Autonomous Integrity Monitoring Research Articles

Research and simulation on key technologies of airliner’s flight management system under next-generation navigation system

This paper focuses on the requirements of flight management system (FMS), such as trajectory based operations (TBO) and required navigation performance (RNP), for large commercial airplanes under next-generation navigation system. systematic research has been carried on the architecture of FMS, the required accurate 4D trajectory prediction function, flight guidance function and integrated navigation functions. Aimed at FMS of large commercial airplanes, the research effort has been mainly on design and develop the 4D trajectory prediction algorithms based on navigation database, performance database, and the energy model of aircrafts. 4D flight guidance law is designed based on performance database. Integrated navigation system promotes navigation accuracy to satisfy the requirement of RNP and autonomous integrity monitoring (AIM) based on various navigation sensors. Based on our independently built flight deck verification platform, the simulation and verification are conducted by using real models of large commercial airplane and flight plans. As a result, the architecture of FMS and its key technologies have been proved to be feasible, reasonable, and applicable for engineering implementation on large commercial airplanes.

EGNOS-BASED MULTI-SENSOR ACCURATE AND RELIABLE NAVIGATION IN SEARCH-AND-RESCUE MISSIONS WITH UAVS

Abstract. This paper will introduce and describe the goals, concept and overall approach of the European 7th Framework Programme's project named CLOSE-SEARCH, which stands for 'Accurate and safe EGNOS-SoL Navigation for UAV-based low-cost SAR operations'. The goal of CLOSE-SEARCH is to integrate in a helicopter-type unmanned aerial vehicle, a thermal imaging sensor and a multi-sensor navigation system (based on the use of a Barometric Altimeter (BA), a Magnetometer (MAGN), a Redundant Inertial Navigation System (RINS) and an EGNOS-enabled GNSS receiver) with an Autonomous Integrity Monitoring (AIM) capability, to support the search component of Search-And-Rescue operations in remote, difficult-to-access areas and/or in time critical situations. The proposed integration will result in a hardware and software prototype that will demonstrate an end-to-end functionality, that is to fly in patterns over a region of interest (possibly inaccessible) during day or night and also under adverse weather conditions and locate there disaster survivors or lost people through the detection of the body heat. This paper will identify the technical challenges of the proposed approach, from navigating with a BA/MAGN/RINS/GNSS-EGNOSbased integrated system to the interpretation of thermal images for person identification. Moreover, the AIM approach will be described together with the proposed integrity requirements. Finally, this paper will show some results obtained in the project during the first test campaign performed on November 2010. On that day, a prototype was flown in three different missions to assess its high-level performance and to observe some fundamental mission parameters as the optimal flying height and flying speed to enable body recognition. The second test campaign is scheduled for the end of 2011.

SEARCHING LOST PEOPLE WITH UAVS: THE SYSTEM AND RESULTS OF THE CLOSE-SEARCH PROJECT

Abstract. This paper will introduce the goals, concept and results of the project named CLOSE-SEARCH, which stands for ’Accurate and safe EGNOS-SoL Navigation for UAV-based low-cost Search-And-Rescue (SAR) operations’. The main goal is to integrate a medium-size, helicopter-type Unmanned Aerial Vehicle (UAV), a thermal imaging sensor and an EGNOS-based multi-sensor navigation system, including an Autonomous Integrity Monitoring (AIM) capability, to support search operations in difficult-to-access areas and/or night operations. The focus of the paper is three-fold. Firstly, the operational and technical challenges of the proposed approach are discussed, such as ultra-safe multi-sensor navigation system, the use of combined thermal and optical vision (infrared plus visible) for person recognition and Beyond-Line-Of-Sight communications among others. Secondly, the implementation of the integrity concept for UAV platforms is discussed herein through the AIM approach. Based on the potential of the geodetic quality analysis and on the use of the European EGNOS system as a navigation performance starting point, AIM approaches integrity from the precision standpoint; that is, the derivation of Horizontal and Vertical Protection Levels (HPLs, VPLs) from a realistic precision estimation of the position parameters is performed and compared to predefined Alert Limits (ALs). Finally, some results from the project test campaigns are described to report on particular project achievements. Together with actual Search-and-Rescue teams, the system was operated in realistic, user-chosen test scenarios. In this context, and specially focusing on the EGNOS-based UAV navigation, the AIM capability and also the RGB/thermal imaging subsystem, a summary of the results is presented.

Integrity assurance of GNSS-based train integrated positioning system

This paper presents a novel Autonomous Integrity Monitoring and Assurance (AIMA) scheme for integrity assurance of the GNSS-based train integrated positioning system. In this scheme, integrity assurance strategies are combined with a three-stage hierarchical architecture, considering the coupling effects among sensor collection, sensor fusion and matching decision level in train integrated positioning. In sensor collecting stage, the AIMA scheme deals with sensor faults and failures with a PCA-based fault detection, diagnosis and isolation approach. In multi-sensor fusion stage, a novel cubature point H∞ filter is presented to enhance the fault tolerance capability, and a hybrid approach is applied to indicating and monitoring the protection level of position estimation, concerning both the estimating covariance and measurement slopes. In map matching stage, hypothesis testing with specific test statistic is carried out to determine effectiveness of positioning results. Position calculation will be invalid with an alarm triggered if the specific integrity criterion is not satisfied in any stage. Since independent solutions are applied in AIMA, integrity assurance is closely coupled with information processing in train integrated positioning. Numerical results of the three cases correspond to the hierarchical architecture with field data and simulations are presented to illustrate features and applicability of the proposed AIMA scheme and specific solutions.

Integrity of an integrated GPS/INS system in the presence of slowly growing errors. Part II: analysis

This is the second of two consecutive papers (Part II) in this journal on the monitoring of the integrity of integrated GPS/INS systems. Part I established that the worst class of error for an integrated system in terms of failure detection performance is that of slowly growing errors (SGEs). It was also concluded that among the integration architectures, the tightly coupled architecture provides the best mechanism to tackle SGEs due to its simpler structure and accessibility to the relevant measurements. In a subsequent comparison of the existing integrity algorithms, the multiple solution separation (MSS) and autonomous integrity monitoring by extrapolation (AIME) methods were selected for further analysis because of their representative characteristics of the current integrity algorithms. This paper carries out a detailed investigation of the capability of the MSS and AIME algorithms to deal with the SGEs, using a realistic simulation platform and limited real data analysis. Results show that although it is possible for the existing algorithms to detect SGEs, the time it takes to detect them varies inversely to the rate of growth of the SGE. A new algorithm is developed to deal with this based on the concept of rate detection. Simulation results show that the new algorithm can detect SGEs earlier than the current algorithms. This is validated by preliminary tests with real data.