classical-doa 1.3.2

Classical DOA estimation algorithms implemented in Python

Moved to doa_py

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 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(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 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
• Broadband
  ChirpSignal MultiCarrierSignal

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.