276 results found
Ali BS, Ochieng WY, Zainudin R, 2017, An analysis and model for Automatic Dependent Surveillance Broadcast (ADS-B) continuity, GPS SOLUTIONS, Vol: 21, Pages: 1841-1854, ISSN: 1080-5370
Ali BS, Schuster W, Ochieng WY, 2017, Evaluation of the Capability of Automatic Dependent Surveillance Broadcast to Meet the Requirements of Future Airborne Surveillance Applications, Journal of Navigation, Vol: 70, Pages: 49-66, ISSN: 0373-4633
© 2016 The Royal Institute of Navigation. Automatic Dependent Surveillance Broadcast (ADS-B) Out supports various ground applications including Air Traffic Control (ATC) surveillance in radar airspace, non-radar airspace and on the airport surface. In addition, the capability of aircraft to receive ADS-B Out messages from other aircraft within their coverage (ADS-B In) enables enhanced airborne surveillance applications. The requirements of the application vary depending on its safety-criticality. More stringent applications will require higher levels of performance. It is therefore critical that the ADS-B system performance is measured against the most stringent application it is designed for. This paper reviews the various enhanced airborne surveillance applications and the required ADS-B information to support them. It identifies the ADS-B based applications required for Air Traffic Management (ATM) modernisation under the SESAR/NextGen programs. It discusses existing ADS-B Out versions and their capabilities. A mapping exercise is undertaken to assess the credibility of the ADS-B system performance to support the functionalities and requirements of the various enhanced airborne surveillance applications and establish those that require further research and development, highlighting some of the key challenges.
Bu J, Sun R, Bai H, et al., 2017, Integrated method for the UAV navigation sensor anomaly detection, IET Radar, Sonar and Navigation, Vol: 11, Pages: 847-853, ISSN: 1751-8784
© 2016, The Institution of Engineering and Technology. The rapid development of unmanned aerial vehicles (UAVs) has made great progress for its widespread uses in military and civilian applications in recent years. On-board integrated navigation sensors are essential for UAV flight control systems in that they must operate with robustness and reliability. To achieve this, timely and effectively anomaly detection capabilities for the estimated UAV status from the integrated navigation sensors are required to ensure the UAV flight safety. Extraction of the anomaly information from the real-time navigation sensors and designing a robust and reliable anomaly detection algorithm are major issues for the UAV navigation sensor anomaly detection. This study introduces a novel integrated algorithm for detecting UAV on-board navigation sensor anomaly, by combining particle filter (PF) estimated state residuals with fuzzy inference system (FIS) decision system. The residual information is obtained based on the difference between the collected Global Positioning System measurements and high accuracy PF estimates. The indicators derived from the PF residuals are further made as inputs for the FIS system to output the different anomaly levels. The simulation and filed test results have demonstrated the effectiveness and efficiency of the proposed anomaly detection method in terms of timeliness, recall and precision.
Qin F, Sun R, Ochieng WY, et al., 2017, Integrated GNSS/DR/road segment information system for variable road user charging, TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, Vol: 82, Pages: 261-272, ISSN: 0968-090X
Shang W, Han K, Ochieng W, et al., 2017, Agent-based day-to-day traffic network model with information percolation, TRANSPORTMETRICA A-TRANSPORT SCIENCE, Vol: 13, Pages: 38-66, ISSN: 2324-9935
Sidiropoulos S, Han K, Majumdar A, et al., 2017, Robust identification of air traffic flow patterns in Metroplex terminal areas under demand uncertainty, TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, Vol: 75, Pages: 212-227, ISSN: 0968-090X
Sun R, Cheng Q, Wang G, et al., 2017, A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults., Sensors (Basel), Vol: 17
The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.
Sun R, Cheng Q, Xue D, et al., 2017, GNSS/Electronic Compass/Road Segment Information Fusion for Vehicle-to-Vehicle Collision Avoidance Application., Sensors (Basel), Vol: 17
The increasing number of vehicles in modern cities brings the problem of increasing crashes. One of the applications or services of Intelligent Transportation Systems (ITS) conceived to improve safety and reduce congestion is collision avoidance. This safety critical application requires sub-meter level vehicle state estimation accuracy with very high integrity, continuity and availability, to detect an impending collision and issue a warning or intervene in the case that the warning is not heeded. Because of the challenging city environment, to date there is no approved method capable of delivering this high level of performance in vehicle state estimation. In particular, the current Global Navigation Satellite System (GNSS) based collision avoidance systems have the major limitation that the real-time accuracy of dynamic state estimation deteriorates during abrupt acceleration and deceleration situations, compromising the integrity of collision avoidance. Therefore, to provide the Required Navigation Performance (RNP) for collision avoidance, this paper proposes a novel Particle Filter (PF) based model for the integration or fusion of real-time kinematic (RTK) GNSS position solutions with electronic compass and road segment data used in conjunction with an Autoregressive (AR) motion model. The real-time vehicle state estimates are used together with distance based collision avoidance algorithms to predict potential collisions. The algorithms are tested by simulation and in the field representing a low density urban environment. The results show that the proposed algorithm meets the horizontal positioning accuracy requirement for collision avoidance and is superior to positioning accuracy of GNSS only, traditional Constant Velocity (CV) and Constant Acceleration (CA) based motion models, with a significant improvement in the prediction accuracy of potential collision.
Sun R, Xie F, Xue D, et al., 2017, A Novel Rear-End Collision Detection Algorithm Based on GNSS Fusion and ANFIS, Journal of Advanced Transportation, Vol: 2017, ISSN: 0197-6729
© 2017 Rui Sun et al. Rear-end collisions are one of the most common types of accidents on roads. Global Satellite Navigation Systems (GNSS) have recently become sufficiently flexible and cost-effective in order to have great potential for use in rear-end collision avoidance systems (CAS). Nevertheless, there are two main issues associated with current vehicle rear-end CAS: (1) achieving relative vehicle positioning and dynamic parameters with sufficiently high accuracy and (2) a reliable method to extract the car-following status from such information. This paper introduces a novel integrated algorithm for rear-end collision detection. Access to high accuracy positioning is enabled by GNSS, electronic compass, and lane information fusion with Cubature Kalman Filter (CKF). The judgment of the car-following status is based on the application of the Adaptive Neurofuzzy Inference System (ANFIS). The field test results show that the designed algorithm could effectively detect rear-end collisions with an accuracy of 99.61% and a false alarm rate of 5.26% in the 10 Hz output rate.
Zhang X, Zhan X, Feng S, et al., 2017, An Analytical Model for BDS B1 Spreading Code Self-Interference Evaluation Considering NH Code Effects, SENSORS, Vol: 17, ISSN: 1424-8220
Anvari B, Bell MGH, Angeloudis P, et al., 2016, Calibration and Validation of a Shared Space Model Case Study, TRANSPORTATION RESEARCH RECORD, Pages: 43-52, ISSN: 0361-1981
Damy S, Majumdar A, Ochieng WY, 2016, GNSS-based High Accuracy Positioning for Railway Applications, 47th Annual Precise Time and Time Interval Systems and Applications Meeting (PTTI) / International Technical Meeting of the-Institute-of-Navigation, Publisher: INST NAVIGATION, Pages: 1003-1014, ISSN: 2330-3646
Goldbeck N, Angeloudis P, Ochieng W, 2016, Joint Vulnerability Analysis of Urban Rail Transit and Utility Networks, Transportation Research Board 95th Annual Meeting, Publisher: Transportation Research Board
As climate change is expected to increase the frequency of extreme weather events, cities around the world develop strategies to improve their disaster resilience. A key issue is the protection of critical urban infrastructure systems, such as transport networks. Rail transit networks are particularly exposed to flood risks and additional vulnerabilities arise from interdependencies with other infrastructure systems. This paper aims to improve modelling techniques that help to understand the conditions under which cascading failure can occur in interdependent urban infrastructure systems. Building on existing network flow models, a novel method for the coupling of networks is introduced, using binary connector variables and mixed integer linear programming. The coupling is modelled as additional commodity demand that is induced in one network depending on the commodity flows in another network. An example problem consisting of a rail transit network, a control system, an electric power network and a water supply network illustrates the practicability of the proposed modelling technique.
Goldbeck N, Angeloudis P, Ochieng W, 2016, Analysing the resilience of metro systems under consideration of interdependencies: A combined Dynamic Bayesian Network and network flow approach, 14th World Conference on Transport Research (WCTR)
Nascimento FAC, Majumdar A, Ochieng WY, et al., 2016, Fundamentals of safety management: The Offshore Helicopter Transportation System Model, SAFETY SCIENCE, Vol: 85, Pages: 194-204, ISSN: 0925-7535
Sidiropoulos S, Han K, Majumdar A, et al., 2016, Identifying significant traffic flow patterns in Multi-Airport systems terminal manoeuvring areas under uncertainty
© 2016 American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. Multi-Airport Systems (MAS), or Metroplex, serve air traffic demand in large metropolitan aeras such as New York, London and Tokyo. Due to the spatial proximity of the airports, Metroplex airspaces are characterized by high complexity and heavy traffic; and current system structures fail to make optimal use of the available spatio-temporal airspace resources. To increase the operational efficiency in such systems, an accurate depiction of the dominant demand patterns over the operational horizon is a prerequisite. This paper proposes a framework for the robust identification of significant air traffic flow patterns in Metroplex systems. The framework is in alignmemt with the Dynamic Route service policy for the effective management of Metroplex operations. A deterministic demand modeling approach is presented first, which takes into account the spatio-temporal changes in demand over the planning horizon of operations for the identification of significant traffic flow patterns. To handle uncertainties in the demand, a Distributionally Robust Optimization (DRO) approach is then proposed, which takes into account historical information regarding both prediction and realization of the traffic demand. Prediction errors are taken into account in a tractable and robust way to ensure the reliability of the optimized control. The DRO approach is applied to decision making in a tactical context (i.e. one-day planning) and on a rolling horizon basis (i.e. every 2 hours). The framework is applied to Time Based Flow Management (TBFM) data for the New York Metroplex (N90). The framework and results are validated by Subject Matter Experts (SMEs) from the Port Authority of New York and New Jersey (PANYNJ).
Sun R, Han K, Hu J, et al., 2016, An Integrated Algorithm Based on BeiDou/GPS/IMU and its Application for Anomalous Driving Detection, 29th International Technical Meeting of The-Satellite-Division-of-the-Institute-of-Navigation (ION GNSS+), Publisher: INST NAVIGATION, Pages: 1885-1890, ISSN: 2331-5911
Sun R, Han K, Hu J, et al., 2016, Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements, TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, Vol: 69, Pages: 193-207, ISSN: 0968-090X
Xue Y, Feng S, Ochieng WY, et al., 2016, The Improvement of the Positioning Accuracy in Search and Rescue with Two Satellites, 7th China Satellite Navigation Conference (CSNC), Publisher: SPRINGER, Pages: 255-261, ISSN: 1876-1100
Zhang X, Zhang Z, Ochieng W, et al., 2016, A Reverse Approach to Antenna Specifications for London Buses Next-generation Positioning System, 29th International Technical Meeting of The-Satellite-Division-of-the-Institute-of-Navigation (ION GNSS+), Publisher: INST NAVIGATION, Pages: 1927-1936, ISSN: 2331-5911
Ali BS, Majumdar A, Ochieng WY, et al., 2015, A causal factors analysis of aircraft incidents due to radar limitations: The Norway case study, JOURNAL OF AIR TRANSPORT MANAGEMENT, Vol: 44-45, Pages: 103-109, ISSN: 0969-6997
Ali BS, Ochieng WY, Schuster W, et al., 2015, A safety assessment framework for the Automatic Dependent Surveillance Broadcast (ADS-B) system, SAFETY SCIENCE, Vol: 78, Pages: 91-100, ISSN: 0925-7535
Anvari B, Bell MGH, Sivakumar A, et al., 2015, Modelling shared space users via rule-based social force model, TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, Vol: 51, Pages: 83-103, ISSN: 0968-090X
Feng S, Wang S, Liu J, et al., 2015, A beidou based multiple-GNSS positioning algorithm for mission critical applications, Pages: 143-155, ISSN: 1876-1100
© 2015, Springer-Verlag Berlin Heidelberg. With the development of the Global Navigation Satellite Systems (GNSS), countries that own a GNSS have realised that critically national infrastructures using Position Navigation and Timing (PNT) services and a portion of the national economy associated with GNSS applications should not be over reliant on other countries. Recently, both China and Russia have made their systems mandatory for some applications. This paper addresses this issue and proposes a Beidou based multiple-GNSS positioning algorithm. It involves three stages: (1) Understanding of the quality of Beidou solutions. This was achieved by Receiver Autonomous Integrity Monitoring (RAIM) embedded in the Beidou positioning algorithm. (2) A real time validation and modelling algorithm for the measurements from the other constellations if Beidou solution is proved good in stage 1. The measurement residual errors relative to the Beidou position solution are assessed. (3) Introduction of measurements from the other constellations if there is not enough Beidou measurements. At this stage, the models derived in stage 2 are applied to the non-Beidou measurements. The tests were carried out using the Beidou and GPS data from a reference station. The signal blockage of Beidou and GPS constellation is simulated. The test results show that the proposed methods can benefit from the validated measurements from the GPS constellation. The performance can be significantly improved in terms of accuracy, continuity, integrity and availability in difficult environments. It can be extended for critical applications where any constellation is mandated.
Goldbeck N, Angeloudis P, Ochieng W, 2015, Analysis of cascading failures across interdependent dynamic networks, 27th European Conference on Operational Research
Kyriakidis M, Majumdar A, Ochieng WY, 2015, Data based framework to identify the most significant performance shaping factors in railway operations, SAFETY SCIENCE, Vol: 78, Pages: 60-76, ISSN: 0925-7535
Mao Q, Zhang L, Li Q, et al., 2015, A least squares collocation method for accuracy improvement of mobile LiDAR systems, Remote Sensing, Vol: 7, Pages: 7402-7424
© 2015 by the authors. In environments that are hostile to Global Navigation Satellites Systems (GNSS), the precision achieved by a mobile light detection and ranging (LiDAR) system (MLS) can deteriorate into the sub-meter or even the meter range due to errors in the positioning and orientation system (POS). This paper proposes a novel least squares collocation (LSC)-based method to improve the accuracy of the MLS in these hostile environments. Through a thorough consideration of the characteristics of POS errors, the proposed LSC-based method effectively corrects these errors using LiDAR control points, thereby improving the accuracy of the MLS. This method is also applied to the calibration of misalignment between the laser scanner and the POS. Several datasets from different scenarios have been adopted in order to evaluate the effectiveness of the proposed method. The results from experiments indicate that this method would represent a significant improvement in terms of the accuracy of the MLS in environments that are essentially hostile to GNSS and is also effective regarding the calibration of misalignment.
Moradi R, Schuster W, Feng S, et al., 2015, The carrier-multipath observable: a new carrier-phase multipath mitigation technique, GPS SOLUTIONS, Vol: 19, Pages: 73-82, ISSN: 1080-5370
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