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Recently an unmanned aerial vehicle (UAV) has been widely used for military and civil applications. The role of a navigation
system in the UAV is to provide navigation data to the flight control computer (FCC) for guidance and control. Since performance
of the FCC is highly reliant on the navigation data, a fault in the navigation system may lead t...
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... However, few studies deal with fault detection and FTS design in integrated navigation systems. For fault detection in a GPS/INS integrated system, the residual signal is generated and statistical analysis reveals the fault characteristic (Oh et al., 2005). The fault detection scheme has been applied to a GPS/INS/magnetic compass integration system with statistical test related to azimuth error output (Oh et al., 2005). ...
... For fault detection in a GPS/INS integrated system, the residual signal is generated and statistical analysis reveals the fault characteristic (Oh et al., 2005). The fault detection scheme has been applied to a GPS/INS/magnetic compass integration system with statistical test related to azimuth error output (Oh et al., 2005). The fault detection scheme is achieved by training a Gaussian process regression (GPR) model to predict the innovation of a Kalman filter (Zhu et al., 2016). ...
The inertial navigation system/Doppler velocity logger (INS/DVL) plays an important role in ocean navigation. Any DVL malfunction poses serious risks to navigation. A precise detection system is required to detect the initial moments of DVL signal malfunctions; moreover, with loss of DVL, a fault-tolerant scheme (FTS) is necessary for DVL signal reconstruction. In this paper, an evolutionary knowledge-based method, namely improved evolutionary TS-fuzzy (I-eTS), is adopted to build an artificial intelligence (AI)-based pseudo DVL to deal with long-term outage of DVL. By employing Gaussian process regression (GPR) models for fault detection, a new FTS is constructed. To verify the effectiveness of the new fault detection and fault tolerance system, navigation data is gathered by a test setup and algorithms are performed in the laboratory. In the tests, it is demonstrated that the proposed FTS leads to rapid detection of both gradual and abrupt faults, which leads to less interaction between fault detection and FTS.
... However, the BDS's pseudo range-error is worst and the model isn't accurate so that it does not meet the basic premise of the indirect KF, thus affects the filter accuracy of the INS's data integration. The thesis gets the nonlinear model of the all-attitude integrated BDS/INS navigation system [3][4][5] by the direct method [6]: The information fusion has two ways: centralized filter and distributed filter. The distributed federal kalman filter (FKF) based on the KF and the information distribution technology has been taken seriously because of the parallel data processing, flexible design, small amount of calculation, better fault-tolerant performance. ...
In order to improve the attitude accuracy, the thesis establishes the all-attitude integrated BDS/INS navigation nonlinear system model based on the position, velocity, attitude by adding the BDS’s attitude measurement information into the measurement equation of the traditional BDS/INS integrated navigation nonlinear system model. Considering the problem that the dynamic navigation system model is difficult to accurately describe the complex navigation environment, the thesis improves the dynamic characteristics of the information distribution of the federal filter algorithm which could timely change based on the eigenvalues ratio of each subsystem’s error variance matrix. Then, the adaptive federal unscented particle filter (AFUPF) is proposed. The simulation shows that the proposed algorithm could effectively weaken the impact on the system accuracy of the inaccurate high-dynamic model, and improve the adaptability, the fault tolerance and the accuracy, especially the attitude accuracy.
... The mean shift principle treats the error as unknown parameters and uses the hypothesis and test theory to process the errors. For example, the fault detection and isolation method uses the chi-squared distribution developed from the Kalman filters information matrix of innovations and measurement residuals to detect the errors and outliers in raw measurements of all sensors [22,23]. The alternative variance inflation principle applies the equivalent weight function to iteratively adjust the weights of measurements according to the posterior variances of the measurements [24,25]. ...
... The mean shift principle treats the error as unknown parameters and uses the hypothesis and test theory to process the errors. For example, the fault detection and isolation method uses the chi-squared distribution developed from the Kalman filters information matrix of innovations and measurement residuals to detect the errors and outliers in raw measurements of all sensors [22,23]. The alternative variance inflation principle applies the equivalent weight function to iteratively adjust the weights of measurements according to the posterior variances of the measurements [24,25]. ...
Constraints can provide additional aids to a multi-sensor integrated navigation system and hence can increase accuracy of the solution and enhance reliability of the system. To integrate the constraints with the data from the sensors, the traditional integration Kalman filter (IKF) needs to be reconstructed. A new algorithm, the so-called constrained adaptive robust integration Kalman filter (CARIKF) is presented, which implements adaptive integration upon the robust direct fusion solution. In the algorithm the raw observations from all heterogeneous sensors are corrected by the pseudoobservations derived from state equality constraint. The posterior covariances of the corrected observations are subsequently estimated upon the robust maximum-likelihood-type estimation (M-estimation) theory. The fusion state and its covariance are solved for all sensors further in the least squares (LS) sense. The pseudoobservations are constructed according to the estimated state and its covariance. They are further combined with the dynamic model of the host platform in an adaptive Kalman filter (AKF), from which a reliable and accurate navigation solution can be then obtained. A state constraint model is proposed upon Newton's forward differential extrapolation numerical method. To demonstrate performance of the CARIKF algorithm, simulations have been conducted in different dynamic and observation scenarios. Several algorithms are compared to evaluate the validity and efficiency of the CARIKF. The results show that the CARIKF is superior to other algorithms and can significantly improve the precision and reliability of the integrated solution.
One of the main factors related to the deterioration of estimation accuracy in inertial measurement unit (IMU)-based orientation determination is the object's acceleration. This is because accelerometer signals under accelerated motion conditions cannot be longer reference vectors along the vertical axis. In order to deal with this issue, some orientation estimation algorithms adopt acceleration-compensating mechanisms. Such mechanisms include the simple switching techniques, mechanisms with adaptive estimation of acceleration, and acceleration model-based mechanisms. This paper compares these three mechanisms in terms of estimation accuracy. From experimental results under accelerated dynamic conditions, the following can be concluded. (1) A compensating mechanism is essential for an estimation algorithm to maintain accuracy under accelerated conditions. (2) Although the simple switching mechanism is effective to some extent, the other two mechanisms showed much higher accuracies, particularly when test conditions were severe.
For the integrated navigation system, the correctness and the rapidity of fault detection for each sensor subsystem affects the accuracy of navigation. In this paper, a novel fault detection method for navigation systems is proposed based on Gaussian Process Regression (GPR). A GPR model is first used to predict the innovation of a Kalman filter. To avoid local optimisation, particle swarm optimisation is adopted to find the optimal hyper-parameters for the GPR model. The Fault Detection Function (FDF), which has an obvious jump in value when a fault occurs, is composed of the predicted innovation, the actual innovation of the Kalman filter and their variance. The fault can be detected by comparing the FDF value with a predefined threshold. In order to verify its validity, the proposed method is used in a SINS/GPS/Odometer integrated navigation system. The comparison experiments confirm that the proposed method can detect a gradual fault more quickly compared with the residual chi-squared test. Thus the navigation system with the proposed method gives more accurate outputs and its reliability is greatly improved.
This paper investigates the fault-tolerant localization problem when the GPS receiver of one low-cost UAV in a fleet works improperly due to failure. A cooperative localization algorithm based on inter-UAV range measurements is proposed. Similar to the principle of GPS, the UAV's location in 2D horizontal plane can be determined using the relative ranges to the other three UAVs at known location in inertial coordinate system. Considering the fact that accuracy of UAV's location is worse than a positioning satellite, a Kalman filter is employed respectively on the three UAVs to estimate their locations with a constant velocity (CV) model during each computing cycle. Based on the estimations and the geometric relationship of the relative ranges, the location of UAV with GPS communication malfunction is calculated. Furthermore, in order to know well about the accuracy and statistic characteristics of calculation result, the horizontal dilution of positioning (HDOP) at length is analyzed through constructing the error equations. Taking the calculation results as observation data, another Kalman filter is applied to the UAV with malfunction, which can calculate the variance of observation noise adaptively on real-time. At last, by programming in Matlab/Simulink, a simulation example with four UAVs in a 2D scenario is shown to evaluate the effectiveness of the proposed algorithm. Copyright © 2010 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
In this work several unconventional navigation systems are presented that include redundancy of traditional navigation sensors. Main focus is on sensor architectures, while sensor fusion algorithms will be treated as a secondary aspect. Specifically, an analysis of systems with multiple IMUs, Gyro-free INS and GPS derived attitude has been performed. Several reasons justify the application of architectures with redundant navigation sensors; the most relevant obviously concerns the capability to detect and identify some faults. Anyway, sensor redundancy also allows increasing the accuracy of some measurements (as for example in multiple IMU's systems) or avoiding the use of some sensors, which may have disadvantages in terms of costs or accuracy, without renounce to their measurements (as for example in Gyro-free INS). In fact these systems are based on the application of pseudomeasurements. Copyright © 2010 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
A unlfted approacb to the ultra-tiptly coupled GPSlINS Integrated navigation system Is pnposed. It Is sbown that other methods of nltra-tlabtly coupled GPSIINS Integrated navigation systems are Included In the proposed framework, and some implementation issues are diseussed. In order to sbow the validity of the pnposed appnaeb, position accuracy and anti-Jamming capabilies of three methods of nltra-tlgbtly-eouple d GPSIINS integration system, Included In the pnposed framework, were compared. Tbe comparlsous were carried out through computer slmulatlons and post-pncesslng of data from a GNSS hardware simulator. Tbe resnlts show that the proposed approacb works weD and gives accurate navigation output with antl-Jammlug capability.
