UAV Altitude Estimation Using MATLAB Kalman Filter Program

This document describes UAV altitude measurement using MATLAB-based Kalman filter programming and data fusion techniques. These methodologies employ multiple sensors to collect data and intelligently combine them to achieve more accurate results. The Kalman filter algorithm, implemented through MATLAB programming, estimates system states by optimally weighting measurement and prediction errors using recursive mathematical operations. Key implementation aspects include state-space modeling, covariance matrix calculations, and predictor-corrector mechanisms that continuously refine altitude estimates. Data fusion techniques combine information from diverse sources such as barometers, GPS, and IMUs to generate comprehensive and precise altitude information while effectively reducing sensor noise and measurement errors. Through MATLAB implementation, programmers can utilize built-in functions like kalman or custom state estimation code to handle sensor fusion, with typical implementation involving process noise (Q) and measurement noise (R) covariance tuning. The integrated approach ensures highly accurate and reliable UAV altitude measurements suitable for autonomous navigation applications.