Integration of the Global Navigation Satellite System (GNSS), with Inertial Measurement Unit (IMU) sensors to improve navigation performance, is widely used in many land-based applications. However, further application, especially in urban areas, is limited by the quality (due mainly to multipath effects) and availability of GNSS measurements, with a significant impact on performance, especially from low grade integration. To maximize the potential of GNSS measurements, this paper proposes a dual w-test-based quality control algorithm for integrated IMU/GNSS navigation in urban areas. Quality control is achieved through fault detection and exclusion (FDE) with the capability to detect simultaneous multiple faults in measurements from different satellites. The remaining fault-free GNSS measurements are fused with IMU sensor measurements to obtain the final improved state solution. The effectiveness of the algorithm is validated in a deep urban field test. Compared to the cases without fault exclusion, the results show improvements of about 24% and 30% in horizontal and vertical positioning components, respectively.

In response to the demand for the evaluation of the actual navigation performance (ANP) of rotorcraft logistics uncrewed aerial vehicle (UAV) navigation systems in urban scenarios, this paper proposes a method for evaluating the ANP of rotorcraft logistics UAVs based on the Improved Sparrow Search Algorithm (ISSA). Taking ANP as the optimization objective, an optimization model for the ANP of rotorcraft logistics UAVs is constructed. Based on the probability of the UAV’s actual position falling within the error circle, an initial population strategy based on probabilistic decision-making is designed, and an adaptive dynamic step size strategy and dynamic compression search strategy are proposed to improve the traditional Sparrow Search Algorithm (SSA), enhancing the algorithm’s ability of optimization and to escape local extremum. The contribution of this paper mainly includes constructing the ANP optimization model and designing the ISSA method. Experimental results show that the proposed method can effectively estimate ANP, achieve onboard performance monitoring and warning, and ensure the required navigation performance (RNP) and flight safety of UAVs.

The spatial and temporal conflicts within terminal maneuvering areas, particularly in multi-airport systems, are growing increasingly complex. Traditional independent processing methods face inherent limitations when dealing with multi-source uncertainties, dynamic weather conditions, and high-density operations. This paper proposes MSTAGNN-MARL that systematically integrates the resolution of spatial conflicts and temporal scheduling issues. This framework is based on four crucial innovations: First, a strategic-tactical-execution hierarchical architecture is constructed that integrates multi-criteria decision optimization with graph neural network-based multi-agent reinforcement learning. Second, an uncertainty perception mechanism is designed that explicitly encodes conflict features as dynamic edge attributes in social graphs, incorporating a real-time dynamic weather model and a Gaussian noise-based perception uncertainty model. Third, develop a compliance automated system for behavior cloning that learns the decision preferences of controllers to achieve human–machine collaboration and provide transparent visualization. Fourth, a robustness assurance mechanism for abnormal scenarios is constructed, employing behavior tree-driven emergency strategies to handle unexpected situations. Experiments demonstrate that the proposed method achieves an 89.3% conflict resolution rate, reduces average delays by 6 min compared to existing methods, and exhibits robust performance under varying traffic densities and dynamic weather conditions. Ablation experiments validate the effectiveness of the four innovations. This framework provides a new research paradigm for scheduling and decision-making in Intelligent Transportation Systems (ITS).