Dual-event-triggering ANFIS-based unscented Kalman filter for cluster cooperative navigation with measurement anomalies

Cooperative navigation is the prerequisite to realize intelligent operation for unmanned system clusters. However, in harsh measurement circumstances, it is challenging to acquire precise and reliable cooperative navigation information for unmanned system clusters at low cost. On the basis of the leader-follower manner using a low-cost Micro Inertial Measurement Unit (MIMU) and relative navigation sensors, this paper proposes a novel Unscented Kalman Filter (UKF) based on dual-event-triggering Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance the filtering robustness against anomalous measurements. This method constructs a cooperative navigation model using a low-cost MIMU and relative navigation sensors (angle, velocity and distance) as measurement means. Subsequently, a dual-event-triggering ANFIS is embedded in the UKF filtering procedure to improve the filtering adaptability to mitigate the influence of anomalous measurements, in which a dual-event-triggering mechanism is established to reduce the unnecessary computational load for the ANFIS. Besides, a training tactic based on online data is designed for the ANFIS to use few-shot learning to better adapt to unknown environments. Simulation results on Unmanned Aerial Vehicle (UAV) cluster indicate that the proposed method can achieve a stronger adaptability with a low computational cost, leading to enhanced robustness against anomalous measurements for cluster cooperative navigation.