CERES :: Browsing by Author "Xu, Zhengjia"

Browsing by Author "Xu, Zhengjia"

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Open Access
Analysis of wireless connectivity applications at airport surface
(IEEE, 2020-04-30) Ayub, Shahid; Petrunin, Ivan; Tsourdos, Antonios; Xu, Zhengjia
The main objective of the current work is to carry out the research to explore the potential wireless communication technologies that can be used during a flight operation at the airport surface for current and potential data applications in future. An important part of this work is the analysis of these services and applications from the perspective of understanding the stakeholders and communication means involved. Different communication services including both critical and non-critical ones are analyzed for aircrafts, airlines, and airport connectivity covering flight stages from landing at the airport to taking off from the airport. We are also proposing the ways of more effective use of communication means including the proposed measures for throughput improvement in order to better meet the needs of the airport stakeholders
Open Access
Autonomous Architecture for UAV-based Agricultural Survey
(AIAA, 2020-01-05) Mondal, Sabyasachi; Williamson, Alex; Xu, Zhengjia; Tsourdos, Antonios
This paper presents the concept of autonomous architecture for UAVs to minimize human involvement for agricultural surveying. Agricultural surveying applications include monitoring crop health and collecting ground truth data for treatment and harvest planning. The proposed architecture can automate the entire surveying process and helps farmers to obtain specific and essential knowledge about the crop more quickly. This architecture helps to increase crop yields while reducing operating costs. The autonomy is achieved by integrating functional modules such as Mission Planning, image processing, task allocation, and communication. This work is focused on describing the mission planning and task allocation since image processing is not within the scope.
Open Access
Autonomous collection of ground truth data by unmanned aerial vehicles instructed using SMS text messages
(IEEE, 2020-02-17) Williamson, Alex; Mondal, Sabyasachi; Xu, Zhengjia; Tsourdos, Antonios
This paper describes a solution to increase the efficiency of collecting agricultural ground truth data by the use of one or more off-the-shelf drones to autonomously collect high quality RGB image data at low level, through the incorporation of a bespoke smartphone application that receives routing path-planned location data in the form of Short Message Service (SMS) text messages.
Open Access
Autonomous navigation with taxiway crossings identification using camera vision and airport map
(AIAA, 2024-01-04) Delezenne, Quentin; Petrunin, Ivan; Xu, Zhengjia; Neptune, Jonathan; Bleakley, Timothy
With increasing demands of unmanned aerial vehicle (UAV) operations envisioned for the future of aviation, the number of pilots will be much lower than the number of drones, necessitating an increased level of autonomy in drones to alleviate workload. Autonomous UAV taxiing enables autonomy to move on the ground, specifically from the gate to the runway and vice versa without human intervention. This study presents a lightweight vision-based autonomous taxiway navigation system, exploring the fusion of camera vision feed under the nose and airport map data to offer guidance and navigation. A sliding window mechanism is applied in centreline identification to detect line divergence. Centreline representations including divergence, direction and heading are cross-referenced with the airport database for localisation and generating navigation solutions. A simple proportional integral derivative (PID) controller is developed over aircraft dynamic models aligned with Eagle Dynamic’s Digital Combat Simulator to demonstrate the centreline following function. The overall system performance is assessed through simulations, encompassing individual functionality performance tests including centreline extraction test, line matching test, line-to-follow test, generalisation capability test, and computational complexity test. The performance evaluations indicate the promising potential of camera visions in enabling autonomous UAV taxiing with a 71% successful rate of detecting correct lines to follow and the remaining 29% as background. The proposed system also suggests a high generalization capability of more than a 67% success rate when testing over other paths. The source code of this proposition is open-sourced at https://github.com/DelQuentin/TaxiEye.
Open Access
Cognitive communication scheme for unmanned aerial vehicle operation
(IEEE, 2020-02-17) Xu, Zhengjia; Petrunin, Ivan; Tsourdos, Antonios; Sabyasachi, Mondal; Williamson, Alex
An intelligent and agile wireless communication scheme is a key factor in provision of efficient air-to-ground (A2G) communication for unmanned aerial vehicles (UAVs) operations. For this purpose we review and propose an architecture for aeronautical cognitive communication system (ACCS) that will be providing command, control and communication (C3) link between ground control stations (GCSs) and multiple UAVs utilizing cognitive radio (CR) concept. The factors reviewed and accounted for in the design process are the topology of cognitive detectors, connectivity between cognitive detector and control agency, connection with unmanned traffic management (UTM) system, data link requirements imposed by cognitive scheme, failure notification and recovery, etc. The proposed ACCS is suitable for supporting UAV operations and features a distributed non-communication architecture consisting of GCS network in the ground zone, hybrid data link with the static uplink and the flexible downlink, demonstrating a dynamic nature overall with the frequency handoff scheme generated periodically in accordance with current spectrum environment.
Open Access
Combination and selection of machine learning algorithms in GNSS architecture design for concurrent executions with HIL testing
(IEEE, 2023-11-10) Xu, Zhengjia; Petrunin, Ivan; Tsourdos, Antonios; Grech, Raphael; Peltola, Pekka; Tiwari, Smita
As machine learning (ML) continuing to gain popularity, ML-assisted Global Navigation Satellite System (GNSS) receivers facilitate the performance of Autonomous Systems (AS) navigation solutions. However, selections of ML is often a trade-off in practice where empirical knowledge is taken to alleviate complexities. Therefore, this paper explores decision-making solutions for maximising determined hardware performance under quantitative and qualitative considerations. This work proposes Algorithm Selection and Matching with Fuzzy Analytic Hierarchy Process (ASM-FAHP) that maps multiple trade-off concerns into a Multi-Criteria Decision-Making (MCDM) problem. The ASM-FAHP firstly searches all the possible alternatives to find possible combinations with hardware resource limitations taken into account. Afterwards, ASM-FAHP prioritizes the most significant candidate by constructing a hierarchical structure with several attributes and scoring with fuzzy numbers. Hereby, the most suitable ML combinations are determined by calculating synthesised fuzzy weights per each alternative. The performance of the ML combination is evaluated by concurrently executing it on resource-constrained hardware, specifically the Jetson Nano board. The ML models are trained and tested using high-fidelity synthetic datasets produced from Spirent GSS7000 simulator and SimGen while connected to hardware-in-the-loop (HIL). It has been discovered that when approaching hardware limits, the selected combination of machine learning algorithms makes full use of memory resources but sacrifices processing speed.
Open Access
Consensus-based deep reinforcement learning for mobile robot mapless navigation
(IEEE, 2024-06-05) Liu, Wenxing; Niu, Hanlin; Caliskanelli, Ipek; Xu, Zhengjia; Skilton, Robert
When using mobile robots to perform data collection about the surroundings, the performance might be dissatisfying since the environments could be unknown and challenging. This situation will pose challenges for mobile robot navigation and exploration. To tackle this issue, we propose a consensus-based deep reinforcement learning (DRL) algorithm for multiple robots to perform mapless navigation and exploration. The proposed algorithm leverages both consensus-based training and DRL, which reduces required training steps while maintaining the same training reward. Once trained with fixed obstacles, the proposed training model can demonstrate adaptability in handling real-world random static obstacles and sudden obstacles. The experimental video is available at: at: https://youtu.be/ym2yvbKg4fU.
Open Access
Dynamic spectrum management with network function virtualization for UAV communication
(Springer, 2021-02-03) Xu, Zhengjia; Petrunin, Ivan; Tsourdos, Antonios
Rapid increases in unmanned aerial vehicles (UAVs) applications are attributed to severe spectrum collision issues, especially when UAVs operate in spectrum scarce environments, such as urban areas. Dynamic air-to-ground (A2G) link solutions can mitigate this issue by utilizing programmable communication hardware in the air and real-time assignment of spectrum resources to achieve high-throughput and low-latency connectivity between UAVs and operators. To mitigate the high-computation issue among ground control station (GCS) networks and provide a broad communication coverage for large number of UAVs, we propose an advanced UAV A2G communication solution integrated with the dynamic spectrum management (DSM) and network function virtualization (NFV) technology to serve urban operations. The edge-cutting UAV communication technologies are surveyed. The proposed scheme is discussed in terms of the high-level system architecture, virtual network architecture, specific virtual functions (SVFs), and affiliated operation support databases. Some major research challenges are highlighted and the possible directions of future research are identified.
Open Access
Efficient allocation for downlink multi-channel NOMA systems considering complex constraints
(MDPI, 2021-03-06) Xu, Zhengjia; Petrunin, Ivan; Li, Teng; Tsourdos, Antonios
To enable an efficient dynamic power and channel allocation (DPCA) for users in the downlink multi-channel non-orthogonal multiple access (MC-NOMA) systems, this paper regards the optimization as the combinatorial problem, and proposes three heuristic solutions, i.e., stochastic algorithm, two-stage greedy randomized adaptive search (GRASP), and two-stage stochastic sample greedy (SSD). Additionally, multiple complicated constraints are taken into consideration according to practical scenarios, for instance, the capacity for per sub-channel, power budget for per sub-channel, power budget for users, minimum data rate, and the priority control during the allocation. The effectiveness of the algorithms is compared by demonstration, and the algorithm performance is compared by simulations. Stochastic solution is useful for the overwhelmed sub-channel resources, i.e., spectrum dense environment with less data rate requirement. With small sub-channel number, i.e., spectrum scarce environment, both GRASP and SSD outperform the stochastic algorithm in terms of bigger data rate (achieve more than six times higher data rate) while having a shorter running time. SSD shows benefits with more channels compared with GRASP due to the low computational complexity (saves 66% running time compared with GRASP while maintaining similar data rate outcomes). With a small sub-channel number, GRASP shows a better performance in terms of the average data rate, variance, and time consumption than SSG.
Open Access
Greedy based proactive spectrum handoff scheme for cognitive radio systems
(IEEE, 2019-11-21) Xu, Zhengjia; Ivan, Petrunin; Li, Teng; Tsourdos, Antonios
The aeronautical spectrum becomes increasingly congested due to raising number of non-stationary users, such as unmanned aerial vehicles (UAVs). With the growing demand to spectrum capacity, cognitive radio technology is a promising solution to maximize the utilization of spectrum by enabling communication of secondary users (SUs) without interfering with primary users (PUs). In this paper we formulate and solve a multi-parametric objective function for proactive handoff scheme in multiple input multiple output (MIMO) system constrained by QoS requirements. To improve the efficiency of handoff scheme for multiple communicating UAVs the greedy strategy is adopted. An innovative aspect of our solution includes consideration of quality of service (QoS) components, e.g. opportunistic service time, channel quality, etc. Some of these components, for example collision probability and false alarm probability, affect QoS in a negative way and are considered as constraints. Simulation of handoff scheme has been performed to evaluate the performance of the proposed algorithm in selecting multiple channels when the spectrum environment changes. The performance of handoff scheme is compared with random selection method and is found outperforming the random selection method in terms of averaged utilization ratio. Analysis of results has shown that the spectrum utilization ratio can be doubled by considering wider bandwidth (more channels) and by making QoS requirements less strict. In both cases this leads to near-linear increase in time consumption for handoff scheme generation.
Open Access
High-frequency band automatic mode recognition using deep learning
(IEEE, 2018-12-10) Xu, Zhengjia; Savvaris, Al; Tsourdos, Antonios; Alawadi, Tareq
Communication in High-Frequency (HF) band allows for good-quality, low-cost, and long-distance data-link transmission over diverse landscapes in aerial communication systems. However, as limited frequency resources are allocated, HF band suffers from poor spectrum efficiency when the channel is congested with many users. To maintain the robustness of the data-link transmission, Automatic Link Establishment (ALE) is the worldwide standard for sustaining HF communication of voice, data, instant messaging, internet messaging, and image communications. Technologies, such as spectrum sensing, Dynamic Spectrum Access (DSA) are utilised in ALE with the primary step of automatic mode recognition based on cognitive radio. Conventional methods, such as Automatic Modulation Recognition (AMR) targets at the classification of single modulation, while modern communication systems require recognising multiple modes in combination of various number of tones, tone spacing, and tone interval. In this study, an approach that features filling the gap using deep learning is proposed. By characterising the common in-use mode formats in HF range, investigation shows that spectrogram diagram varies significantly, which necessitates the accurate characterisation and classification of multiple communication modes. Specifically, Convolutional Neural Network (CNN or ConvNet) is adopted for classification. The dataset is collected through USRP N210 with GNU Radio simulation. By reconstructing the communication in selected modes, the mode formats are classified. The performance result of recognition accuracy is displayed with confusion matrix. The confident classification of spectral characteristics, as well as accurate estimation, are established for practical communication scenarios.
Open Access
Identification of communication signals using learning approaches for cognitive radio applications
(IEEE, 2020-07-14) Xu, Zhengjia; Petrunin, Ivan; Tsourdos, Antonios
Signal detection, identification, and characterization are among the major challenges in aerial communication systems. The ability to detect and recognize signals using cognitive technologies is still under active development when addressing uncertainties regarding signal parameters, such as blank spaces available within the transmitted signal and the utilized bandwidth. This paper proposes a learning-based identification framework for heterogeneous signals with orthogonal frequency division multiplexing (OFDM) modulation as generated in a simulated environment at an a priori unknown frequency. The implemented region-based signal identification method utilizes cyclostationary features for robust signal detection. Signal characterization is performed using a purposely-built, lightweight, region-based convolutional neural network (R-CNN). It is shown that the proposed framework is robust in the presence of additive white Gaussian noise (AWGN) and, despite its simplicity, shows better performance compared with conventional popular network architectures, such as GoogLeNet, AlexNet, and VGG 16. The signal characterization performance is validated under two degraded environments that are unknown to the system: Doppler shifted and small-scale fading. High performance is demonstrated under both degraded conditions over a wide range of signal to noise ratios (SNRs) and it is shown that the detection probability for the proposed approach is improved over those for conventional energy detectors. It is found that the signal characterization performance deteriorates under extreme conditions, such as lower SNRs and higher Doppler shifts
Open Access
Integrating GRU with a Kalman filter to enhance visual inertial odometry performance in complex environments
(MDPI, 2023-10-29) Tabassum, Tarafder Elmi; Xu, Zhengjia; Petrunin, Ivan; Rana, Zeeshan A.
To enhance system reliability and mitigate the vulnerabilities of the Global Navigation Satellite Systems (GNSS), it is common to fuse the Inertial Measurement Unit (IMU) and visual sensors with the GNSS receiver in the navigation system design, effectively enabling compensations with absolute positions and reducing data gaps. To address the shortcomings of a traditional Kalman Filter (KF), such as sensor errors, an imperfect non-linear system model, and KF estimation errors, a GRU-aided ESKF architecture is proposed to enhance the positioning performance. This study conducts Failure Mode and Effect Analysis (FMEA) to prioritize and identify the potential faults in the urban environment, facilitating the design of improved fault-tolerant system architecture. The identified primary fault events are data association errors and navigation environment errors during fault conditions of feature mismatch, especially in the presence of multiple failure modes. A hybrid federated navigation system architecture is employed using a Gated Recurrent Unit (GRU) to predict state increments for updating the state vector in the Error Estate Kalman Filter (ESKF) measurement step. The proposed algorithm’s performance is evaluated in a simulation environment in MATLAB under multiple visually degraded conditions. Comparative results provide evidence that the GRU-aided ESKF outperforms standard ESKF and state-of-the-art solutions like VINS-Mono, End-to-End VIO, and Self-Supervised VIO, exhibiting accuracy improvement in complex environments in terms of root mean square errors (RMSEs) and maximum errors.
Open Access
International airline alliance network design with uncertainty
(MDPI, 2021-03-30) Yang, Wendong; Shao, Jiajia; Jiang, Yun; Xu, Zhengjia; Tsourdos, Antonios
This paper addresses the alliance route network design problem considering uncertainty in the unit transportation cost. An alliance route network was constructed based on a hub-and-spoke (HS) network, in which airlines could achieve inter-area passenger transport through their international gateways. The design problem was formulated with a robust model containing a set of uncertain cost parameters. The model was established based on a three-subscript model of an HS network. A case study with real-world data was used to test the proposed model. The results showed that this robust solution can reduce the impact of cost uncertainty
Open Access
Layout analysis of the RCEP international airline network based on hub identification using improved contribution matrix
(Springer, 2024-05-12) Yang, Wendong; Chi, Yulin; Huang, Yining; Wei, Wenbin; Xu, Zhengjia
The signing of the Regional Comprehensive Economic Partnership agreement brings new opportunities for the development of international air transportation. Faced with fierce competition, it is worth studying how hub airports should enhance competitiveness, and how low-cost carriers and full-service carriers should optimize the RCEP international airline network layout for better development. Aiming at providing suggestions for the development of hub airports, low-cost and full-service carriers in the RCEP international airline network, this paper identifies the hub airports, analyzes the layout of the RCEP international airline network, and the multi-layered characteristics based on an improved contribution matrix using data from 2010 to 2019 collected from the Official Airline Guide (OAG). This method comprehensively considers attributes of hub airports and the multi-layered characteristics of the airports and routes. The layout analysis indicates that the RCEP international transportation market presents a more open environment for competition and cooperation where base carriers are often the biggest supporters of hub construction. The multi-layered characteristics analysis reveals that low-cost carriers contribute more towards opening up new RCEP routes than full-service carriers. It is advised that carriers newly entering the RCEP international aviation transportation market and low-cost carriers dedicate to establishing new routes around their hub airports to monopolize this market and enhance their market share, whilst full-service carriers consolidate existing routes and increase route density to achieve economic benefits.
Open Access
Learning based spectrum hole detection for cognitive radio communication
(IEEE, 2020-04-30) Xu, Zhengjia; Petrunin, Ivan; Tsourdos, Antonios; Ayub, Shahid
This paper proposes a novel learning based (LB) solution for detection and quantification of spectrum holes in periodic communications of unmanned aerial vehicles (UAVs), Instead of hypothesis testing after implementation of spectrum sensing methods, the implemented LB solution based on spectral correlation function (SCF) uses region convolutional neural network (R-CNN) for extracting quantitative parameters of the spectrum holes. The proposed LB approach is implemented using GoogLeNet architecture for the wide band detection in the scenario of orthogonal frequency division multiplexing (OFDM) communication system with the additive white Gaussian noise (AWGN) channel model. The simulation of single input single output (SISO) communication system with spectrum holes is presented. Examples of wide band detection results for both SISO and multiple input multiple output (MIMO) systems are shown and the proposed LB detector is found to be fairly accurate in identification of spectrum holes. By analyzing the training performance, the GoogLeNet architecture, along with its hyperparameter configurations and training dataset is validated. We also demonstrated that our LB detector is resilient to the AWGN environment by analyzing the precision and recall curves, average precision and mean relative error (MRE) versus signal noise ratio (SNR).
Open Access
Long-term network structure evolution investigation for sustainability improvement: an empirical analysis on global top full-service carriers
(MDPI, 2024-01-31) Yang, wendong; Jiang, Yun; Chi, Yulin; Xu, Zhengjia; Wei, Wenbin
The continuous and strategic planning of full-service carriers plays a prominent role in transferring and adapting them into resilient full-service carrier network structures. The exploration of full-service carrier network structures using the latest long-term empirical data facilitates enhancing cognitive capabilities in aspects of identifying network development tendencies, readjusting network structures, and supporting determinations of strategic business routes. Aiming at providing sustainable transport network solutions with historical long-term network structure analysis, this paper researches the global top 10 full-service carriers’ air transport networks from 2007 to 2022, applied using social network analysis (SNA). The static metrics from local to path-based perspectives are adopted to explore the global network evolution trend, along with competitiveness characteristics over critical airports. The cascading failure model is applied as a key indicator to analyze the dynamic robustness capability for the network. The similarity changing feature among the selected networks over the past years from 2007 to 2022 is measured using the autocorrelation function (ACF). The results indicate that, from 2011 to 2019, the majority of full-service carrier networks belong to the network types of closed, structural symmetry and two-way transitivity. The critical airports in North America present superiority in terms of network efficiency over those in Europe, Asia, and Oceania. The 10 full-service carriers’ air transport networks all show the trend of being more destruction-resistant. During the COVID-19 pandemic period, the merger with other airlines and the signing of a joint venture agreement led to higher temporal variability in the network structure.
Open Access
Modeling and performance analysis of opportunistic link selection for UAV communication
(MDPI, 2021-01-13) Xu, Zhengjia; Petrunin, Ivan; Tsourdos, Antonios
In anticipation of wide implementation of 5G technologies, the scarcity of spectrum resources for the unmanned aerial vehicles (UAVs) communication remains one of the major challenges in arranging safe drone operations. Dynamic spectrum management among multiple UAVs as a tool that is able to address this issue, requires integrated solutions with considerations of heterogeneous link types and support of the multi-UAV operations. This paper proposes a synthesized resource allocation and opportunistic link selection (RA-OLS) scheme for the air-to-ground (A2G) UAV communication with dynamic link selections. The link opportunities using link hopping sequences (LHSs) are allocated in the GCSs for alleviating the internal collisions within the UAV network, offloading the on-board computations in the spectrum processing function, and avoiding the contention in the air. In this context, exclusive technical solutions are proposed to form the prototype system. A sub-optimal allocation method based on the greedy algorithm is presented for addressing the resource allocation problem. A mathematical model of the RA-OLS throughput with above propositions is formulated for the spectrum dense and scarce environments. An interference factor is introduced to measure the protection effects on the primary users. The proposed throughput model approximates the simulated communication under requirements of small errors in the spectrum dense environment and the spectrum scarce environment, where the sensitivity analysis is implemented. The proposed RA-OLS outperforms the static communication scheme in terms of the utilization rate by over 50% in case when multiple links are available. It also enables the collaborative communication when the spectral resources are in scarcity. The impacts from diverse parameters on the RA-OLS communication performance are analyzed.
Open Access
Selection and aggregation of low-cost particle sensors for outdoor particulate matter measurement
(IEEE, 2024-06-28) Li, Jie; Nasar, Zaheer; Ferracci, Valerio; Harris, Neil; Xu, Zhengjia
A growing number of low-cost sensors (LCS) have been used to monitor air pollution in outdoor air. The benefit of utilizing LCS lies in its ability to offer increased spatial coverage, which provides real-time measurements at a reduced cost. The selection and combination of low-cost sensors represent the primary challenge in conducting observations using such sensors. This paper employs a sensor quality ranking strategy, utilizing random forest (RF) for aggregating the selected LCS combination, followed by evaluating the correction results using various model evaluation metrics. The LCS used in this study, regardless of their quality grades, achieves a coefficient of determination of 0.93 or higher after model calibration, indicating the effectiveness of employing RF for aggregation. It is found that using a pair of top and averaged LCS can significantly enhance the measurement quality by 25% in RMSE. Using RF to calibrate a single LCS increases the measurement performance at least two times in terms of MSE, RMSE, and MAE. Using paired LCS with RF aggregation for measuring PM2.5, the aggregated observation significantly approximates the reference measurement with R2=0.986.

Browsing by Author "Xu, Zhengjia"

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