<div align="left">
<img src="pics/doa_py.svg" alt="DOA_Py logo" width="20%">
</div>
# DOA_Py
DOA Estimation algorithms implemented in Python. It can be used for ULA, UCA and broadband/wideband DOA estimation.
## Getting Started
### Installation
```bash
pip install doa_py
```
or install from source
```bash
git clone https://github.com/zhiim/doa_py.git
cd doa_py
pip install .
```
### Usage
A sample example of DOA estimation using MUSIC algorithm.
```python
import numpy as np
from doa_py import arrays, signals
from doa_py.algorithm import music
from doa_py.plot import plot_spatial_spectrum
# Create a 8-element ULA with 0.5m spacing
ula = arrays.UniformLinearArray(m=8, dd=0.5)
# Create a complex stochastic signal
source = signals.ComplexStochasticSignal(fc=3e8)
# Simulate the received data
received_data = ula.received_signal(
signal=source, snr=0, nsamples=1000, angle_incidence=np.array([0, 30]), unit="deg"
)
# Calculate the MUSIC spectrum
angle_grids = np.arange(-90, 90, 1)
spectrum = music(
received_data=received_data,
num_signal=2,
array=ula,
signal_fre=3e8,
angle_grids=angle_grids,
unit="deg",
)
# Plot the spatial spectrum
plot_spatial_spectrum(
spectrum=spectrum,
ground_truth=np.array([0, 30]),
angle_grids=angle_grids,
num_signal=2,
)
```
You will a get a figure like this:
Check [examples](https://github.com/zhiim/doa_py/tree/master/examples) for more examples.
## What's implemented
### Array Structures
- Uniform Linear Array (ULA)
- Uniform Rectangular Array (URA, to be implemented)
- Uniform Circular Array (UCA)
### Signal Models
- **Narrowband**
- _ComplexStochasticSignal_: The amplitude of signals at each sampling point is a complex random variable.
- _RandomFreqSignal_: Signals transmitted by different sources have different intermediate frequencies (IF).
- **Broadband**
- _ChirpSignal_: Chirp signals with different chirp bandwidths within the sampling period.
- _MultiFreqSignal_: Broadband signals formed by the superposition of multiple single-frequency signals within a certain frequency band.
- _MixedSignal_: Narrorband and broadband mixed signal
### Algorithms
- DOA estimation for ULA
- [x] MUSIC
- [x] ESPRIT
- [x] Root-MUSIC
- [x] OMP
- [x] l1-SVD
- DOA estimation for URA
- [ ] URA-MUSIC
- [ ] URA-ESPRIT
- DOA estimation for UCA
- [x] UCA-RB-MUSIC
- [x] UCA-ESPRIT
- Broadband/Wideband DOA estimation
- [x] ISSM
- [x] CSSM
- [x] TOPS
## License
This project is licensed under the [MIT](LICENSE) License - see the LICENSE file for details.
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"description": "<div align=\"left\">\n <img src=\"pics/doa_py.svg\" alt=\"DOA_Py logo\" width=\"20%\">\n</div>\n\n# DOA_Py\n\nDOA Estimation algorithms implemented in Python. It can be used for ULA, UCA and broadband/wideband DOA estimation.\n\n## Getting Started\n\n### Installation\n\n```bash\npip install doa_py\n```\n\nor install from source\n\n```bash\ngit clone https://github.com/zhiim/doa_py.git\ncd doa_py\npip install .\n```\n\n### Usage\n\nA sample example of DOA estimation using MUSIC algorithm.\n\n```python\nimport numpy as np\n\nfrom doa_py import arrays, signals\nfrom doa_py.algorithm import music\nfrom doa_py.plot import plot_spatial_spectrum\n\n# Create a 8-element ULA with 0.5m spacing\nula = arrays.UniformLinearArray(m=8, dd=0.5)\n# Create a complex stochastic signal\nsource = signals.ComplexStochasticSignal(fc=3e8)\n\n# Simulate the received data\nreceived_data = ula.received_signal(\n signal=source, snr=0, nsamples=1000, angle_incidence=np.array([0, 30]), unit=\"deg\"\n)\n\n# Calculate the MUSIC spectrum\nangle_grids = np.arange(-90, 90, 1)\nspectrum = music(\n received_data=received_data,\n num_signal=2,\n array=ula,\n signal_fre=3e8,\n angle_grids=angle_grids,\n unit=\"deg\",\n)\n\n# Plot the spatial spectrum\nplot_spatial_spectrum(\n spectrum=spectrum,\n ground_truth=np.array([0, 30]),\n angle_grids=angle_grids,\n num_signal=2,\n)\n```\n\nYou will a get a figure like this:\n\n\nCheck [examples](https://github.com/zhiim/doa_py/tree/master/examples) for more examples.\n\n## What's implemented\n\n### Array Structures\n\n- Uniform Linear Array (ULA)\n- Uniform Rectangular Array (URA, to be implemented)\n- Uniform Circular Array (UCA)\n\n### Signal Models\n\n- **Narrowband**\n - _ComplexStochasticSignal_: The amplitude of signals at each sampling point is a complex random variable.\n - _RandomFreqSignal_: Signals transmitted by different sources have different intermediate frequencies (IF).\n- **Broadband**\n - _ChirpSignal_: Chirp signals with different chirp bandwidths within the sampling period.\n - _MultiFreqSignal_: Broadband signals formed by the superposition of multiple single-frequency signals within a certain frequency band.\n - _MixedSignal_: Narrorband and broadband mixed signal\n\n### Algorithms\n\n- DOA estimation for ULA\n - [x] MUSIC\n - [x] ESPRIT\n - [x] Root-MUSIC\n - [x] OMP\n - [x] l1-SVD\n- DOA estimation for URA\n - [ ] URA-MUSIC\n - [ ] URA-ESPRIT\n- DOA estimation for UCA\n - [x] UCA-RB-MUSIC\n - [x] UCA-ESPRIT\n- Broadband/Wideband DOA estimation\n - [x] ISSM\n - [x] CSSM\n - [x] TOPS\n\n## License\n\nThis project is licensed under the [MIT](LICENSE) License - see the LICENSE file for details.\n",
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