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In this paper, the chaotic characteristics of air traffic flow are studied, ADS-B data easily available to ground aviation users are selected as the basic data of traffic flow, and a high-dimensional prediction model of air traffic flow time series based on the non-iterative PSR-ELM algorithm is established. The prediction results of the proposed algorithm are then compared with those of the SVR algorithm, which requires iteration. Moreover, airspace operation data before and after the outbreak of the COVID-19 epidemic are selected as the experimental scene, and the prediction effects of time series with different degrees of chaos are comparatively analyzed. The experimental results reveal that the PSR-ELM algorithm achieves fast and accurate results, and, when the traffic flow state is sparse, the degree of chaos is reduced and the prediction effect is improved. The findings of this research provide a reference for air traffic flow theory.

Understanding the chaos of air traffic flow is significant to the achievement of advanced air traffic management, and trajectory data are the basic material for studying the chaotic characteristics. However, at present, there are two main obstacles to this task, namely, large amounts of noise in the measured data and the tedium of existing data processing methods. This paper improves the incorrect trajectory processing method based on ADS-B trajectory data and proposes a method by which to quickly extract the traffic flow through a certain waypoint. Currently, the commonly used theoretical analysis tools for nonlinear complex systems include the classical nonlinear dynamics analysis method and the newly developed complex network-based analysis method. The latter is currently in an exploratory stage because it has just been introduced into the study of air traffic flow. From these two perspectives, the chaotic characteristics of air traffic flow are studied in the present work. From the perspective of nonlinear dynamics, the improved C-C method is used to calculate the reliability parameters, namely, the time delay τ and embedding dimension m, of phase-space reconstruction, and the maximum Lyapunov index is calculated by using the small data volume method to prove the existence of chaos in the system. From the perspective of complex networks, the construction of a visibility graph and horizontal visibility graph is used to prove the existence of chaos in the system, and the goodness-of-fit parameters of the degree distributions of two fitting methods under different time scales are evaluated, which provides support for the air traffic flow theory.

The development of civil aviation has led to flight operations generating massive datasets. Derived from Automatic Dependent Surveillance-Broadcast technology, an eigengap-based automatic hierarchical clustering algorithm is proposed, aiming to overcome the human intervention requirement of hierarchical clustering to determine the number of track clusters in the terminal area. First, the trajectory pair similarity matrix is calculated based on the Euclidean distance and an adaptive scale parameter. Second, the Laplace transformation is applied on the similarity matrix and the eigenvalues are obtained in order to determine the number of clusters. Finally, the hierarchical clustering algorithm divides the terminal area trajectory into several sub-categories. Taking a terminal approach trajectory as an example, simulation analysis is performed, with results revealing that the algorithm divides 404 north-south-oriented aircraft flight trajectories into 2 and 3 categories. Furthermore, the new-index is used to evaluate the clustering results, demonstrating its effectiveness is better than the automatic k-means algorithm. Our study provides support for reliability analysis of air traffic network in the terminal area.

Fractal theory is a new scientific method and theory that can describe the complexity and irregularity of nature. Aiming at the problem that the frequency modulation slope obtained by the traditional linear frequency modulation (LFM) signal parameter estimation algorithm has high complexity with poor real-time performance and small SNR adaptation range, the LFM signal frequency modulation slope estimation method based on the fractal-box dimension is proposed. The proposed method is utilized to evaluate the frequency modulation slope of the LFM signal, and the affect of signal amplitude and phase on the fractal-box dimension of the signal is taken into account. The estimation error at different SNRs is analyzed, and the relationship graph of the pulse width, FM bandwidth and fractal-box dimension is drawn. The simulation results demonstrate that the proposed method can accurately estimate the parameters of LFM signals under varying SNR environment. Compared with the traditional linear frequency modulation (LFM) signal parameter estimation algorithms, the anti-noise performance of the proposed algorithm is stronger, and the proposed algorithm is relatively simple and has good application value.

To achieve flight risk identification and assessment in the new field of UAVs, two index systems of risk prediction and safety evaluation are constructed in this work by categorizing the set of UAV flight risks, and the identification subset is set according to the UAV characteristics and use. Risk identification elements are set for the identification subset, and the qualitative attributes of risk identification elements are quantified by set-valued statistics. A general quantitative model for the flight risk identification of UAVs is established. According to the different flight risk factors, UAV flight risks are categorized, the fuzzy comprehensive evaluation method is adopted, and case data are used to quantitatively evaluate different types of risks. Finally, the application of the techno-economic evaluation method to the feasibility evaluation of the UAV flight risk prevention scheme is proposed to provide support for UAV flight risk management.

To scientifically evaluate the reliability of air traffic networks, a definition of air traffic network reliability is proposed in this paper. Calculation models of the connectivity reliability, travel-time reliability, and capacity reliability of the air traffic network are constructed based on collected historical data, considering the current status and the predicted future evolution trends. Considering the randomness and fuzziness of factors affecting reliability, a comprehensive evaluation model of air traffic networks based on the uncertainty transformation model is established. Finally, the reliability of the US air traffic network is analyzed based on data published by the Transportation Statistics Bureau of the US Department of Transportation. The results show that the connectivity reliability is 0.4073, the capacity reliability is 0.8300, the travel-time reliability is 0.9180, and the overall reliability evaluated is &ldquo, relatively reliable&rdquo, This indicates that although the US structural reliability is relatively low, the US air traffic management is very efficient, and the overall reliability is strong. The reliability in nonpeak hours is much higher than that in peak hours. The method can identify air traffic network reliability efficiently. The main factors affecting reliability can be found in the calculation process, and are beneficial for air traffic planning and management. The empirical analysis also reflects that the evaluation model based on the uncertainty transformation model can transform the quantitative data of network structure and traffic function into the qualitative language of reliability.

Flight operation data provides a record of air traffic behaviour in detail, and has become an important resource for studying air traffic patterns. In this paper, the degree characteristic of air traffic networks is studied using flight operation data in order to explain air traffic behaviour. The degree indexes were founded from the perspective of network structure and network function based on complex network theory and traffic flow theory. The connectivity degree, traffic degree and delay degree were established, and the characteristics of these three degrees were studied based on American flight data from different days in 2015 over different time periods of each day. Research shows that at all scales the number degrees, volume degrees and duration degrees are all of power-law distribution, and the power exponents are different depending on the scale, date and index. The power exponents fluctuate from 0.5 to 1.5. In general, the connectivity degree exponent is greater than that of the traffic degree and the delay degree. The duration degree exponent is greater than the traffic degree. The exponents are greater on holidays than they are on non-holidays, as well as during non-working hours than during working hours of the day. Compared with non-holidays, the average connectivity degree and delay degree are larger on holidays, and the average traffic degree is smaller. This reveals that most airports only provide flights to one airport, and the airports’ numbers and flight volumes both decrease on holidays. The power indexes of the delay degree and traffic degree are basically the same. The main mode of traffic (arrivals or departures) during each time period is also studied.

iWith the rapid development and the increasing security threats of D2D communication, the D2D communication security has become more and more important. Device recognition is the foundation of communication security. Traditional device authentication is the cryptographic mechanism and the security protocol that are not enough for the new security threat. RF fingerprint of wireless device has physical characteristics, which is difficult to clone. Therefore, it can be used for D2D device recognition. In this paper, a recognition algorithm of D2D device based on RF fingerprint is proposed. First, Hilbert transform and principal component analysis is used to generate the RF fingerprint of D2D device. Second, support vector machine (SVM) is introduced and CV-SVM is used as the classifier. Finally, simulation results show that the recognition rate is more than 90% under SNR = 5 dB, which proves that the proposed method is effective for D2D device recognition. Therefore, it can be used as the trust management of D2D communication.

Automated modulation classification plays a very important part in cognitive radio networks. Deep learning is also a powerful tool that we could not overlook its potential in addressing signal modulation recognition problem. In our last work, we propose a new data conversion algorithm in order to gain a better classification accuracy of communication signal modulation, but we still believe that the convolution neural network (CNN) can work better. However, its application to signal modulation recognition is often hampered by insufficient data and overfitting. Here, we propose a smart approach to programmatic data augmentation method by using the auxiliary classifier generative adversarial networks (ACGANs). The famous CNN model, AlexNet, has been utilized to be the classifier and ACGAN to be the generator, which will enlarge our data set. In order to alleviate the common issues in the traditional generative adversarial nets training, such as discriminator overfitting, generator disconverge, and mode collapse, we apply several training tricks in our training. With the result on original data set as our baseline, we will evaluate our result on enlarged data set to validate the ACGAN’s performance. The result shows that we can gain 0.1~6% increase in the classification accuracy in the ACGAN-based data set.

The growing need of responsiveness for enterprises facing market volatility raises a strong demand for flexibility in their human resource management. This paper presents a multi-objective model for Multi-skill Project Scheduling Problem. We propose a new wage distribution method in which different perform efficiencies of human resources are taken into account. The model aims at minimizing project duration and project costs concurrently. An improved NSGA-II algorithm is designed to solve the model. The algorithm introduces a multi-dimensional chromosome coding scheme to identify the priorities and staff allocation of each activity. Special chromosome crossover and mutation operation are employed to address resource conflicts and constraint violations. Eventually, A case study is presented to verify the efficiency of the proposed approach.

This paper aims to provide an insight into the roles of the different types of airports in China by improved K-means clustering algorithm. The first part of the work analyzed the characteristics of Chinese airline network and pointed out that the key to construct hub-and-spoke airline network is determining the function of each airport. The index system of airport function orientation was established from airport operation index, airport hinterland index and airport growth index. The airports in China were classified into four classes by the K-means clustering algorithm. In order to improve reliability of clustering algorithm, a formula was used to normalize the value of each index, and the airports were clustered by improved K-means clustering algorithm. The algorithm was simulated by the MATLAB and the clustered results show the airports have obvious hierarchy.

To overcome the traditional disadvantages of single source points detection methods in underdetermined blind source separation problem, this paper proposes a novel algorithm to detect single source points for the linear instantaneous mixed model. First, the algorithm utilizes a certain relationship between the time-frequency coefficients and the complex conjugate factors of the observation signal to realize single source points detection. Then, the algorithm finds more time-frequency points that meets the requirements automatically and cluster them by utilizing a clustering algorithm based on the improved potential function. Finally, the estimation of the mixed matrix is achieved by clustering the re-selected single source points. Simulation experiments on linear mixture model demonstrates the efficiency and feasibility for estimating the mixing matrix.

1090ES ADS-B is a new technology in civil aviation, mainly used to monitor dynamic condition of aircrafts and share the flight information. However, the ADS-B signals are often interfered with other overlapped signals during the signal transmission, causing signal overlap and bringing difficulties to signal processing afterwards. This article applies Blind Source Separation (BSS) into the separation process of ADS-B overlapped signals and constructs the ADS-B overlapped signal separation model using Infomax algorithm, in order to separate ADS-B overlapped signals into single-way ADS-B signals and improve signal decoding rate.

With the popularization of self-driving cars and the rapid development of intelligent transportation, pedestrian detection shows more and more extensive application scenarios in daily life, which have higher and higher application values. It also raises more and more interest from academic community. Pedestrian detection is fundamental in many human-oriented tasks, including trajectory tracking of people, recognition of pedestrian gait, and autopilot recognition of pedestrians to take appropriate response measures. In this context, this paper studies the design and implementation of a pedestrian detection system. The pedestrian detection system of this article is mainly composed of two parts. The first part is a pedestrian detector based on deep learning, and the second part is a graphical interface that interacts with the user. The former part mainly uses the Faster R-RCNN learning model, which can use convolutional neural networks to learn features from the data and extract the features of the image. It can also search the image through RPN network for areas where the target is located and then classify them. In this paper, a complete pedestrian detection system is implemented on the basis of deep learning framework Caffe. Experiments show that the system has high recognition rate and fast recognition speed in real world.

Insights into the temporal-spatial characteristics of air traffic flow is the prerequisite of air traffic flow modeling, prediction and optimization. The intrinsic dynamics of air traffic flow were analyzed according to radar track data by classical nonlinear analysis and complex network methods. The nonlinear dynamic characteristics of air traffic flow were revealed from multiple perspectives by means of phase space reconstruction, recurrence plot, Lyapunov exponent, correlation dimension and Hurst exponent. The experimental results show that the example traffic flow became the most chaotic and the state persistence was poorest at time scale of 30 min. Besides, this paper explored the application of a complex network to analyze the nonlinearity of air traffic flow, and verified the chaotic and fractal characteristics by analyzing the degree distribution of the converted network. The degree distribution of the network derived from the traffic flow time series was approximately fitted to the exponential function and featured a strength level of chaos that was positively correlated with the size of network degree distribution power exponent. The linear relationship between network degree distribution power exponent and Hurst exponent was revealed, and the nonlinearity of air route traffic flow was systematically characterized. Particularly, the complex network-based analysis method helped examine air traffic flow from a new perspective, enriching the research means. These novel findings will provide theoretical basis for aggregated air traffic flow modeling, decision support system design and tactical flow management.

In this paper, information entropy and ensemble learning based signal recognition theory and algorithms have been proposed. We have extracted 16 kinds of entropy features out of 9 types of modulated signals. The types of information entropy used are numerous, including Renyi entropy and energy entropy based on S Transform and Generalized S Transform. We have used three feature selection algorithms, including sequence forward selection (SFS), sequence forward floating selection (SFFS) and RELIEF-F to select the optimal feature subset from 16 entropy features. We use five classifiers, including k-nearest neighbor (KNN), support vector machine (SVM), Adaboost, Gradient Boosting Decision Tree (GBDT) and eXtreme Gradient Boosting (XGBoost) to classify the original feature set and the feature subsets selected by different feature selection algorithms. The simulation results show that the feature subsets selected by SFS and SFFS algorithms are the best, with a 48% increase in recognition rate over the original feature set when using KNN classifier and a 34% increase when using SVM classifier. For the other three classifiers, the original feature set can achieve the best recognition performance. The XGBoost classifier has the best recognition performance, the overall recognition rate is 97.74% and the recognition rate can reach 82% when the signal to noise ratio (SNR) is −10 dB.

In order to study the applicability of data mining in the study of ATC operational safety, take the six typical factors that may affect the safety of ATC as the former, and the level of unsafe incidents in ATC as the next term, use correlation analysis and Apriori algorithm, And set a reasonable degree of confidence in the rules, the degree of support for the rules, analysis of air traffic insecurity incidents. Taking the general ATC operational safety incident as an example, the results show that the data mining has applicability in the problem of ATC operational safety, and each of the influencing factors has a certain relevance; Each of the preceding factors has an impact on the safety of ATC operations, but the degree of impact is different. Among them, the factors that have a greater impact are mainly control load, airspace environment and control equipment.