Applications of Multi-height Sensors Data Fusion and Fault-tolerant Kalman Filter in Integrated Navigation System of UAV

Geng, Keke; Чулин, Н.А.

doi:10.1016/j.procs.2017.01.090

Cited by 42 publications

(15 citation statements)

References 2 publications

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“…Although there were other works [ 20 , 26 ] that used the weighted fusion algorithm, based on different support degree functions, weighted fusion is preferred, due to the easy computation. The computational complexity and precision of these support degree functions are critical issues to be solved.…”

Section: Related Workmentioning

confidence: 99%

“…Data fusion is one of data processing techniques to reduce the data redundancy and improve the data quality [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. Data fusion can be based on different theories, such as the artificial neural network fusion algorithm (ANN), fuzzy set theory, rough set theory, Dempster-Shafer evidence theory (DS), Bayesian fusion algorithm, Kalman filter theory, weighted average fusion algorithm, etc.…”

Section: Introductionmentioning

confidence: 99%

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Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks

Shi

Yongming

et al. 2018

Sensors

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For monitoring the aquaculture parameters in pond with wireless sensor networks (WSN), high accuracy of fault detection and high precision of error correction are essential. However, collecting accurate data from WSN to server or cloud is a bottleneck because of the data faults of WSN, especially in aquaculture applications, limits their further development. When the data fault occurs, data fusion mechanism can help to obtain corrected data to replace abnormal one. In this paper, we propose a data fusion method using a novel function that is Dynamic Time Warping time series strategy improved support degree (DTWS-ISD) for enhancing data quality, which employs a Dynamic Time Warping (DTW) time series segmentation strategy to the improved support degree (ISD) function. We use the DTW distance to replace Euclidean distance, which can explore the continuity and fuzziness of data streams, and the time series segmentation strategy is adopted to reduce the computation dimension of DTW algorithm. Unlike Gauss support function, ISD function obtains mutual support degree of sensors without the exponent calculation. Several experiments were finished to evaluate the accuracy and efficiency of DTWS-ISD with different performance metrics. The experimental results demonstrated that DTWS-ISD achieved better fusion precision than three existing functions in a real-world WSN water quality monitoring application.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks

Shi

Yongming

et al. 2018

Sensors

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“…For example, considering disturbances of unknown bounds, Chang et al [33] propose an adaptive sliding mode algorithm to deal with bias fault and noise. A fault tolerant Kalman filter is designed to detect and isolate the faulty sensors in [34]. Ponsart et al [35] propose a feedback controller based on a robust LPV observer to track reference position subjected to sensor faults and disturbances.…”

Section: Introductionmentioning

confidence: 99%

“…(1) For the quadrotor UAV, different from the sensor fault research [31][32][33][34][35], this paper addresses the diverse sensor faults (three additive and one multiplicative faults), and simultaneously considers uncertain parameters, external disturbances, and measurement noises. (2) Different from the adaptive backstepping methods for special second-order system in [19,20], the proposed approach can be used for different kinds of systems besides quadrotor UAV.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy adaptive nonlinear sensor‐fault tolerant control for a quadrotor unmanned aerial vehicle

Cao

Zhou

et al. 2018

Asian Journal of Control

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In this paper, a novel fuzzy adaptive nonlinear fault tolerant control design scheme is proposed for attitude dynamics of quadrotor UAV subjected to four sensor faults (bias, drift, loss of accuracy, loss of effectiveness). The sensor faults in Euler angle loop are transformed equivalently into a mismatched uncertainty vector, and other unknown items involving faults, uncertain parameters and external disturbances in angular velocity loop are lumped into an unknown nonlinear function vector.Fuzzy logic systems with adaptive parameters are used to approximate the mismatched uncertainty and lumped nonlinear function vectors. Dynamic surface control is applied to design the fault tolerant controller, and sliding mode control is introduced to improve the control accuracy. All signals of the closed-loop control system are proved to be semi-global uniformly ultimately bounded. Simulations demonstrate the effectiveness of the proposed approach for sensor faults. KEYWORDS dynamic surface control, fault tolerant control, fuzzy logic system, Quadrotor unmanned aerial vehicle, sensor fault, sliding mode control

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“…A multimode federated Gaussian sum particle fault-tolerant filtering method was proposed to improve the estimation precision for nonlinear and non-Gaussian systems in [8]. In [9], the 2 detection method was utilized to detect the fault of three height sensors for unmanned aerial vehicles (UAV). The faulty height sensor was isolated to achieve fault-tolerant integrated navigation for UAV.…”

Section: Introductionmentioning

confidence: 99%

Sequential Fault-Tolerant Fusion Estimation for Multisensor Time-Varying Systems

Feng

Wen

2018

Discrete Dynamics in Nature and Society

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In this paper, the impact of the fusion framework on the fault diagnosis process is discussed. The centralized fusion framework makes it difficult to locate and estimate the sensor fault. Based on the fault detection method, the sequential fusion framework could locate and estimate the sensor fault and realize the fault-tolerant estimation of system states. In the sense of minimum mean square error (MMSE), based on the sequential detection of bias fault of sensors, a sequential fault-tolerant fusion estimation approach is presented to estimate the sensor fault and the system state, simultaneously, optimally, in real time. Further, a novel alternate fault-tolerant fusion estimation method is proposed to alternately estimate the sensor fault and the system state, in the frame of sequential fusion. What is more, the equivalency of the two proposed methods is proved. And the feasibility and equivalency of them are also verified by the computer simulation.

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Applications of Multi-height Sensors Data Fusion and Fault-tolerant Kalman Filter in Integrated Navigation System of UAV

Cited by 42 publications

References 2 publications

Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks

Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks

Fuzzy adaptive nonlinear sensor‐fault tolerant control for a quadrotor unmanned aerial vehicle

Sequential Fault-Tolerant Fusion Estimation for Multisensor Time-Varying Systems

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