Integrated Navigation System: Strapdown Inertial Navigation and GPS with Kalman Filter Data Fusion

This article explores the implementation of an integrated navigation system combining strapdown inertial navigation and GPS technology to achieve enhanced positioning accuracy. The core implementation involves using a Kalman filter algorithm to fuse data from these two distinct sensor systems, resulting in more precise navigation outputs. From a code perspective, the Kalman filter implementation typically includes state prediction and measurement update stages, where inertial measurements provide high-frequency short-term accuracy while GPS data offers long-term stability. The algorithm effectively handles sensor noise characteristics through covariance matrices and gain calculations. Additionally, we'll examine practical application domains for this technology and discuss optimization strategies for maintaining high-precision positioning across diverse environmental conditions. Key implementation considerations include sensor calibration, filter tuning parameters, and handling GPS signal outages through pure inertial navigation during temporary signal loss.