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In this paper, analysis is conducted on the risk assessment regarding the vertical collision of CSPR (Closely Spaced Parallel Runways) paired-approach, to ensure flight safety. A vertical kinematics equation is established with analysis of CSPR paired approach and starting from the preconditions that the proceeding aircraft altitude is lower than that of the following aircraft during paired approach: the time consuming of passing initial safety separation by the proceeding aircraft decelerated less or greater than that of the proceeding aircraft with uniform speed. Based on the two conditions, its corresponding risk-evaluation model is established, proceeding from the aircraft ADS-B data and the analysis on the relation between aircraft position error and altitude maintain ability, relevant model parameters specified. Conclusion has been achieved on risk assessment that implementing vertical collision risk of paired approach has little to do with aircraft type and initial longitudinal separation, but has more correlation with initial vertical interval and aircraft altitude maintain ability; rules of at least 180-m vertical interval and altitude error not exceeding 40.77 m (within 95% flight time) must be obeyed when paired approach applied.

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.

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.