Efficient UAV path-planning algorithms significantly improve inspection efficiency and reduce costs. However, due to the limitation of battery capacity, the endurance of existing UAVs is limited, making it difficult for them to directly undertake information collection, cruising, and inspection tasks over large work areas. This paper considers the problem of path allocation for multiple UAVs to minimize work time and reports research on multi-UAV (unmanned aerial vehicle) multi-task long-duration operation path planning. We propose a multi-UAV collaborative search algorithm based on the greedy algorithm (MUCS-GD) and a multi-UAV collaborative search algorithm based on the binary search algorithm (MUCS-BSAE), and later apply two UAV collaborative search algorithms to five UAV flight paths: (1) a snake curve path, (2) a “square wave signal” curve path, (3) a Peano curve path, (4) a Hilbert curve path, and (5) a Moore curve path, and compare the simulation results. We found that the performance of MUCS-BSAE was better than that of MUCS-GD in all of the above flight paths. In addition, the path with the “square wave signal” curve was the near-optimal path among all the flight paths. Finally, we improved the MUCS-BSAE applied on the “square wave signal” curve path and obtained an improved collaborative search algorithm for multiple UAVs based on the binary search algorithm (IMUCS-BSAE), which further reduced the working time of the UAV. 

Efficient Multi-UAV Path Planning for Collaborative Area Search Operations

In Vehicle-to-vehicle communication the Tera hertz band communication is not suitable as it affects health and RIS is the hopeful technique. This paper suggests a UAV to UAV or UAV to base station type of communication and its determining parameters under hardware constituting its errors. The position-based information is taken as a data source to avoid doppler effect and also spectrally efficient under a high-speed communication at hardware problems. This also aids the multi-RIS system. The proposed method upsurges the spectral efficiency for increasing the multi-RIS based communication. Comparisons are based on optimization of diverse phase shift with maximum spectral efficiency based on the power control. The proposed method eliminates the doppler spread. The delay spread is quite little possible and it can also be further more decreased by the using novel form of signal like delay-doppler modulation technique that utilizes a two-dimensional waveform. Simulation results show that increasing the number of IRS elements in each IRS gives a better spectral efficiency. 

UAV and Vehicular Communication with Hardware Impairments in Multiple RIS System

Robust GNSS spoofing detection against UE maneuver in a GNSS-5G mmWave hybrid positioning system

In Vehicle-to-vehicle communication the Tera hertz band communication is not suitable as it affects health and RIS is the hopeful technique. This paper suggests a UAV to UAV or UAV to base station type of communication and its determining parameters under hardware constituting its errors. The position-based information is taken as a data source to avoid doppler effect and also spectrally efficient under a high-speed communication at hardware problems. This also aids the multi-RIS system. The proposed method upsurges the spectral efficiency for increasing the multi-RIS based communication. Comparisons are based on optimization of diverse phase shift with maximum spectral efficiency based on the power control. The proposed method eliminates the doppler spread. The delay spread is quite little possible and it can also be further more decreased by the using novel form of signal like delay-doppler modulation technique that utilizes a two-dimensional waveform. Simulation results show that increasing the number of IRS elements in each IRS gives a better spectral efficiency.

Amaç: İdiyopatik intrakraniyal hipertansiyon (İİH) tanısı almış hasta grubunda çeşitli nöroradyolojik görüntüleme bulgularının diyagnostik katkılarını 

Evaluation of Magnetic Resonance Imaging Findings and Clinical Features in Idiopathic Intracranial Hypertension Patients

Adaptive risk-free coordinated trajectory planning for UAV cluster in dynamic obstacle environment

Kernel method is a non-parametric linearization method for system modeling, which uses nonlinear projection from input data space to high-dimensional Hilbert feature space and employs kernel function for hiding the projection operator in a linear learner to replace inner product calculation in Hilbert space and to avoid the curse of dimensionality. Kernel method is data-driven and learnable to nonlinear model. For independency to a priori model, it provides a novel way of state estimation for nonlinear dynamic systems with model uncertainties. In this paper, an adaptive kernel learning Kalman filtering method is proposed and applied into the problem of maneuvering target tracking. Without use of a priori system model, the method performs Kalman filtering in a reproducing kernel Hilbert space (RKHS) by estimating conditional embedding operator (CEO) as system state transfer function from training data. For the stochastic uncertainty in system model, the maximum correntropy criterion (MCC) is introduced to obtain kernel parameter optimization and balance of estimation performance, while a sliding window is designed for online updating estimation of state transfer function to get adaptability to unknown system dynamics. Such the construction for kernel Kalman filtering (KKF) is helpful to extend application from time-series signal processing to state estimation of uncertain dynamical systems. Simulation scenarios include average sunspot prediction, hovering target tracking and hypersonic maneuvering target tracking, corresponding to verification in low-dynamic, periodic-dynamic and high-dynamic systems. Numerical results have illustrated that the proposed adaptive KKF can realize model-free tracking for target that has nonlinear dynamical motion, shown adaptability to non-cooperative target maneuver and better precision and convergence speed than typical model-based algorithms.

Adaptive Kernel Learning Kalman Filtering With Application to Model-Free Maneuvering Target Tracking

Most traditional unmanned aerial vehicle (UAV) countermeasures algorithms use centralized task allocation algorithm for target matching and optimization in static environment with limited aircraft units. It is questionable for the dynamic confrontation task of large-scale swarm. In order to solve the task allocation problem of UAV swarm dynamic countermeasure decision making, extended CBBA algorithm (ECBBA) is proposed in this paper. This distributed target allocation algorithm can effectively improve algorithm efficiency and support UAV dynamic real-time target allocation, suitable for large-scale swarm dynamic countermeasures. Finally, simulation experiments are carried out to verify the effectiveness of the ECBBA algorithm.

A Distributed Target Allocation Algorithm for UAV Swarm Confrontation

Without dependency on a prior model, kernel method provides a novel way of state estimation for nonlinear dynamic system with uncertainty. To perform maneuver target tracking without system model, a sliding-windowed adaptive kernel dynamic filtering method is proposed in this paper. The method performs Kalman filtering on a Reproducible Kernel Hilbert Space (RKHS) by estimating conditional embedding operator (CEO) as system state transfer function with training data. For system stochastic uncertainty, maximum correntropy criterion (MCC) is introduced for realizing kernel parameter optimization and the balance of estimation performance. The novel design of a sliding window is necessary for updating the estimates of the state transfer function online and to generate adaptability to unknown system dynamics. Such construction of the kernel Kalman filtering (KKF) is helpful to extend its application range from time-series signal processing to state estimation of uncertain dynamic systems. A typical scenario of hypersonic maneuvering target tracking is used for simulation. The results have demonstrated that the proposed method can realize effective model-free tracking to a target with nonlinear dynamical motion, showing the adaptability to non-cooperative target maneuver and better precision than typical model-based algorithms.

Kernel Adaptive Dynamic Filtering for Hypersonic Maneuvering Target Tracking

Flatness-based path planning for fixed-wing UAVs in tight formation under synergistic aerodynamic constraints

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