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doatools.py

(★ 191)

A simple library for theoretical research on direction-of-arrival (DOA) estimation in array signal processing.

파일 탐색기

최종 버전 다운로드 (.zip)

- - __init__.py
  - beamforming.py
  - coarray.py
  - core.py
  - esprit.py
  - grid.py
  - min_norm.py
  - ml.py
  - music.py
  - preprocessing.py
  - source_number.py
  - sparse.py
- - __init__.py
  - array_elements.py
  - arrays.py
  - coarray.py
  - perturbations.py
  - signals.py
  - snapshots.py
  - sources.py
- - __init__.py
  - l1lsq.py
- - __init__.py
  - crb.py
  - mse.py
  - utils.py
- - __init__.py
  - plot_array.py
  - plot_spectrum.py
- - __init__.py
  - test_array_designs.py
  - test_beamforming.py
  - test_coarray.py
  - test_coarray_music.py
  - test_crb.py
  - test_esprit.py
  - test_grids.py
  - test_mse.py
  - test_music.py
  - test_preprocessing.py
  - test_signals.py
  - test_source_number.py
  - test_source_placement.py
  - test_utils.py
- - __init__.py
  - conversion.py
  - math.py
- __init__.py
- _version.py
- - conf.py
  - index.rst
- make.bat
- Makefile
- - coarrays_music_and_crb.ipynb
- ex0_design_and_visualize_arrays.ipynb
- ex1_doa_with_ula.ipynb
- ex2_coprime_array.ipynb
- ex3_ula_performance.ipynb
- ex4_ula_resolution.ipynb
- ex5_spatial_smoothing.ipynb
.gitignore
LICENSE.md
README.md
setup.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/morriswmz/doatools.py

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd doatools.py

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install .

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

jupyter notebook

브라우저에서 노트북(.ipynb) 파일들을 열람하고 실행할 수 있는 Jupyter 화면을 켭니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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# doatools.py

**doatools.py** is the Python version of my [doa-tools](https://github.com/morriswmz/doa-tools) in MATLAB. It provides basic tools for theoretical research on direction-of-arrival (DOA) estimation, including basic array designs, various DOA estimators, plus tools to compute performance bounds. The MATLAB version served as a small toolbox for [my research](http://research.wmz.ninja/research.html) related to array signal processing. I made this Python version because I will no longer have access to MATLAB.

I made some notebooks that produce figures similar to those in my papers (may not be exactly the same due to the randomness of Monte Carlo simulations). You can browse them [here](examples/paper). These examples are not as complete as those in the MATLAB version.

You can also view the full documentation [here](https://morriswmz.github.io/doatools.py/).

## Features

* Several array design and difference coarray related functions.
* Commonly used DOA estimators including MVDR beamformer, MUSIC, root-MUSIC, ESPRIT.
* Maximum-likelihood estimators including AML, CML, and WSF.
* Sparsity-based DOA estimator.
* Functions to compute the [Cramér-Rao bounds](https://en.wikipedia.org/wiki/Cram%C3%A9r%E2%80%93Rao_bound).
* Functions to compute the asymptotic covariance matrix of the estimation errors of MUSIC estimators (including difference coarray based).
* Functions to visualize the estimation results.

>**Note:** **doatools.py** is designed to facilitate my theoretical research on array signal processing. It is not designed for real-world applications. Nevertheless, the implementations of various DOA estimators in this repository provide good references on understanding these estimation algorithms.

## Differences from the MATLAB version

The Python version retains most of the functionalities of the MATLAB version, while adding some new ones. For instance, the Python version supports 2D (azimuth and elevation) DOA estimation and includes more ML-based estimators.

The Python version is also more modular, providing more flexibility over the MATLAB version. Many spectrum-based estimators (e.g., MUSIC) are now reusable, and work for both 1D and 2D sources.

The Python version is better documented.

## Requirements

**doatools.py** requires [NumPy](https://github.com/numpy/numpy), [SciPy](https://github.com/scipy/scipy) and [Matplotlib](https://github.com/matplotlib/matplotlib). It also requires [CVXPY](https://github.com/cvxgrp/cvxpy) to solve sparse recovery problems. To run the examples, you also need to install [tqdm](https://github.com/tqdm/tqdm).

## Examples

You can view the examples [here](examples/).

## License

The source code is released under the [MIT](LICENSE.md) license.

## Citation

If you find my code helpful. You are welcomed to cite my papers [here](http://research.wmz.ninja/research.html).