Greedy based proactive spectrum handoff scheme for cognitive radio systems

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

Novel in-service combustion instability detection using the chirp fourier higher order spectra

Combustion instabilities, known as “rumble” and “screech” are the self-excited aerodynamic instabilities in the gas turbine combustor. They cause the premature failures of the gas turbine components, and, consequently, the failure of the gas turbine as a whole. Because of the complex physical effects underlying the rumble and the screech phenomena, it is difficult to eliminate them completely at the design stage. Therefore, special attention should be paid to the detection of the combustion instabilities in the gas turbine in order to prevent its prolonged operation in this mode. There are known techniques, which are able to detect the rumble and the screech in gas turbines. Most of them do not consider the combustion instabilities as non-linear and non-stationary events and, therefore, have lower detection efficiency. Novel technique for in-service combustion instability detection is implemented in this paper. This technique overcomes the limitations of the existing solutions

Novel health monitoring technology for in‐service diagnostics of intake separation in aircraft engines

Diagnostics and elimination of airflow separation effects draw essential attention of researchers in the areas of energy generation, civil engineering, and aerospace due to unwanted and harmful interaction of separated airflow with different structures. In aviation, distortion of the intake airflows of an aircraft engine, known as intake separation, not only reduces the efficiency of the engine due to decrease in air intake but also interacts with engine structural components, for example, blades, significantly increasing their vibration. This leads to fatigue and subsequent accelerated failure of these components. Therefore, health monitoring and diagnostics of the intake separation effects using structural health monitoring (SHM) framework are of high importance for ensuring both optimal engine performance and its safe operation. In the present paper, a novel health monitoring technology based on advanced signal processing, the integrated higher order spectral technique, is applied for the first time in worldwide terms for in‐service intake separation diagnostics in aircraft engine using casing vibration data

Modeling and performance analysis of opportunistic link selection for UAV communication

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

Assuring safe and efficient operation of UAV using explainable machine learning

The accurate estimation of airspace capacity in unmanned traffic management (UTM) operations is critical for a safe, efficient, and equitable allocation of airspace system resources. While conventional approaches for assessing airspace complexity certainly exist, these methods fail to capture true airspace capacity, since they fail to address several important variables (such as weather). Meanwhile, existing AI-based decision-support systems evince opacity and inexplicability, and this restricts their practical application. With these challenges in mind, the authors propose a tailored solution to the needs of demand and capacity management (DCM) services. This solution, by deploying a synthesized fuzzy rule-based model and deep learning will address the trade-off between explicability and performance. In doing so, it will generate an intelligent system that will be explicable and reasonably comprehensible. The results show that this advisory system will be able to indicate the most appropriate regions for unmanned aerial vehicle (UAVs) operation, and it will also increase UTM airspace availability by more than 23%. Moreover, the proposed system demonstrates a maximum capacity gain of 65% and a minimum safety gain of 35%, while possessing an explainability attribute of 70%. This will assist UTM authorities through more effective airspace capacity estimation and the formulation of new operational regulations and performance requirements

Strategic conflict management using recurrent multi-agent reinforcement learning for urban air mobility operations considering uncertainties

The rapidly evolving urban air mobility (UAM) develops the heavy demand for public air transport tasks and poses great challenges to safe and efficient operation in low-altitude urban airspace. In this paper, the operation conflict is managed in the strategic phase with multi-agent reinforcement learning (MARL) in dynamic environments. To enable efficient operation, the aircraft flight performance is integrated into the process of multi-resolution airspace design, trajectory generation, conflict management, and MARL learning. The demand and capacity balancing (DCB) issue, separation conflict, and block unavailability introduced by wind turbulence are resolved by the proposed the multi-agent asynchronous advantage actor-critic (MAA3C) framework, in which the recurrent actor-critic networks allow the automatic action selection between ground delay, speed adjustment, and flight cancellation. The learned parameters in MAA3C are replaced with random values to compare the performance of trained models. Simulated training and test experiments performed on a small urban prototype and various combined use cases suggest the superiority of the MAA3C solution in resolving conflicts with complicated wind fields. And the generalization, scalability, and stability of the model are also demonstrated while applying the model to complex environments

Identification of communication signals using learning approaches for cognitive radio applications

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 shift

Traffic flow prediction for UTM application: a deep learning approach

Over the past few years, the research community has focused greatly on predicting air traffic flows, yielding remarkable outcomes. We found that existing literature in the field mainly covers prediction of air traffic flows for conventional aircraft. However, there is limited research about prediction of air traffic flows for Uncrewed Aircraft Traffic Management (UTM). This research study proposes a deep learning-based approach to predict air traffic congestion in the context of UTM over a period of three minutes. The use of the model aims to address congestion considering air traffic uncertainties instead of addressing the conventional issues of trajectory prediction or conflict detection and resolution. Our model also considers the influence of recreational users who fly UAVs at random times, during the execution of the above essential missions. Further, the effects of airspace structure configurations like static No-Fly Zones (NFZ), airfields with variable availability for drone flights, recreational areas, emergency UTM operation and environmental factors such as weather conditions have also been studied. The proposed model shows better performance compared to other approaches such as the Shallow neural networks and regression models

Experimental evaluation of GNSS and IMU fusion using gated recurrent unit

In this paper, a data-driven Inertial navigation systems (INS) and Global Navigation Satellite System (GNSS) fusion algorithm based on the use of the Gated Recur-rent Unit (GRU) is proposed. In this project, we trained the GRU neural network with Inertial Measurement Unit (IMU) raw data and GNSS Position, Velocity and Timing (PVT) solutions as input and the position difference between GNSS and ground truth as labels. Therefore, the trained model can estimate the rover’s positions by subtracting the predicted GNSS error from GNSS positions given IMU raw measurements and GNSS PVT solutions. To evaluate the performance of GNSS/INS fusion algorithms in realistic scenarios, we developed an experimental platform. Our experimental platform consists of a moving test rig and an external validation system. The moving test rig consists of a rover equipped with an LPMS-CU2: 9-Axis Inertial Measurement Unit (IMU) and U-Blox ZED-F9P GNSS receiver. For validation purposes, we employ an onboard real-time kinematic positioning (RTK)-GNSS receiver. The test scenarios include both open-sky and challenging conditions near buildings, which is beneficial for devolving and testing urban navigation systems. After training with collected experimental data in multiple test scenarios, the proposed algorithm is able to improve GNSS positioning accuracy by more than 60% for the open-sky environment and 30% for the urban environment

Health condition estimation of bearings with multiple faults by a composite learning-based approach

Bearings are critical components found in most rotating machinery; their health condition is of immense importance to many industries. The varied conditions and environments in which bearings operate make them prone to single and multiple faults. Widespread interest in the improvements of single fault diagnosis meant limited attention was spent on multiple fault diagnosis. However, multiple fault diagnosis poses extra challenges due to the submergence of the weak fault by the strong fault, presence of non-Gaussian noise, coupling of the frequency components, etc. A number of existing convolutional neural network models operate on a distinct feature that is not enough to assure reliable results in the presence of these challenges. In this paper, extended feature sets in three homogenous deep learning models are used for multiple fault diagnosis. This ensures a measure of diversity is introduced to the health management dataset to obtain complementary solutions from the models. The outputs of the models are fused through blending ensemble learning. Experiments using vibration datasets based on bearing multiple faults show an accuracy of 98.54%, with an improvement of 2.74% in the overall effectiveness over the single models. Compared with other technologies, the results show that this approach provides an improved generalized diagnostic capability