Enhancing Altitude Data Accuracy in Small Aircraft Systems Using Standard Kalman Filters

Accurate altitude estimation is a critical aspect of small aircraft system operational efficiency and design. This research studies pressure sensors and methods that could improve the accuracy of altitude data for small aircraft applications. Digital pressure sensors BMP180 and BMP280 have been used for data fusion. Altitude data derived from disparate sources have less uncertainty than if they were used individually. Despite the benefits of sensor fusion of the BMP180 and BMP280 sensors themselves, their sensitivity to noise presents a significant challenge requiring noise reduction strategies, such as filtering techniques. Standard Kalman filter (SKF) has been used for enhancing altitude data accuracy due to its robust real-time processing and ability to control linear systems noise. SKF reduces noise, increasing height measurement accuracy. The proposed methodology was substantiated through the integration of sensor fusion, on an Arduino Uno platform. Also, we consider application of these pressure sensors with SKF data processing in the altitude control systems of airplanes.