State Of Unmanned Aerial Vehicle Research Articles

Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications

This article investigates the transmission of downlink control signals for multiple unmanned aerial vehicle (UAV) clusters in collaborative search and rescue operations in mountainous environments. In this scenario, a reconfigurable intelligent surface (RIS) mounted on the UAV is utilized to overcome obstacles between the ground base station (BS) and UAVs. By leveraging the fixed channel of the RIS to the BS, the line-of-sight (LoS) path characteristics of the air-to-air channel, and the position information of the UAV, the RIS forms a directional beam by adjusting the RIS coefficient, which points towards UAVs in the cluster. To ensure low delay in control signaling and UAV state transmission, we adopt semi-persistent scheduling (SPS), which allocates pre-specified periodic intervals to each UAV for the formation of corresponding RIS coefficients. The allocation of time slots is constrained by the transmission intervals required by different UAVs and the number of RISs available. We propose a time slot scheduling scheme for UAVs to reduce inter-cluster interference caused by RIS beams. The time slot allocation problem is formulated as a combinatorial optimization problem. To solve this problem, we first propose an intuitive greedy scheme called local interference minimization (LIM). Building upon the LIM scheme, we propose a rollout-based algorithm called rollout interference minimization (RIM). Through simulation, we compare the LIM and RIM schemes with the benchmark scheduling scheme. The results demonstrate that our proposed scheme significantly reduces interference between UAV clusters while satisfying the conditions of periodic transmission and RIS quantity constraints.

Research on Key Technology of Ship Re-Identification Based on the USV-UAV Collaboration

Distinguishing ship identities is critical in ensuring the safety and supervision of the marine agriculture and transportation industry. In this paper, we present a comprehensive investigation and validation of the progression of ship re-identification technology within a cooperative framework predominantly governed by UAVs. Our research revolves around the creation of a ship ReID dataset, the creation of a ship ReID dataset, the development of a feature extraction network, ranking optimization, and the establishment of a ship identity re-identification system built upon the collaboration of unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs). We introduce a ship ReID dataset named VesselID-700, comprising 56,069 images covering seven classes of typical ships. We also simulated the multi-angle acquisition state of UAVs to categorize the ship orientations within this dataset. To address the challenge of distinguishing between ships with small inter-class differences and large intra-class variations, we propose a fine-grained feature extraction network called FGFN. FGFN enhances the ResNet architecture with a self-attentive mechanism and generalized mean pooling. We also introduce a multi-task loss function that combines classification and triplet loss, incorporating hard sample mining. Ablation experiments on the VesselID-700 dataset demonstrate that the FGFN network achieves outstanding performance, with a Rank-1 accuracy of 89.78% and mAP of 65.72% at a state-of-the-art level. Generalization experiments on pedestrian and vehicle ReID datasets reveal that FGFN excels in recognizing other rigid body targets and diverse viewpoints. Furthermore, to further enhance the advantages of UAV-USV synergy in ship ReID performance, we propose a ranking optimization method based on the homologous fusion of multi-angle UAVs and heterologous fusion of USV-UAV collaborative architecture. This optimization leads to a significant 3% improvement in Rank-1 performance, accompanied by a 73% reduction in retrieval time cost.

Dynamic Analysis and Numerical Simulation of Arresting Hook Engaging Cable in Carried-Based UAV Landing Process

Carrier-based unmanned aerial vehicles (UAVs) require precise evaluation methods for their landing and arresting safety due to their high autonomy and demanding reliability requirements. In this paper, an efficient and accurate simulation method is presented for studying the arresting hook engaging arresting cable process. The finite element method and multibody dynamics (FEM-MBD) approach is employed. By establishing a rigid–flexible coupling model encompassing the UAV and arresting gear system, the simulation model for the engagement process is obtained. The model incorporates multiple coordinate systems to effectively capture the relative motion between the rigid and flexible components. The model considers the material properties, arresting gear system characteristics, and UAV state during engagement. Verification is conducted by comparing simulation results with experimental data from a referenced arresting hook rebound. Finally, simulations are performed under different touchdown points and roll angles of the UAV to analyze the stress distribution of the hook, center of gravity variations, and the tire touch and rollover cable response. The proposed rigid–flexible coupling arresting dynamics model in this paper enables the effective analysis of the dynamic behavior during the arresting hook engaging arresting cable process.

Low-Cost Onboard Multisensor State Estimator Based on Global Mixed Sampling Optimization Filter

Long-term convergent and accurate state estimation( attitude, velocity, and position) is critical to unmanned aerial vehicles (UAVs). However, the measurement accuracy and noise of multiple low-cost sensors onboard are poorer compared to high-precision sensors. To improve the solution accuracy and reliability of UAVs state estimation based on the low-cost sensors, a two-step multi-sensor fusion estimator (federated mixed-sampling Kalman filter, FMSKF) is proposed. Firstly, a multi-sensor integrated navigation model array is designed depending on the different sensors, including the gyroscope, accelerometer, magnetometer, GPS module, and barometer. Then, a three-stage mixed sampling Kalman filter is proposed to obtain the local convergent state vector, which is difficult to meet the flight accuracy requirements. Therefore, a multi-sensor global information optimization filter is proposed to obtain the global convergent navigation solution parameters. Finally, the simulation and flight experimental results and detailed analysis demonstrate that the proposed algorithm can improve the state estimation accuracy, filtering robustness, and obtain desirable navigation performance.

Autonomous Maneuver Decision for Unmanned Aerial Vehicle via Improved Pigeon-Inspired Optimization

As a crucial technology of air-to-air confrontation, autonomous maneuver decision has attracted wide attention in recent years. This paper proposes an improved pigeon-inspired optimization method to realize autonomous maneuver decision for unmanned aerial vehicles (UAVs) rapidly and accurately in an aerial combat engagement. The maneuver library is designed, including some advanced offensive and defensive maneuvers. A dependent set of trial maneuvers is generated to help UAVs make decisions in any tactical situation, and a future engagement state of the opponent UAV is predicted for each trial maneuver. The core of the decision-making process is that the objective function to be optimized is designed using the game mixed strategy, and the optimal mixed strategy is obtained by the improved pigeon-inspired optimization. A comparative analysis with other classical optimization algorithms highlights the advantage of the proposed algorithm. The simulation tests are conducted under four different initial conditions, namely, neutral, offensive, opposite, and defensive conditions. The simulation results verify the effectiveness of the proposed autonomous maneuver decision method.

Noise-Tolerant Identification and Tuning Approach Using Deep Neural Networks for Visual Servoing Applications

Vision-based control of unmanned aerial vehicles (UAVs) has been adopted in a wide range of applications due to the availability of low-cost onboard sensors and computers. Tuning such systems to work properly requires extensive domain-specific experience, which limits the growth of emerging applications. Moreover, obtaining performance limits for UAVs performing visual servoing is difficult due to the complexity of the models used. In this article, we propose a novel noise-tolerant approach for real-time identification and tuning of visual servoing systems. This is based on the deep neural networks (DNNs) classification of the system response generated by the modified relay feedback test (MRFT). The proposed method, called DNN with noise-protected MRFT (DNN-NP-MRFT), can be used with a multitude of vision sensors and estimation algorithms despite high levels of sensor noise. The response of DNN-NP-MRFT to noise perturbations is investigated and its effect on identification and tuning performance is analyzed. The proposed DNN-NP-MRFT is able to detect performance changes induced by the use of high latency vision sensors or by integrating an inertial measurement unit sensor into the UAV states estimation pipeline. Experimental identification closely matches simulation results, which can be used to explain the system behavior and predict the closed-loop performance limits for a given hardware and software setup. We also demonstrate the ability of DNN-NP-MRFT-tuned UAVs to reject external disturbances like wind or human push and pull. Finally, we discuss the advantages of the proposed DNN-NP-MRFT visual servoing design approach compared with other approaches in the literature.

Short-range air combat maneuver decision of UAV swarm based on multi-agent Transformer introducing virtual objects

With the development of Unmanned Aerial Vehicle (UAV) swarm technology, there has been a growing interest in using Artificial Intelligence (AI) to drive UAV swarms for short-range air combat. However, due to the complexity of situation information in UAV swarm air combat, making accurate air combat decisions based on air combat situation information has become a challenge. In this paper, we propose the multi-agent Transformer introducing virtual objects (MTVO) to address this issue. First, the proposed approach designs a multi-agent Transformer network structure by exploiting the homogeneity feature of the UAV state information in the swarm. This structure enables the structured processing of complex situation information. Specifically, the local situation information of each UAV is calculated by self-attention, which reduces the size of the swarm situation information while retaining the information of key UAVs. This approach reduces the difficulty of processing UAV swarm situation information. Moreover, we add a virtual object to the UAV swarm information to assist in calculating the weight distribution of the local situation. The weighted fusion of local situations allows us to obtain a more effective representation of the global situation, which serves as the basis for more accurate air combat maneuver decisions. We demonstrate the performance of the proposed method through air combat simulation results using Reinforcement Learning (RL) methods, and validate the applicability and effectiveness of the MTVO network for the short-range air combat problem of UAV swarms.

QoS-Oriented Sensing–Communication–Control Co-Design for UAV-Enabled Positioning

Unmanned aerial vehicle (UAV)-enabled positioning that uses UAVs as aerial anchor nodes has been envisioned as a promising solution for providing positioning services in harsh environments. In previous research, state sensing and control of UAVs was either ignored or assumed to be performed continuously, resulting in system instability or a waste of wireless resources. Therefore, in this paper, we propose a quality-of-service (QoS)-oriented sensing-communication-control (SCC) co-design scheme for UAV-enabled positioning systems. We first establish mathematical models of UAV state sensing and control. Then, we analyze the influence of sensing scheduling and transmission failure on the stability of UAV, as well as the performance of positioning services in the presence of UAV control error. Based on these models and analysis results, we further study the problem of minimizing the amount of data transmitted by optimizing the sensing scheduling and blocklength allocation under the condition of meeting each user’s requirement on position accuracy. Finally, a heuristic algorithm is developed to solve this mixed-integer nonlinear problem. Numerical results demonstrate the validity and superiority of the proposed scheme. Compared with two benchmark schemes, our scheme reduces the failure rate or resource consumption of positioning services by more than 75% or 80%.

Cooperative Area Search Algorithm for Multi-UAVs in Uncertain Battlefield Environment

Aiming at the problem of multiple unmanned aerial vehicles (UAVs) cooperative area search in uncertain battlefield environment, a multi-UAVs cooperative area search algorithm based on co-evolutionary genetic algorithm (CEGA) is proposed. Firstly, the raster Map and extended Search Probability Map (SPM) model of the task area are constructed, the UAV state model and sensor detection model are established, and the search environment updating method based on the Bayesian criterion is presented. Then, considering the cooperative elements of multi-UAVs, the cooperative area search planning model is established, and based on the rolling optimization idea, the co-evolutionary genetic algorithm with elite retention strategy is used to optimize the solution, and the multi-UAVs cooperative search paths are generated. Through simulation experiments and comparative analysis, the effectiveness of the proposed algorithm is verified.

Trajectory optimization of UAV based on Hp-adaptive Radau pseudospectral method

<p style='text-indent:20px;'>Unmanned Aerial Vehicles (UAVs) have been extensively studied to complete the missions in recent years. The UAV trajectory planning is an important area. Different from the commonly used methods based on path search, which are difficult to consider the UAV state and dynamics constraints, so that the planned trajectory cannot be tracked completely. The UAV trajectory planning problem is considered as an optimization problem for research, considering the dynamics constraints of the UAV and the terrain obstacle constraints during flight. An hp-adaptive Radau pseudospectral method based UAV trajectory planning scheme is proposed by taking the UAV dynamics into account. Numerical experiments are carried out to show the effectiveness and superior of the proposed method. Simulation results show that the proposed method outperform the well-known RRT* and A* algorithm in terms of tracking error.</p>

Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military and civilian fields. With the continuous enrichment and extensive expansion of application scenarios, the safety of UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones to improve drone safety. We deployed a one-class kernel extreme learning machine (OCKELM) to detect anomalies in drone data. By default, OCKELM uses the radial basis (RBF) kernel function as the kernel function of the model. To improve the performance of OCKELM, we choose a Triangular Global Alignment Kernel (TGAK) instead of an RBF Kernel and introduce the Fast Independent Component Analysis (FastICA) algorithm to reconstruct UAV data. Based on the above improvements, we create a novel anomaly detection strategy FastICA-TGAK-OCELM. The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies (ALFA) dataset. The experimental results show that compared with other methods, the accuracy of this method is improved by more than 30%, and point anomalies are effectively detected.

A GNSS Instrumentation Covert Directional Spoofing Algorithm for UAV Equipped With Tightly-Coupled GNSS/IMU

Measurement and navigation users cannot operate without the positioning, navigation, and timing (PNT) service provided by the global navigation satellite system (GNSS) instrumentation, but users are also faced with unpredictable and complex interference and even spoofing. In order to effectively control and even counter the unmanned aerial vehicle (UAV) that may pose security threats, using the GNSS spoofing technology to counter UAV is an effective and important technical means. Compared with the existing spoofing algorithms, this article fully considers the navigation system configuration and flight control principle, as well as antispoofing module of UAV equipped, the UAV dynamic model under GNSS spoofing environment is established, and the response law of UAV state to spoofing is analyzed. In order to realize the covert directional spoofing on UAV equipped with tightly-coupled GNSS and inertial measurement unit (IMU), a GNSS covert spoofing algorithm is proposed. The algorithm includes a fixed-point four-step spoofing strategy and pays attention to avoiding the least-squares residual receiver autonomous integrity monitoring (LSR-RAIM) of UAV, so as to ensure the concealment of the deception process. Through the construction of experimental platform and experimental analysis, the effectiveness and concealment of the algorithm are verified. The experiment shows that the spoofing yaw angle error of GNSS covert directional spoofing is −0.03°. In addition, the spoofer has effectively avoided the LSR-RAIM of UAV system in the whole process, and the evasion rate has reached 100% to achieve covert spoofing. For the actual spoofing scenario application, the spoofing yaw angle error can meet the actual needs. Future work will focus on the research and design of more complex covert spoofing algorithms to meet the spoofing needs of UAV equipped with more complex navigation systems and antispoofing techniques.

Monocular-Vision-Based Precise Runway Detection Applied to State Estimation for Carrier-Based UAV Landing.

Improving the level of autonomy during the landing phase helps promote the full-envelope autonomous flight capability of unmanned aerial vehicles (UAVs). Aiming at the identification of potential landing sites, an end-to-end state estimation method for the autonomous landing of carrier-based UAVs based on monocular vision is proposed in this paper, which allows them to discover landing sites in flight by using equipped optical sensors and avoid a crash or damage during normal and emergency landings. This scheme aims to solve two problems: the requirement of accuracy for runway detection and the requirement of precision for UAV state estimation. First, we design a robust runway detection framework on the basis of YOLOv5 (you only look once, ver. 5) with four modules: a data augmentation layer, a feature extraction layer, a feature aggregation layer and a target prediction layer. Then, the corner prediction method based on geometric features is introduced into the prediction model of the detection framework, which enables the landing field prediction to more precisely fit the runway appearance. In simulation experiments, we developed datasets applied to carrier-based UAV landing simulations based on monocular vision. In addition, our method was implemented with help of the PyTorch deep learning tool, which supports the dynamic and efficient construction of a detection network. Results showed that the proposed method achieved a higher precision and better performance on state estimation during carrier-based UAV landings.

UAV formation control based on distributed Kalman model predictive control algorithm

To address the perturbation of formation of multiple unmanned aerial vehicles (UAVs) subject to external disturbances, an algorithm of distributed Kalman model predictive control is proposed in this paper to improve the accuracy of maintaining a formation in flight. A UAV two-order discrete-time system model was built before devising a Kalman prediction model based on the standard prediction model. The desired formation configuration and neighbor Kalman optimal state estimation were conducted to determine the reference state of UAVs. While taking into account the formation tracking error and input stability, a logarithmic barrier function was introduced in the design of the overall cost function to ensure flight safety. Meanwhile, information was exchanged with neighbors with the directed and time-invariant communication topological structure. With the Lyapunov stability theorem, sufficient conditions were defined for the asymptotic stability of the formation system. Simulation results revealed that the algorithm could effectively suppress the perturbation in the formation of UAVs arising from external disturbances, allowing the formation to cope with the conflicts between individual UAVs.

Proactive Handover Decision for UAVs with Deep Reinforcement Learning.

The applications of Unmanned Aerial Vehicles (UAVs) are rapidly growing in domains such as surveillance, logistics, and entertainment and require continuous connectivity with cellular networks to ensure their seamless operations. However, handover policies in current cellular networks are primarily designed for ground users, and thus are not appropriate for UAVs due to frequent fluctuations of signal strength in the air. This paper presents a novel handover decision scheme deploying Deep Reinforcement Learning (DRL) to prevent unnecessary handovers while maintaining stable connectivity. The proposed DRL framework takes the UAV state as an input for a proximal policy optimization algorithm and develops a Received Signal Strength Indicator (RSSI) based on a reward function for the online learning of UAV handover decisions. The proposed scheme is evaluated in a 3D-emulated UAV mobility environment where it reduces up to 76 and 73% of unnecessary handovers compared to greedy and Q-learning-based UAV handover decision schemes, respectively. Furthermore, this scheme ensures reliable communication with the UAV by maintaining the RSSI above −75 dBm more than 80% of the time.

Influence of Estimated Mission Data on Air Mission Commander Situation Awareness During Datalink Loss

AbstractThis article presents the impact of displaying accurate or inaccurate position estimates on air mission commanders’ (AMC) situation awareness (SA) during datalink interruptions between AMC and unmanned aerial vehicles (UAVs). The lack of current information about UAV state and position during datalink interruptions is suspected to negatively influence the controlling AMC's SA. We assume that the AMC's SA can be supported by displaying a state estimate, based on last transmitted data, instead of displaying last known position. An experimental evaluation in a full‐mission research cockpit simulator showed improvements in subjective SA with estimate shown. Objective SA only improved, if the estimate was accurate. Although we expected a negative impact of inaccurate estimates on objective SA (since the user might falsely trust it), none could be observed. Improvements in SA with accurate estimates, but no observable degradations with inaccurate estimates suggest benefits of showing an estimate in any case.

Networked Security Observer-Based Reference Tracking Control of Stochastic Quadrotor UAV System Under Cyber-Attack:T-S Fuzzy Approach

In this study, a robust <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> networked security observer-based reference tracking control scheme is proposed for the stochastic quadrotor unmanned aerial vehicle (UAV) system under malicious attacks on the actuator and sensor of network control system (NCS). To reduce the computational burden on UAV system, the UAV system is connected with a remote computing unit and the complicated tracking control command can be calculated by remote computing unit. By using the novel discrete smoothed model, the model of attack signals on actuator and sensor can be embedded in system state of UAV and thus the attack signals as well as the quadrotor system state can be simultaneously estimated through a conventional Luenberger observer. Further, the corruption of attack signals on state estimation of UAV is also avoided. To eliminate the effect of unavailable external disturbance and intrinsic fluctuation during the reference tracking control process, a robust <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> networked security observer-based reference tracking control scheme is introduced to attenuate their effects on the NCS of quadrotor UAV. By using the characteristic of convex Lyapunov function, the design condition of robust <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> networked security observer-based tracking control is derived in terms of the nonlinear functional inequalities. Since the nonlinear functional inequalities are not easy to be solved analytically or numerically, the Takagi-Sugeno (T-S) fuzzy interpolation technique is employed to interpolate the nonlinear stochastic quadrotor NCS by a set of linear local systems via fuzzy bases. In this case, the nonlinear functional inequalities can be converted to a set of linear matrix inequalities (LMIs) which can be easily solved by the MATLAB LMI TOOLBOX. Simulation results are provided to validate the effectiveness of the proposed method in comparison with conventional robust observer-based T-S fuzzy tracking control scheme.

Quadrotor motion control using deep reinforcement learning

We present a deep neural-net-based controller trained by a model-free reinforcement learning (RL) algorithm to achieve hover stabilization for a quadrotor unmanned aerial vehicle (UAV). With RL, two neural nets are trained. One neural net is used as a stochastic controller, which gives the distribution of control inputs. The other maps the UAV state to a scalar, which estimates the reward of the controller. A proximal policy optimization (PPO) method, which is an actor–critic policy gradient approach, is used to train the neural nets. Simulation results show that the trained controller achieves a comparable level of performance to a manually tuned proportional-derivative (PD) controller, despite not depending on any model information. The paper considers different choices of reward function and their influence on controller performance.

Flocking Control Algorithms Based on the Diffusion Model for Unmanned Aerial Vehicle Systems

Flock is a desired state of unmanned aerial vehicle (UAV) systems, where multiple UAVs collaborate with each other in energy-efficient ways to complete mission-critical tasks while avoiding mutual collision. However, existing flocking control models still have defects such as potential node collision between followers, lack of capacity analysis, and difficulty in implementation. In this backdrop, this paper presents a model, called the diffusion model, to characterize the flock generation process of a UAV system; moreover, the system capacity of the diffusion model is analyzed. Then, a flocking control algorithm based on the diffusion model is detailed. Finally, a series of experiments based on both the OMNeT++ platform and a heterogeneous UAV-based prototype system are conducted to verify the effectiveness of our proposal. The experimental results show that our flocking control algorithm based on the diffusion model can make the UAV system behave as a flock while avoiding collision under diverse parameter settings.

Analysis of Related Factors of the Precision of Large-scale Surveying of Unmanned Aerial Vehicles (UAV)

With the development of UAV technology, obtaining high-resolution remote sensing images through aerial photography has gradually become popular. This technique has been widely used in various aspects of geomatics. However, state of UAV when flying in the air will be affected by the valley airflow due to small size, light weight, and low flying height in UAV. In addition, the image range of the non-measurement camera equipped with the drone is small, which may lead to a large error in plane and elevation accuracy. For example, it is difficult for the accuracy of drone photogrammetry to meet the survey work of the 1:500 large-scale topographic maps in hills and mountains. Therefore, how to improve the mapping accuracy of the UAV has attract people’s attention at present. In this paper, the source of errors in the 1: 500 UAV mapping process is used as an entry point, and the related factors that affect the accuracy of the mapping are analyzed, so as to improve the design of the UAV route.