Journal of Aerospace Information Systems最新文献

Securing the borders of a protected region using sensor network deployment is termed “barrier coverage.” Unmanned aerial vehicles (UAVs) with cameras pointed downward can serve as mobile sensors to achieve barrier coverage of a protected region. The resolution of the camera, in addition to the extent of coverage, is a crucial parameter used to evaluate the quality of barrier coverage of a region. This paper presents a cost function that measures the resolution of a barrier coverage network, which can be used to improve the quality of an already established barrier-covered network. An optimization problem is proposed to find the barrier coverage while adhering to an overlapping constraint for UAVs that are placed arbitrarily in the belt. The approach is also demonstrated to be applicable for borders of any shape by utilizing multiple rectangular belts in combination. Furthermore, a fault tolerance model is proposed to ensure continuous barrier coverage even in the presence of faulty UAVs. This model utilizes nearby functional UAVs to compensate for any gaps and preserve the overlap constraint. Specifically, the model identifies neighboring functional UAVs for each faulty UAV and uses them to maintain barrier coverage.

This paper presents a real-time trajectory planning framework for urban air mobility (UAM) that is both safe and scalable. The proposed framework employs a decentralized, free-flight concept of operation in which each aircraft independently performs separation assurance and conflict resolution, generating safe trajectories by accounting for the future states of nearby aircraft. The framework consists of two main components: a data-driven reachability analysis tool and an efficient Markov-decision-process-based decision maker. The reachability analysis overapproximates the reachable set of each aircraft through a discrepancy function learned online from simulated trajectories. The decision maker, on the other hand, uses a 6-degree-of-freedom guidance model of fixed-wing aircraft to ensure collision-free trajectory planning. Additionally, the proposed framework incorporates reward shaping and action shielding techniques to enhance safety performance. The proposed framework was evaluated through simulation experiments involving up to 32 aircraft in a generic city-scale area with a 15 km radius, with performance measured by the number of near-midair collisions (NMAC) and computational time. The results demonstrate the planner’s ability to generate safe trajectories for the aircraft in polynomial time, showing its scalability. Moreover, the action shielding and reward shaping strategies show up to a 78.71 and 85.14% reduction in NMAC compared to the baseline planner, respectively.

In within-visual-range (WVR) air combat, basic fighter maneuvers (BFMs) are widely used. A BFM decision support scheme has been proposed to aid human pilots in the complex air combat engagement. Recent artificial intelligence advances provide novel opportunities for the development of BFM decision support research. This paper commences by establishing an air-combat-engagement database. Key features that pilots rely on for BFM decision-making in WVR air combat are analyzed, which identifies the input and output data essential for the development of the BFM decision support scheme. A Long Short-Term-Memory (LSTM)-based BFM decision support scheme is then proposed to map input (i.e., combat situations) to output (i.e., BFM decision). Additionally, Shapley-Additive-Explanations-based explainability analysis is also employed to assess the importance of each input feature in the LSTM blocks, and to explain the contribution of each feature to the BFM decision. To evaluate the effectiveness of the proposed BFM decision support scheme, WVR air-combat tests are conducted, which justify the effectiveness of the proposed scheme.