A human-in-the-loop simulation of an integrated set of time-based automation tools that provided precision scheduling, sequencing and ground-based merging and spacing functions was run in the fall of 2010. These functions were combined into the Terminal Area Precision Scheduling and Spacing (TAPSS) system. TAPSS consists of a scheduler and two suites of advisory tools, one for the Air Route Traffic Control Center (ARTCC, or Center) and one for Terminal Radar Approach Control (TRACON) operations. Both suites are designed to achieve maximum throughput and controllability of traffic. The subject airspace was the terminal area around Los Angeles airport (LAX) and the en route space immediately beyond. Scenario traffic was based on the demand from today's heavy arrival periods, and traffic levels were simulated that matched these or added five, ten or twenty percent to this amount. Eight retired, highly experienced controllers worked two final, three feeder and three en-route positions to deliver traffic to the two outboard arrival runways at LAX (24R and 25L). Although the main research question was whether controllers could safely control the traffic, their level of performance was also of interest and how the advanced tools facilitated or hindered their tasks. The results show that the TAPSS tools enabled higher airport throughput and a larger number of continuous descent operations from cruise to touchdown for the jet aircraft in the scenarios. This contrasts sharply with the "current day" operations in which the Center controllers utilize step-down descents to meter the aircraft. Reported workload levels were lower in the "TAPSS tools" condition than in the "current-day" condition and the TAPSS operations earned cautiously acceptable ratings, indicating the prototype tools have value.
In wireless mobile location systems, NLOS problem has been considered as the key issue that affects the estimation accuracy. In this paper, mobile location in a mixed line-of-sight/non-line-of-sight (LOS/NLOS) condition is considered, and a NLOS error mitigation technique is proposed, which utilizes unscented Kalman filter (UKF) to jointly estimate mobile state and the hidden sight state based on the data collected by each BS. In addition, data fusion method is further applied to achieve high estimation accuracy. Simulation results demonstrate that the performance of the proposed method meet the requirement of the FCC E911 in different LOS/NLOS conditions.
Presents a collection of slides covering the following topics: terminal area precision scheduling and spacing system; TAPSSS; TRACON tools; air traffic control; aircraft control and airspace simulation.
Based on the hypothesis of the Manhattan world, we propose a tightly-coupled monocular visual-inertial odometry (VIO) system that combines structural features with point features and can run on a mobile phone in real-time. The back-end optimization is based on the sliding window method to improve computing efficiency. As the Manhattan world is abundant in the man-made environment, this regular world can use structural features to encode the orthogonality and parallelism concealed in the building to eliminate the accumulated rotation error. We define a structural feature as an orthogonal basis composed of three orthogonal vanishing points in the Manhattan world. Meanwhile, to extract structural features in real-time on the mobile phone, we propose a fast structural feature extraction method based on the known vertical dominant direction. Our experiments on the public datasets and self-collected dataset show that our system is superior to most existing open-source systems, especially in the situations where the images are texture-less, dark, and blurry.
In the unmanned aerial vehicle (UAV) swarm combat system, multiple UAVs’ collaborative operations can solve the bottleneck of the limited capability of a single UAV when they carry out complicated missions in complex combat scenarios. As one of the critical technologies of UAV collaborative operation, the mobility model is the basic infrastructure that plays an important role for UAV networking, routing, and task scheduling, especially in high dynamic and real-time scenarios. Focused on real-time guarantee and complex mission cooperative execution, a multilevel reference node mobility model based on the reference node strategy, namely, the ML-RNGM model, is proposed. In this model, the task decomposition and task correlation of UAV cluster execution are realized by using the multilayer task scheduling model. Based on the gravity model of spatial interaction and the correlation between tasks, the reference node selection algorithm is proposed to select the appropriate reference node in the process of node movement. This model can improve the real-time performance of individual tasks and the overall mission group carried out by UAVs. Meanwhile, this model can enhance the connectivity between UAVs when they are performing the same mission group. Finally, OMNeT++ is used to simulate the ML-RNGM model with three experiments, including the different number of nodes and clusters. Within the three experiments, the ML-RNGM model is compared with the random class mobility model, the reference class mobility model, and the associated class mobility model for the network connectivity rate, the average end-to-end delay, and the overhead caused by algorithms. The experimental results show that the ML-RNGM model achieves an obvious improvement in network connectivity and real-time performance for missions and tasks.
Electricity bill recovery risk is a difficult problem for power grid enterprises to operate. This paper comprehensively considers the impact of industry development on electricity customers under the current economic situation and the basic attributes of users, consumption behavior, payment behavior, etc., and proposes an electricity bill recovery risk identification model based on industry development trend to give early warning to users with recovery risks. First, the industry development trend prediction model is constructed to obtain the industry development situation awareness results. Then, the characteristic system is constructed by combining the basic attributes of users, the data of electricity consumption and payment behavior. On this basis, the classification regression algorithm is used to construct the electricity rate recovery risk early warning model, predict the risk probability of electricity bill recovery. Through the example verification, the model has good forecasting ability and has practical and expanding significance.
This paper studies indoor Bluetooth network. wireless positioning using a network of Bluetooth signals. Fingerprints of received signal strength indicators are used for localization. Due to the relatively long interval between the available consecutive Bluetooth signal strength measurements, we propose a method of information filtering with speed detection, which combines the estimation information from the received signal strength (RSS) measurements with the prior information from the motion model. Speed detection is further assisted to correct the outliers of position estimation. The field tests show that the new algorithm proposed applying information filter with speed detection improves the horizontal positioning accuracy of indoor navigation with about 17% compared to the static fingerprinting positioning method, achieving a 4.2 m positioning accuracy on the average, and about 16% improvement compared to the point Kalman filter.
Indoor localization with high accuracy plays a key role in the field of Internet of Things in 5th-Generation (5G) era. With the introduction of Multiple Input Multiple Output (MIMO) technology in 5G, the direction-of-arrival (DOA) method is highly feasible in indoor localization. However, the direction of arrival is susceptible to complex indoor environment. To improve the accuracy and stability of DOA estimation, an adjacent angle power difference (AAPD) method is proposed based on Orthogonal Matching Pursuit (OMP). This method uses OMP to obtain an initial estimate of the direction, and then, adjusts the estimation by calculating the difference power of adjacent points at initial value point to get the fractional DOA. In the scenario of continuous movement, beamforming is further applied, which reduces the amount of calculation. Both simulation and experimental results show that the proposed method can achieve high accuracy and eliminate the error jitter. Compared with the classical Multiple Signal Classification (MUSIC) method for DOA estimation, the proposed method can increase accuracy by 46% under the condition of low SNR (Signal-to-Noise Ratio). The probability that the measurement error does not exceed 5° in the actual movement tests is 97.5%.
Abstract The fifth-generation (5G) network has been deployed for a vast number of users. The advanced capabilities of 5G technology have opened up opportunities for accuracy positioning and navigation. However, when it comes to indoor positioning using commercial 5G signals, there are persistent challenges. One particular challenge arises from the fact that in numerous indoor scenarios, there is only one base station (called gNB) heard from the receiver. This limitation makes the traditional geometric methods difficult to be applied indoors for 5G positioning. To solve the problem of indoor positioning with single 5G gNB, we propose a fingerprinting method based on the multi-beam of 5G downlink signals. This method utilizes the multi-beam Channel State Information (CSI) and employs an Extreme Learning Machine (ELM) for dimensionality reduction, aiming to improve both the accuracy and efficiency of indoor positioning. To assess the effectiveness of this method, field tests were conducted in indoor scenarios. The results demonstrate that, by taking the advantages of multi-beam property in 5G signals, it is able to achieve CSI positioning in single-gNB scenario, and the positioning accuracy over 94% while improving the positioning efficiency.
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