Dual-Antenna GNSS/MEMS INS Tightly Coupled Algorithm for Agricultural Machinery Based on Adaptive Federated Filtering

Modern agricultural machinery relies on high-accuracy navigation systems; however, the common loosely coupled (LC) solution of dual-antenna global navigation satellite system (GNSS) and micro-electromechanical system inertial navigation system (MEMS INS) often fails to meet accuracy requirements in complex environments. Theoretically, the tightly coupled (TC) solution of the dual-antenna baseline constraint and MEMS INS offers better attitude accuracy. However, its state space is incomplete, comprising only attitude, gyro biases, and ambiguity. Moreover, previous studies have not conducted a state observability analysis on this model, which is essential for understanding its state estimation capabilities. Therefore, we derived the TC model of dual-antenna baseline constraint and MEMS INS within a complete state space and performed an observability analysis. Based on these results and considering computational efficiency, we integrated this model into the GNSS/MEMS INS TC model using federated filtering. To further improve the algorithm's accuracy in complex agricultural environments, an adaptive robust positioning algorithm is proposed based on turning state detection. The proposed algorithm was validated through three sets of experiments, demonstrating position accuracy within 2 cm in both open and slightly occluded environments, with heading accuracy within 0.6 degrees, and maintaining optimal accuracy even in severely occluded environments.