Allan Variance Analysis with MATLAB Implementation

We present a MATLAB-based Allan variance analysis program that includes sample inertial navigation data for immediate implementation. This program utilizes optimized algorithms for calculating overlapping Allan deviation, featuring functions for data preprocessing, variance computation, and graphical result representation. The core implementation includes time-series segmentation, cluster formation, and statistical averaging techniques essential for accurate noise characterization.

Before utilizing this program, understanding the fundamental principles and applications of Allan variance analysis is recommended. Allan variance serves as a critical methodology for inertial navigation error analysis, effectively evaluating system precision and stability through distinct noise identification (angle random walk, bias instability, rate random walk). The algorithm processes inertial sensor data by computing root Allan variance across multiple cluster times, revealing various noise components that impact navigation performance.

Key program functions include data normalization routines, automated tau-point generation, and log-log plotting capabilities for noise parameter extraction. When applying this tool, ensure data integrity and measurement reliability through proper sensor calibration and sampling frequency validation. The program incorporates error-checking mechanisms for data gap handling and outlier detection to maintain analytical credibility.