vineeths96 / Interpolation-of-Faulty-Sensor

In this repository, we deal with developing a Wiener (LMMSE) filter and Kalman filter for interpolation of a faulty sensor in a uniform sensor array and implementing and evaluating the performance of the estimators that we have derived.

Interpolation of a faulty sensor

Interpolation of a faulty sensor data sample
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tags : interpolation, wiener, kalman, lmmse, estimation, missing data

About The Project

This project deals with developing a Wiener (LMMSE) filter and Kalman filter for interpolation of a faulty sensor in a uniform sensor array and implementing and evaluating the performance of the estimators that we have derived. We derive and implement the Wiener filter interpolator (LMMSE) using Wiener-Hopf equations and Kalman filter interpolator, and evaluate their performance.

Built With

This project was built with

• python v3.7
• The list of libraries used for developing this project is available at requirements.txt.

Getting Started

Clone the repository into a local machine using

git clone https://github.com/vineeths96/Interpolation-of-faulty-sensor

Prerequisites

Please install required libraries by running the following command (preferably within a virtual environment).

pip install -r requirements.txt

Instructions to run

The wiener_plots.py acts as an interface to the Wiener filter implementations in wiener_interpolator.py. The kalman_plots.py acts as an interface to the Kalman filter implementations in kalman_filter.py. The comparision.py compares the performance of Kalman filter predictor to that of a causal Wiener predictor.

The respective program can be executed by

python <file_name>.py

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Vineeth S - [e-mail]

Project Link: https://github.com/vineeths96/Interpolation-of-faulty-sensor