com/thomaswengerter)

See also the list of [contributors](https://github.com/thomaswengerter/FMCW_Radar_Target_Simulator/contributors) who participated in this project.

## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details

## Acknowledgments

* MATLAB's work on the phased array toolbox
* Support from the High Frequency Department at the Technical University of Munich

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