Mathematical Model and Synthetic Data Generation for Infra-Red Sensors

Area of Science:

• Sensor Technology
• Data Science
• Optical Engineering

Background:

• Improving infrared (IR) sensor capabilities requires efficient data pre-processing algorithms.
• Uncooled IR sensors face challenges in data handling due to complex physical characteristics.

Purpose of the Study:

• To develop a mathematical model for uncooled IR sensor data generation.
• To create a synthetic data framework for designing data pre-processing algorithms.

Main Methods:

• Mathematical modeling of focal plane array, bolometer readout, optics, and environment.
• Development of a synthetic data generation framework for uncooled IR sensors.
• Simulation of sensor configurations, pixel defects, non-uniformity, and noise parameters.

Main Results:

• A validated mathematical model capable of generating realistic synthetic data for uncooled IR sensors.
• A flexible framework supporting diverse sensor configurations and defect scenarios.
• Demonstrated utility in designing and testing data pre-processing algorithms.

Conclusions:

• The developed model and framework are crucial for advancing uncooled IR sensor data pre-processing.
• This approach facilitates the creation of more robust and efficient IR sensing systems.
• Enables tailored algorithm development for specific sensor characteristics and environmental conditions.