classical-doa 1.0.1

Classical DOA estimation algorithms implemented in Python

Classical DOA

Classical DOA Estimation algorithms implemented in Python.

Getting Started

Installation

pip install classical_doa

or install from source

git clone https://github.com/zhiim/classical_doa.git
cd classical_doa
pip install .

Usage

A sample example of DOA estimation using MUSIC algorithm.

import numpy as np

from classical_doa import arrays, signals
from classical_doa.algorithm.music import music
from classical_doa.plot import plot_spatial_specturm

# 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_position=np.arange(0, 4, 0.5), signal_fre=3e8,
                 angle_grids=angle_grids, unit="deg")

# Plot the spatial spectrum
plot_spatial_specturm(spectrum=spectrum, ground_truth=np.array([0, 30]),
                      angle_grids=angle_grids)

You will a get a figure like this:

Check 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
  complex stochastic signal
• Broadband
  chirp signal

Algorithms

• ULA
  • MUSIC
  • ESPRIT
  • Root-MUSIC
  • OMP
  • l1-SVD
• URA
  • URA-MUSIC
  • URA-ESPRIT
• UCA
  • UCA-RB-MUSIC
  • UCA-ESPRIT
• Broadband
  • ISSM
  • CSSM
  • TOPS

License

This project is licensed under the MIT License - see the LICENSE file for details.