Attitude and Heading Estimation in Symmetrical Inertial Arrays

Attitude and heading reference systems (AHRSs) play a central role in autonomous navigation systems on land, air, and maritime platforms. AHRS utilizes inertial sensor measurements to estimate platform orientation. In recent years, there has been increasing interest in multiple inertial measurement units (MIMUs) arrays to improve navigation accuracy and robustness. A particularly challenging MIMU implementation is the gyro-free (GF) configuration, in which angular velocity is derived solely from accelerometer measurements. While the GF configurations have multiple benefits, including outlier detection and in angular acceleration measurements, their main drawbacks are inherent instability and an increased divergence rate. To address these shortcomings, we introduce a novel symmetrical MIMU (SMIMU) formulation, in which the IMUs are arranged in symmetric diagonal pairs to decouple linear and rotational acceleration components. To this end, we derive the theoretical foundations for the SMIMU formulation of the GF equations, develop a nonlinear least-squares estimation process, and integrate statistical hypothesis testing into an AHRS error-state extended Kalman filter (EKF). We validate our approach using real-world datasets containing 85 min of navigation data recorded on both airborne and land platforms. Our results demonstrated a 30% average reduction in attitude estimation errors, an average of 96.7% rotation detection accuracy, and significantly improved stability compared to a standard GF implementation. These results enable reliable GF navigation in applications where the integration of gyroscopes is unfeasible, unreliable, or energy-constrained. Common examples include miniature platforms, computational-constraint platforms, and long-endurance marine platforms.