/Deep_RSA_DOA

Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse Arrays

Primary LanguagePythonMIT LicenseMIT

Deep-Learning-Enabled-Robust-DOA-Estimation-with-Single-Snapshot-Sparse-Arrays

This is the code for paper "Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse Arrays"

Simulated dataset generation for trianing and validation

python scr/dataset_gen.py --output_dir './' --num_samples_val 1024 --num_samples_train 100000 --N 10 --max_targets 3

Network architectures

Training

Without sparse augmentation model:

python train.py --data_path './data' --checkpoint_path './checkpoint' --number_elements 10 --output_size 61 --sparsity 0.3 --use_sparse False --learning_rate 0.0001 --batch_size 1024 --epochs 300

With sparse augmentation model:

python train.py --data_path './data' --checkpoint_path './checkpoint' --number_elements 10 --output_size 61 --sparsity 0.3 --use_sparse True --learning_rate 0.0001 --batch_size 1024 --epochs 300

Evaluation

The evaluation of the model can be conducted immediately using weights that we have trained and provided. These weights are available in the 'checkpoint' directory. Before proceeding with the following steps, ensure you are in the correct directory where the scripts or applications are located.

cd  scr

Single target accuracy

python run_eval.py --num_simulations 1000 --num_antennas 10 --evaluation_mode 'accuracy1'

Expected outputs: ULA(left), SLA(right)

Two target accuracy

python run_eval.py --num_simulations 1000 --num_antennas 10 --evaluation_mode 'accuracy2'

Expected outputs: ULA(left), SLA(right)

Seperatebility

python run_eval.py --num_simulations 1000 --num_antennas 10 --evaluation_mode 'separate'

Expected outputs: ULA(left), SLA(right)

Complexity

python run_eval.py --num_simulations 1000 --num_antennas 10 --evaluation_mode 'complexity'

Expected outputs:

Total trainable parameters in MLP model: 2848829

Total trainable parameters in Ours model: 4106301

Results examples

With simulated data

python run_eval.py --evaluation_mode 'examples'

Expected outputs:

With real world data

python run_eval.py --evaluation_mode 'examples' --real True

Expected outputs:

## Real World dataset please refer README in the folder 'real_World_DOA_dataset'

Enviroment

The Conda environment required for this project is specified in the file 'conda_env.txt'. This file contains a list of all the necessary Python packages and their versions to ensure compatibility and reproducibility of the project's code.

If you find this project helpful for your research, please consider citing:

@article{zheng2024antenna,
  title={Antenna Failure Resilience: Deep Learning-Enabled Robust DOA Estimation with Single Snapshot Sparse Arrays},
  author={Zheng, Ruxin and Sun, Shunqiao and Liu, Hongshan and Chen, Honglei and Soltanalian, Mojtaba and Li, Jian},
  journal={arXiv preprint arXiv:2405.02788},
  year={2024}
}