Classical DOA Estimation algorithms implemented in Python.
pip install classical_doaor install from source
git clone https://github.com/zhiim/classical_doa.git
cd classical_doa
pip install .A sample example of DOA estimation using MUSIC algorithm.
import numpy as np
from classical_doa import arrays, signals
from classical_doa.algorithm import music
from classical_doa.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(nsamples=1000, fre=3e8, fs=1e9)
# Simulate the received data
received_data = ula.received_signal(
signal=source, snr=0, 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 for more examples.
- Uniform Linear Array (ULA)
- Uniform Rectangular Array (URA, to be implemented)
- Uniform Circular Array (UCA)
- Narrowband
complex stochastic signal - Broadband
chirp signal broadband signal consisting of multiple narrowband signals
- ULA
- MUSIC
- ESPRIT
- Root-MUSIC
- OMP
- l1-SVD
- URA
- URA-MUSIC
- URA-ESPRIT
- UCA
- UCA-RB-MUSIC
- UCA-ESPRIT
- Broadband
- ISSM
- CSSM
- TOPS
This project is licensed under the MIT License - see the LICENSE file for details.