This study introduces a hybrid sensor fusion approach that integrates Schreiber’s nonlinear
filter with traditional filtering methods to enhance the performance of IMU-based systems
in autonomous vehicles. As autonomous vehicles grow more dependent on Inertial Measurement Unit (IMU) data for real-time stability and control, the need for resilient and
accurate sensor fusion becomes critical. This research addresses that need by introducing a
method capable of maintaining robustness under highly dynamic and uncertain conditions.
Accelerometer and gyroscope data from an IMU are first fused using a complementary
filter. The fused signals are then refined by phase-space reconstruction and local manifold
projection, improving noise resilience and maintaining system dynamics. Two datasets
are used to assess the methodology: one was collected indoors with a smartphone, and
another was captured outdoors using a Bosch sensor in various environmental settings. The
proposed method demonstrates superior noise reduction, greater resistance to outliers, and
improved signal consistency compared to conventional complementary and Kalman filters.
The findings demonstrate how chaos-based nonlinear filtering may improve the reliability
of sensor fusion on a variety of sensing platforms in highly dynamic environments. Given
the importance of IMU data for maintaining vehicle stability, this study seeks to support
the development of more stable autonomous transportation systems.