Kalman Filter Algorithm with Zero-Velocity Update for Indoor Navigation

This paper presents a Kalman filter algorithm incorporating zero-velocity update (ZUPT) correction, designed specifically for personal indoor navigation applications. The algorithm processes input data formats from inertial measurement units (IMUs) and implements zero-velocity detection mechanisms to significantly enhance indoor navigation accuracy. Key implementation aspects include state vector initialization, covariance matrix configuration, and real-time zero-velocity detection using threshold-based or machine learning approaches. Through this algorithm, high-precision positioning and navigation can be achieved in indoor environments, providing users with enhanced experience and services. The implementation typically involves sensor data acquisition, preprocessing filters, and adaptive Kalman gain calculation to handle dynamic motion patterns. It is important to note that the algorithm's performance may vary across different environments, requiring parameter tuning and optimization based on specific deployment conditions. Implementation considerations include adaptive threshold adjustment for zero-velocity detection, noise covariance adaptation, and motion state classification to achieve optimal performance in diverse indoor scenarios.