The space-based Automatic Dependent Surveillance-Broadcast (ADS-B) system is vital for achieving global aviation surveillance, and one effective approach to mitigate co-channel interference within the system is the adoption of multi-beam reception. However, traditional static beam optimization methods struggle to maintain optimal reception performance under time-varying conditions due to the dynamic and non-uniform distribution of aircraft within satellite coverage areas. To address this challenge, this paper first establishes a dynamic optimization model for multi-beam reception, and presents a physics-consistent Markov Decision Process (MDP) formulation with an elaborate design of the state space and reward function based on space-based ADS-B characteristics. Then, a dual-layer predictive beamforming control (DPBC) method based on deep reinforcement learning (DRL) is proposed. It incorporates a State Prediction Network (SPN) to restore the Markov property by compensating the state spatiotemporal misalignment, and employs a capacity constrained clustering initialization strategy to embed geometric priors, thereby improve exploration efficiency in high-dimensional continuous beamforming control. Experiments using in-orbit data demonstrate that the DPBC method dynamically adjusts beam configurations to achieve superior reception performance compared to static methods across varying aircraft distributions, and can achieve full coverage with update intervals below 8 s compared to other DRL dynamic optimization approaches. Therefore, the proposed method is effective and adaptive for dynamic beam control in the space-based ADS-B system, meeting the required surveillance performance for air traffic control and having practical application potential in the growing aviation sector.

Space-based Automatic Dependent Surveillance-Broadcast (ADS-B) can provide seamless real-time surveillance service for global aircraft due to its global coverage characteristic compared with terrestrial ADS-B systems. However, the low signal-to-noise ratio (SNR) caused by long transmission distance degrades the continuity in the required surveillance performance (RSP). To ensure surveillance continuity, this paper proposes a coherent reception method based on the expectation maximization (EM) algorithm, realizing high-sensitivity reception for space-based ADS-B signals with low SNR. The reception requirements and the SNR threshold when using the coherent method are first analyzed. An EM model is then established to estimate ADS-B signals’ carrier frequency offset (CFO) and carrier phase offset (CPO) accurately. Simulation experiments and in-orbit test results show that the reception sensitivity metric of space-based ADS-B receivers can be effectively heightened through the proposed method, which can reach -102 dBm at 90

As drones and unmanned aerial vehicles (UAVs) are used in different scenarios, a variety of potential risks and safety challenges have arisen. One of the threats is that an increasing number of UAV encounter events are found near the airport in recent years, which pose significant dangers to manned aircraft and result in accidents. However, only a few studies examine the impacts of these events and propose effective countermeasures to enhance safety. To unveil the risks of UAV risk events (incidents or accidents) and examine the mechanism with risk factors, this study uses a tree-augmented naive Bayes network (TAN-BN). This method analyses the relationships among risk factors and UAV accidents/incidents to assess the efficacy of risk mitigation measures. Environmental, technological and human factors are simultaneously considered in constructing the Bayesian network. The analysis results reveal 12 specific risk factors that are significantly associated with UAV accidents/incidents in UAV operation scenarios, among which flight control system failure (the most critical factor), remote communication failure, other aircraft approaching, loss of electrical power, adverse weather, electromagnetic interference, operational errors, violations and risk factors leading to loss-of-control in flight are recognised as the most prominent factors. Based on these, five targeted risk mitigation measures are comprehensively implemented and evaluated. Moreover, a case study using UAV operation data near the Guanghan airport is introduced to justify the generalisability of the proposed TAN-BN model and the effectiveness of risk mitigation measures.

Automatic dependent surveillance-broadcast (ADS-B) is widely deployed in both civil and uncrewed aviation networks, yet its plaintext broadcast design leaves it vulnerable to eavesdropping, spoofing, and message forgery. Existing cryptographic solutions incur excessive overhead, depend on complex key infrastructures, and fail to accommodate the broadcast nature of ADS-B, making them unsuitable for real-time internet of things (IoT) aviation scenarios. In this article, we propose ADSB-identity-based broadcast encryption (IBBE), the first lightweight and scalable security scheme tailored for confidential ADS-B broadcast communication in IoT-enabled aerial networks. ADSB-IBBE integrates a novel IBBE construction to enable efficient key distribution, together with a purpose-built, format-preserving stream cipher that secures critical ADS-B fields without extending the message size. It further incorporates a customized sharding consortium blockchain for decentralized identity management and rapid key revocation, while a header compression mechanism reduces transmission overhead. Security analysis confirms indistinguishability against selective-identity chosen ciphertext attacks (IND-sID-CCAs) security under the random oracle model (ROM). Experiments show over 80% lower computational cost and 90% reduced communication overhead compared to the most demanding baseline, with increasing advantages as the number of receivers grows, achieving the lowest overhead of 510-bit header and 80-bit ciphertext. These results demonstrate the suitability of our scheme for secure and efficient ADS-B communication in next-generation IoT-enabled air traffic management (ATM) systems.

As the next-generation air traffic surveillance technology, the Automatic Dependent Surveillance-Broadcast (ADS-B) system plays a critical role in broadcast communication for intelligent vehicles such as autonomous aerial vehicles and aircraft. However, the unauthenticated channels expose the ADS-B system to a high risk of attacks. Besides, the centralization of intelligent vehicles management center is susceptible to single point failures and performance bottlenecks. In this paper, we propose a robust and distributed intelligent vehicles management architecture leveraging blockchain technology to resolve security and performance issues lying in the registration, update, and revocation of intelligent vehicles' identity public keys. Then, we leverage certificate-less short signature (CLSS) to achieve the dynamic identity verification of ADS-B system participants while ensuring message integrity, non-repudiation, and compatibility with low-data-bit message packets. Furthermore, to enhance intelligent vehicles transaction process efficiency within the proposed architecture, we develop an Verifiable Random Function-based Byzantine Fault Tolerance protocol (VRBFT) combining threshold signatures. Finally, through a comprehensive security analysis and feasibility assessment, the results indicate that authentication requires less than 30 ms, block generation occurs within 200 ms, and the system exhibits high throughput and low latency. These findings demonstrate that our solution significantly enhances the security of the ADS-B system and offers a practical and promising approach for real-time, large-scale intelligent vehicle management.

Passive positioning technology is a kind of positioning technology that determines the position of a signal source by passively receiving the signals emitted by the signal source. The direct positioning determination (DPD) based on the reception of signals by array signal antennas has the advantage of higher positioning accuracy and is widely used. However, the DPD suffers from disadvantages such as a large computational burden. In this article, the artificial lemming algorithm (ALA) is introduced into the optimization process of the DPD cost function (i.e., ALA-DPD). Based on the ALA-DPD, the Optimal-Preservation Gradient-Enhanced ALA for DPD (OGALA-DPD) is proposed. The iterative optimization ability of the algorithm is enhanced by introducing the strategies of optimal individual retention and a bidirectional optimization search method. The results of the simulation demonstrate that the 95% simulation convergence generation of OGALA-DPD is approximately 22 % higher than that of ALA-DPD. Furthermore, in the context of the comparative analysis of other four algorithms, the OGALA-DPD algorithm has been demonstrated to exhibit a reduced error loss. Experiments have demonstrated that the OGALA-DPD proposed in this paper provides a more efficient and accurate solution for single-station passive positioning technology.

The revolutionary development of the unmanned aerial vehicles (UAVs) has brought a paradigm shift towards the high-tech era for modern people's lifestyles. However, current UAV regulatory techniques have notable deficiencies in terms of recognition performance and resource consumption. In con-junction with the abuse of UAVs, these issues have raised critical concerns about personal privacy and public security. This paper proposes a novel methodology for effectively identifying UAVs via radio frequency (RF) signal. This methodology is herein referred to as the Identification Model with Multi-Domain Self-Attention (IM-MDSA). The short-time Fourier transform (STFT) is firstly utilized to generate the time-frequency spectra (TFS), thereby decoupling the UAV signals from the same-band interference and ambient noise. Secondly, two feature extractors based on improved self-attention mechanisms are developed to capture the variability in RF fingerprints across the space, time, and frequency domains. Finally, a lightweight classifier is desig-ned based on an improved convolutional neural network (CNN). Numerous experiments conducted on DroneRFb-Spectra dataset show that IM-MDSA achieves an accuracy rate of 99.64% for 12 different categories of signal while concurrently requiring as few as 0.52 million parameters. This results underscore the efficacy of IM-MDSA compared to alternative studies, thereby providing a promising method for the future UAV identification.

Urban combat environments pose complex and variable challenges for UAV path planning due to multidimensional factors, such as static and dynamic obstructions as well as risks of exposure to enemy detection, which threaten flight safety and mission success. Traditional path planning methods typically depend solely on the distribution of static obstacles to generate collision-free paths, without accounting for constraints imposed by enemy detection and strike capabilities. Such a simplified approach can yield safety-compromising routes in highly complex urban airspace. To address these limitations, this study proposes a multi-parameter path planning method based on reachable airspace visibility graphs, which integrates UAV performance constraints, environmental limitations, and exposure risks. An innovative heuristic algorithm is developed to balance operational safety and efficiency by both exposure risks and path length. In the case study set in a typical mixed-use urban area, analysis of airspace visibility graphs reveals significant variations in exposure risk at different regions and altitudes due to building encroachments. Path optimization results indicate that the method can effectively generate covert and efficient flight paths by dynamically adjusting the exposure index, which represents the likelihood of enemy detection, and the path length, which corresponds to mission execution time.

The automatic dependent surveillance broadcast (ADS-B) system is a critical surveillance technology in air traffic management (ATM), essential for enhancing aviation safety and operational efficiency. However, ADS-B broadcasts plaintext messages over open channels without authentication mechanisms, and is constrained by message length limitations and low bandwidth, making it susceptible to various attacks, including deception, tampering, and replay. To address these challenges, we propose a secure and lightweight blockchain-integrated certificateless signature scheme (ECB-CLS) tailored for ADS-B systems with packet resilience. Specifically, we introduce an efficient signature verification algorithm based on elliptic curve cryptography (ECC) that supports batch verification without the need for certificate management, complex bilinear pairing, or hash-to-point calculations, significantly reducing computational overhead. Furthermore, our scheme leverages blockchain to ensure the decentralization and traceability of massive public keys and provides provable security against Type I and Type II adversary attacks. To address packet loss in practical environments, we incorporate both standard and enhanced Reed-Solomon (RS) coding to recover lost data. Experimental evaluations demonstrate that our blockchain-integrated ECB-CLS scheme offers substantial advantages in computational efficiency and signature length compared to existing methods, while also showing that RS coding introduces low-performance overhead. This makes our solution highly suitable for resource-constrained ADS-B systems.

Radio Frequency Fingerprint Identification (RFFI) technology provides a means of identifying spurious signals. This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast (ADS-B) signal spoofing problems. However, the effects of circuit changes over time often lead to a decline in identification accuracy within open-time set. This paper proposes an ADS-B transmitter identification method to solve the degradation of identification accuracy. First, a real-time data processing system is established to receive and store ADS-B signals to meet the conditions for open-time set. The system possesses the following functionalities: data collection, data parsing, feature extraction, and identity recognition. Subsequently, a two-dimensional Time-Frequency Feature Diagram (TFFD) is proposed as a signal pre-processing method. The TFFD is constructed from the received ADS-B signal and the reconstructed signal for input to the recognition model. Finally, incorporating a frequency offset layer into the Swin Transformer architecture, a novel recognition network framework is proposed. This integration can enhance the network recognition accuracy and robustness by tailoring to the specific characteristics of ADS-B signals. Experimental results indicate that the proposed recognition architecture achieves recognition accuracy of 95.86% in closed-time set and 84.33% in open-time set, surpassing other algorithms.

As Automatic Dependent Surveillance-Broadcast (ADS-B) devices are widely used, verification of the authenticity of ADS-B signals becomes increasingly important. False signals can disrupt normal aircraft navigation and seriously threaten airspace safety. This paper proposes an ADS-B signal recognition method based on the ambiguity function (AF). It utilizes two-dimensional convolutional images to represent radio frequency fingerprint (RFF) characteristics in both time and frequency domains by convolving the actual received signal with an ideal reconstructed signal. A convolutional neural network is used to extract the fingerprint information of the signal and verify the identity of the radiation source, achieving the purpose of determining the authenticity of the signal. The experiments analyzed the impact of the number of aircraft categories and signal-to-noise ratio on recognition results, confirming the effectiveness of ADS-B recognition based on the AF. This study demonstrates the feasibility of RFF in the aerospace industry and can be generalized for use in Internet of Things (IoT) devices.

Due to its advantages such as high precision and ease of use, the Automatic Dependent Surveillance-Broadcast (ADS-B) system has surpassed radar and is more widely utilized in air traffic management. However, security issues within the ADS-B system, such as lack of identity authentication, inability to ensure data integrity, and susceptibility to single-point failures, hinder its further development by allowing attackers to easily disrupt the system. In order to address these issues and provide security while maintaining performance for the ADS-B system, we conduct the following research. Firstly, we select a signature algorithm with a signature length of only |ℤ_q^*| , which ensures security while maintaining high system performance. Secondly, we design a blockchain-based secure ADS-B system to guarantee system security, incorporating a multi-leader BFT protocol where all nodes can act as leader to propose transactions, thus reducing transmission overhead and increasing throughput. Finally, we conduct performance evaluation of the system, demonstrating that our solution can achieve all stated security objectives while ensuring high system performance.

In this paper, a quantitative ground risk assessment mechanism is proposed in which urban ground features are extracted based on high-resolution data in a satellite image when unmanned aerial vehicles (UAVs) operate in urban areas. Ground risk distributions are estimated and a risk map is constructed with a multi-layer method considering the comprehensive risk imposed by UAV operations. The urban ground feature extraction is first implemented by employing a K-Means clustering method to an actual satellite image. Five main categories of the ground features are classified, each of which is composed of several sub-categories. Three more layers are then obtained, which are a population density layer, a sheltering factor layer, and a ground obstacle layer. As a result, a three-dimensional (3D) risk map is formed with a high resolution of 1 m × 1 m × 5 m. For each unit in this risk map, three kinds of risk imposed by UAV operations are taken into account and calculated, which include the risk to pedestrians, risk to ground vehicles, and risk to ground properties. This paper also develops a method of the resolution conversion to accommodate different UAV operation requirements. Case study results indicate that the risk levels between the fifth and tenth layers of the generated 3D risk map are relatively low, making these altitudes quite suitable for UAV operations.

Under the influence of COVID-19, some residents have been sealed at home. To reduce the risk of cross-infection when distributing materials, how to safely and effectively has become a major problem. Considering that the application technology of drones in the field of logistics has become more and more mature in recent years, the joint distribution mode of trucks and drones has been proposed. According to the characteristics of the problem, a three-stage solution algorithm is designed; the first stage is to set the safety distance threshold between the demand point and the truck stop and use the K-means algorithm to find the truck stop site; the second stage is to design an improved ALNS (Adaptive Large Neighborhood Search) algorithm to optimize the drone path and compare the results with the optimizer Gurobi; and the third stage is to optimize the truck route. All three phases are implemented using python programming, and the second stage of the core is studied and evaluated, which shows that the ALNS algorithm solves faster and with better results than Gurobi.

Air logistics transportation has become one of the most promising markets for the civil drone industry. However, the large flow, high density, and complex environmental characteristics of urban scenes make tactical conflict resolution very challenging. Existing conflict resolution methods are limited by insufficient collision avoidance success rates when considering non-cooperative targets and fail to take the temporal constraints of the pre-defined 4D trajectory into consideration. In this paper, a novel reinforcement learning-based tactical conflict resolution method for air logistics transportation is designed by reconstructing the state space following the risk sectors concept and through the use of a novel Estimated Time of Arrival (ETA)-based temporal reward setting. Our contributions allow a drone to integrate the temporal constraints of the 4D trajectory pre-defined in the strategic phase. As a consequence, the drone can successfully avoid non-cooperative targets while greatly reducing the occurrence of secondary conflicts, as demonstrated by the numerical simulation results.

In this paper, a methodology to assess ground risk with multi-uncertainties is introduced, which is associated with a major unmanned aerial vehicle (UAV) in-flight incident. In the assessment model, random factors are taken into account including uncertainty in the drag force, uncertainty in the UAV velocity, and the random effects of local wind. The probability distribution of impact positions is first estimated by using a second-order drag model with probabilistic assumptions regarding the least well-known parameters. Then, an approach for modeling and estimating the ground risks is presented, in which the ground casualties are set as the safety index. In the multifactor risk estimation model, ground casualty areas covered by the UAVs’ debris are determined. Correspondingly, the probability of fatal injuries to people is derived by addressing the protection effects, impact energy, and energy threshold a person can sustain. Further, four kinds of sheltering effects are defined. Finally, the affected area on the ground is partitioned into six zones, taking into consideration the density and distribution of the local population. Case studies are conducted for fixed-wing and rotary-wing UAVs. Risk levels on the ground are obtained and compared with the widely accepted target safety level of manned aircrafts.

Space-based Automatic Dependent Surveillance-Broadcast (ADS-B) technology has become a new research hotspot in the field of aviation surveillance due to its global coverage capability. While the space-based ADS-B system can solve the blind spots of the terrestrial ADS-B system, it also brings new problems such as weak ADS-B reception energy and increased signal conflicts. Aiming at the demodulation of weak signals for space-based ADS-B, this paper derived the signal-to-noise ratio (SNR) threshold for the demodulation of space-based ADS-B signal and analyzed the BER distribution as well as the relationship between SNR and correct decoding probability of the coherent demodulation method. Through simulation experiments and in-orbit data verification, the theoretical analysis of this paper met the actual situation, and the coherent demodulation method can meet the requirements of space-based ADS-B weak signal reception, which provides a direction for the improvement of the sensitivity of space-based ADS-B receivers.

The minimum Hamming distance of satellite-based Automatic Dependent Surveillance-Broadcast (ADS-B) signals at low signal-to-noise ratios (SNRs) is only 6, which is inadequate to meet airspace surveillance requirements in terms of packet decoding probability (PD). An enhanced error correction algorithm combined with directed density-based clustering for satellite-based ADS-B signals is proposed to address this phenomenon, and its performance is verified by simulation. Firstly, the density-based clustering model will cluster a given signal sequence according to its partial minimum Hamming distance from other sequences, reducing the chance of undetectable errors. Secondly, the proposed error syndrome matrix built offline streamlines the Brute Force correction, preserving on-star resources. Finally, the a, b algorithm compensates for low SNR-induced unreliability of confidence arrays through error correction depth a and error correction capability b. The simulation results show that the 20, 10 error correction algorithm can achieve a PD of 86.4

With the rapid development of air traffic control (ATC) globalization, the space-based ADS-B system has attracted extensive research in the field of ATC because of its global coverage characteristics to overcome the blind spots of existing ground-based systems. Beihang Aviation Satellite-1 is China's first ADS-B technology verification satellite for ATC surveillance requirements and has conducted in-orbit technology verification of space-based ADS-B. This paper discusses the availability of Beihang Aviation Satellite-1 in various flight phases for civil aviation and general aviation surveillance applications with its in-orbit data, and analyzes the surveillance coverage radius and the position message update interval. The results show that Beihang Aviation Satellite-1 has the capability to provide global surveillance of civil aircraft in all phases of takeoff/landing, departure/approach and cruise, as well as the capability to track general aviation aircraft on a global scale. The maximum surveillance coverage radius is more than 2,800 km and the update interval is less than 8 seconds in different-density airspace.

Under the demand of urban terminal "Last Mile Delivery" scenario, finding a safe and efficient UAV path planning method is a crucial issue of current research. Nowadays, reinforcement learning is widely used in UAV path planning, but it is difficult to ensure the safety of the learning or execution phases due to the lack of hard constraints. Aiming at the constraints above, this paper studies how to combine safety properties with RL algorithm to find a safe path and proposes a safe reinforcement learning method called Shield-DDPG for UAV path planning. In the method, a protection mechanism Shield is mainly introduced to prevent the algorithm from outputting unsafe actions. Further, the state space, action space, and reward function are specifically improved for efficiency and safety. Then we compare the Shield-DDPG algorithm with the DDPG and RRT algorithm in some different scenarios, and the results show that the proposed algorithm has a better performance. With the proposed path planning method, UAV can learn well to efficiently and safely reach the destination via calling the trained policy. This research is of great importance to UAV operations and practical applications in complex urban airspace.