The airspeed is an important feedback signal for flight control, and its measurement accuracy is related to the safety of aircraft, especially for hybrid vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAV) in the transition phase. However, offline calibration of the pitot tube cannot fully simulate the situation in real cases, and this is why online calibration after installation is necessary. In addition, the environmental wind field creates a high risk for the conversion flight of a hybrid UAV, thus real-time wind field measurement of the flight field has great significance for the flight path planning of the conversion process. In this article, an innovative method for online calibration of pitot tube and wind field estimation is proposed. After establishing the extended Kalman filter (EKF) for estimation, an analysis method is proposed to analyze the observability of EKF under different flight strategies. Then, the hovering flight is selected as the experimental flight trajectory. The laboratory computer simulation and flight experiment results validate the theory and prove that the proposed method could re-calibrate the scale factor of the pitot tube and estimate the wind field.

In this paper, a control method of a novel tilt-rotor UAV with a blended wing body layout is studied. The novel UAV is capable of vertical take-off and landing and has strong stealth capabilities that can be applied to carrier-borne reconnaissance aircraft. However, the high aspect ratio of blended wing body UAVs leads to a wingtip or oar-tip touchdown problem when adopting the conventional position-attitude control (CPAC) scheme with a large crosswind disturbance. Moreover, when the UAV is subject to interference during reconnaissance, aerial photography, and landing missions, the conventional scheme cannot provide both attitude stability and track accuracy. First, a direct thrust vectoring control (DTVC) scheme is proposed. The control authority of the rotor tilt mechanism was added to enable the decoupling of the attitude and trajectory and to improve the response rate and response bandwidth of the flight trajectory. Second, considering the problems of strong couplings and parameter uncertainties and the nonlinear features and mismatched perturbations that are inevitable in the tilt-rotor, we designed a robust UAV controller based on the backstepping sliding mode control method and determined the stability of the control system through the Lyapunov function. Finally, in the case of crosswire interference during vertical takeoff and landing and the aerial photography missions of the UAV, the numerical simulation of the CPAC scheme and the DTVC scheme was carried out, respectively, and the Monte Carlo random test method was introduced to conduct the statistical test of the landing accuracy. The simulation results show that the DTVC scheme improves the landing accuracy and speed compared to the CAPC scheme and decouples the position control loop from the attitude control loop, finally enabling the UAV to complete the flight control in the VTOL phase.

The integrated navigation of inertial navigation systems (INS) and the Global Positioning System (GPS) is essential for small unmanned aerial vehicles (UAVs) such as multicopters, providing steady and accurate position, velocity, and attitude information. Nevertheless, decreasing navigation accuracy is a serious threat to flight safety due to the long-term drift error of INS in the absence of GPS measurements. To bridge the GPS outage for multicopters, this paper proposes a novel navigation reconstruction method for small multicopters, which combines the vehicle dynamic model and micro-electro-mechanical system (MEMS) sensors. Firstly, an induced drag model is introduced into the dynamic model of the vehicle, and an efficient online parameter identification method is designed to estimate the model parameters quickly. Secondly, the body velocity can be calculated from the vehicle model and accelerometer measurement. In addition, the nongravitational acceleration estimated from body velocity and radar height are utilized to yield a more accurate attitude estimate. Fusing the information of the attitude, body velocity, magnetic heading, and radar height, a navigation system based on an error-state Kalman filter is reconstructed. Then, an adaptive measurement covariance algorithm based on a fuzzy logic system is designed to reduce the weight due to the disturbed acceleration. Finally, the hardware-in-loop experiment is carried out to demonstrate the effectiveness of the proposed method. Simulation results show that the proposed navigation reconstruction algorithm aided by the vehicle model can significantly improve navigation accuracy during a GPS outage.

The optimal trajectory planning for a novel tilt-rotor unmanned aerial vehicle (UAV) in different take-off schemes was studied. A novel tilt-rotor UAV that possesses characteristics of both tilt-rotors and a blended wing body is introduced. The aerodynamic modeling of the rotor based on blade element momentum theory (BEMT) is established. An analytical method for determining the taking-off envelope of tilt angle versus airspeed is presented. A novel takeoff–tilting scheme, namely tilting take-off (TTO), is developed, and its optimal trajectory is designed based on the direct collocation method. Parameters such as the rotor thrust, tilt angle of rotor and angle of attack are chosen as control variables, and the forward velocity, vertical velocity and altitude are selected as state variables. The time and the energy consumption are considered in the performance optimization indexes. The optimal trajectories of the TTO scheme and other conventional schemes including vertical take-off (VTO) and short take-off (STO) are compared and analyzed. Simulation results indicate that the TTO scheme consumes 47 percent less time and 75 percent less energy than the VTO scheme. Moreover, with minor differences in time and energy consumption compared to the STO scheme, but without the need for sliding distance, TTO is the optimal take-off scheme to satisfy the flight constraints of a novel tilt-rotor UAV.

The objective of person re-identification (Re-ID) is to recognize a given target pedestrian across different cameras. However, perspective variations, resulting from differences in shooting angles, often significantly impact the accuracy of person Re-ID. To address this issue, this paper presents an attention-enhanced person Re-ID algorithm based on appearance–gait information interaction. Specifically, appearance features and gait features are first extracted from RGB images and gait energy images (GEIs), respectively, using two ResNet-50 networks. Then, a multimodal information exchange module based on the attention mechanism is designed to build a bridge for information exchange between the two modalities during the feature extraction process. This module aims to enhance the feature extraction ability through mutual guidance and reinforcement between the two modalities, thereby improving the model’s effectiveness in integrating the two types of modal information. Subsequently, to further balance the signal-to-noise ratio, importance weight estimation is employed to map perspective information into the importance weights of the two features. Finally, based on the autoencoder structure, the two features are weighted and fused under the guidance of importance weights to generate fused features that are robust to perspective changes. The experimental results on the CASIA-B dataset indicate that, under conditions of viewpoint variation, the method proposed in this paper achieved an average accuracy of 94.9%, which is 1.1% higher than the next best method, and obtained the smallest variance of 4.199, suggesting that the method proposed in this paper is not only more accurate but also more stable.

This paper develops a distributed control framework for leader–follower formation tracking in multi-agent unmanned aerial vehicle (UAV) systems. Feedforward compensation converts the networked tracking task into local error stabilization problems, and an L_p zero-sum game is used to construct a finite-time robust feedback law. The disturbance-free closed loop is proven to converge in finite time, whereas under nonzero disturbances, the result is a certified L_p attenuation bound rather than exact finite-time convergence. A single-critic adaptive dynamic programming architecture approximates the value function, and an offline sampled data training procedure avoids injecting probing noise into the physical plant. In the reported planar outer-loop simulation, the local errors settle within 4.3 s, compared with 8.2 s for the quadratic L₂ baseline, and the reported cumulative disturbance attenuation indicator decreases from 2.74 to 0.48. The current validation uses a fully actuated translational outer-loop abstraction; extensions to underactuated six-degree-of-freedom dynamics, saturation, and hardware experiments are left for future work L_p.