Abstract:The integration of globe navigation satellite system ͑GNSS͒ with inertial navigation system ͑INS͒ is being heavily investigated as it can deliver more robust and reliable systems than either of the individual systems. In order to ensure the integrity of navigation solutions, it is necessary to incorporate an effective quality control scheme which uses redundant information provided by both the measurement and dynamic models. As the GNSS receiver autonomous integrity monitoring ͑RAIM͒ algorithms are well develo… Show more
“…Some FDE algorithms have been developed for tightly coupled GNSS/INS system over the past years. Autonomous Integrity Monitoring by Extrapolation [10], Innovation-Based (IB) method [11], Residual-Based (RB) method [11], Solution Separation (SS) method [12,13], Quality Control (QC) [14], Generalized Likelihood Ratio (GLR) [15], Extended RAIM (ERAIM) [16], and Rate Detector (RD) method [17] are the most representative hypothesis-test FDE algorithms. Recently, some novel methods have been proposed, including Support Vector Machine (SVM) approach [18], neural network approach [19], robust estimator approaches [20][21][22] and nonlinear filter approaches [23,24].…”
To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.
“…Some FDE algorithms have been developed for tightly coupled GNSS/INS system over the past years. Autonomous Integrity Monitoring by Extrapolation [10], Innovation-Based (IB) method [11], Residual-Based (RB) method [11], Solution Separation (SS) method [12,13], Quality Control (QC) [14], Generalized Likelihood Ratio (GLR) [15], Extended RAIM (ERAIM) [16], and Rate Detector (RD) method [17] are the most representative hypothesis-test FDE algorithms. Recently, some novel methods have been proposed, including Support Vector Machine (SVM) approach [18], neural network approach [19], robust estimator approaches [20][21][22] and nonlinear filter approaches [23,24].…”
To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.
“…Standard outlier rejection techniques [14], [25], [29], [30] can be executed to detect and remove outliers. 2) Starting from (X,Ň), solve the NMILS problem in (11) to obtain the optimal solution (X * , N * ).…”
“…extend Receiver Autonomous Integrity Monitoring techniques [24], [25] to reduce the effects of the outlier measurements on the estimation and to detect incorrect integers. In [17], integer ambiguity resolution is considered within the CRT framework for the RTK GPS/INS application.…”
Abstract-Integer ambiguity resolution is a challenging technical issue that exists in real-time kinematic (RTK) global positioning system (GPS) navigation. Once the integer vector is resolved, centimeter-level positioning estimation accuracy can be achieved using the GPS carrier phase measurements. Recently, a real-time sliding window Bayesian estimation approach to RTK GPS and inertial navigation was proposed to provide reliable centimeter accurate-state estimation, via integer ambiguity resolution utilizing a prior along with all inertial measurement unit and GPS measurements within the time window. One challenge to implementing that approach in practice is the high computation cost. This paper proposes a novel implementation approach with significantly lower computational requirements and includes a thorough theoretical analysis. The implementation results show that the proposed method resolves an integer vector identical to that of the original method and achieves state estimation with centimeter global positioning accuracy.Index Terms-Global positioning system (GPS), inertial navigation, inertial navigation system (INS), integer ambiguity, real-time kinematic (RTK), sliding window estimation.
“…The GNSS receivers make use of carrier phase based positioning which allows for accuracy up to centimeter level 17,18 . Furthermore to ensure integrity monitoring, an integrated system of GNSS and INS with extended autonomous integrity monitoring is used 19 . To optimally benefit from the vortex, the location of the vortex core should be accurately known.…”
The airline industry is under continuous pressure to reduce emissions and costs. This paper investigates the feasibility for commercial airlines to use formation flight to reduce emissions and fuel burn. To fly in formation, an aircraft needs to benefit from the wake vortices of the preceding aircraft. This requires a stable aerodynamic flow, accurate navigation and a highly sophisticated aircraft to counteract the negative consequences of flying in formation. It is found that the most stable region for an aircraft is between ten to twenty wingspans. For safety reasons the formation will fly in an echelon shape, indicating that only one wing of the aircraft is benefitting from the vortex. A GNSS/INS integrated navigation system is needed to allow for safe and accurate spacing between the aircraft. LiDAR based vortex detection is used to fly in the most stable and beneficial area of the vortex. Extra measures are taken to counteract the negative effects induced by flying with one wing in the vortex. A morphing wing is used to counteract the rolling moment due to an increase in lift on one wing. A strengthened tail is necessary to compensate the yaw moment induced by a reduction on drag on one wing. The benefits of formation flying in combination with the state-of-the-art open rotor jet could lead to fuel and emission savings of 54% compared to a Boeing 787. Implemented in 2030 this would be a major impact on the carbon footprint of the aviation industry.
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