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integrated navigation for autonomous underwater vehicles in aquaculture: A review

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Information Processing in Agriculture 2020年第1期7卷 139-151页

作者： Jianhua Bao Daoliang Li Xi Qiao Thomas Rauschenbach Beijing Engineering and Technology Research Center for Internet of Things in Agriculture China Agricultural UniversityBeijing 100083PR China College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083PR China School of Electrical Engineering and Automation Jiangsu Normal UniversityXuzhou 221116PR China Fraunhofer Center for Applied Systems Technology Am Vogelherd 5098693 IlmenauGermany

Aquaculture is the world’s fastest growing sector within the food industry,supplying humans with over half their aquatic *** quality monitoring or cage inspection is an indispensable part in aquaculture and is usually done *** underwater vehicles(AUVs)are increasingly being used in aquaculture as technology advances and the cost *** navigation is considered as a basic function of AUVs but is a challenging issue primarily due to the attenuated nature of electromagnetic waves in water and unstructured underwater *** inertial navigation system(INS)is usually selected as the core navigation equipment for AUV navigation because it never fails to *** paper reviews and surveys the latest advances in integrated navigation technologies for AUVs and provides a comprehensive reference for researchers who intend to apply AUVs to autonomous monitoring of *** INS has difficulty obtaining long-range precision navigation due to the inherent error accumulation of inertial sensors over time;aiding inertial navigation systems with auxiliary sensors are common means to improve the navigation accuracy of an INS for *** survey is conducted according to different assisted navigation technologies for inertial ***,the future challenges of the AUV navigation are also presented.

关键词： Aquaculture Underwater navigation Autonomous underwater vehicle (AUV) Inertial navigation system(INS) integrated navigation

integrated navigation Based on Robust Estimation Outputs of Multi-sensor Measurements and Adaptive Weights of Dynamic Model Information

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Geo-Spatial Information Science 2005年第3期8卷 201-204,224页

作者： YANG Yuanxi GAO Weiguang Xi"an Research Institute of Surveying and Mapping

An integrated navlgation based on the kinematic or dynamic state model and the raw measurements has the advantages of high redundancy, high reliability, as well as high ability of fault tolerance and simplicity in calculation. In order to control the influences of measurements outliers and the kinematic model errors on the integrated navigation results, a robust estimation method and an adaptive data fusion method are applied. An integrated navigation example using simulated data is performed and analyzed.

关键词： integrated navigation multi-sensor robust estimation adaptive data fusion

A novel optimal data fusion algorithm and its application for the integrated navigation system of missile

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Chinese Journal of Aeronautics 2022年第5期35卷 53-68页

作者： Di LIU Xiyuan CHEN Xiao LIU Key Laboratory of Micro-Inertial Instrument and Advanced navigation Technology Ministry of Education School of Instrument Science and EngineeringSoutheast UniversityNanjing 210096China School of Electronic and Information Engineering(Department of Physics) Qilu University of TechnologyJinan 250353China

For Inertial navigation System(INS)/Celestial navigation System(CNS)/Global navigation Satellite System(GNSS)integrated navigation system of the missile,the performance of data fusion algorithms based on the Cubature Kalman Filter(CKF)is seriously degraded when there are non-Gaussian noise and process-modeling errors in the system ***,a novel method is proposed,which is called Optimal Data Fusion algorithm based on the Adaptive Fading maximum Correntropy generalized high-degree CKF(AFCCKF-ODF).First,the Adaptive Fading maximum Correntropy generalized high-degree CKF(AFCCKF)is proposed and used as the local filter for the INS/GNSS and INS/CNS subsystems to improve the robustness of local state ***,the local state estimation is fused based on the minimum variance principle and highdegree cubature criterion to get the globally optimal ***,the experimental results verify that the proposed algorithm can significantly improve the robustness of the missile-borne INS/CNS/GNSS integrated navigation system to non-Gaussian noise and process modeling error and obtain the global optimal navigation information.

关键词： Data fusion High-degree cubature Kalman filter integrated navigation Non-Gaussian noise Process-modeling error

Intelligent fault-tolerant algorithm with two-stage and feedback for integrated navigation federated filtering

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Journal of Systems Engineering and Electronics 2011年第2期22卷 274-282页

作者： Li Cong Honglei Qin Zhanzhong Tan School of Electronic and Information Engineering Beihang University Beijing 100191 P. R. China

In order to take full advantage of federated filter in fault-tolerant design of integrated navigation system, the limitation of fault detection algorithm for gradual changing fault detection and the poor fault tolerance of global optimal fusion algorithm are the key problems to deal with. Based on theoretical analysis of the influencing factors of federated filtering fault tolerance, global fault-tolerant fusion algorithm and information sharing algorithm are proposed based on fuzzy assessment. It achieves intelligent fault-tolerant structure with two-stage and feedback, including real-time fault detection in sub-filters, and fault-tolerant fusion and information sharing in main filter. The simulation results demonstrate that the algorithm can effectively improve fault-tolerant ability and ensure relatively high positioning precision of integrated navigation system when a subsystem having gradual changing fault.

关键词： integrated navigation federated filter fuzzy assess-ment fault-tolerant fusion information sharing.

Practical integrated navigation fault detection algorithm based on sequential hypothesis testing

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Journal of Systems Engineering and Electronics 2011年第1期22卷 146-149页

作者： Feng Yang Cheng Cheng Quan Pan Gongyuan Zhang Department of Automation Northwestern Polytechnical University Xi'an 710072 R R. China

In detecting system fault algorithms,the false alarm rate and undectect rate generated by residual Chi-square test can affect the stability of *** paper proposes a fault detection algorithm based on sequential residual Chi-square test and applies to fault detection of an integrated navigation *** simulation result shows that the algorithm can accurately detect the fault information of global positioning system（GPS）,eliminate the influence of false alarm and missed detection on filter,and enhance fault tolerance of integrated navigation systems.

关键词： residual Chi-square test integrated navigation fault detection.

Robust adaptive UKF based on SVR for inertial based integrated navigation

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Defence Technology（防务技术） 2020年第4期16卷 846-855页

作者： Meng-de Zhang Hai-fa Dai Bai-qing Hu Qi Chen College of Electrical Engineering Naval Univ.of EngineeringWuhan 430033China Qingdao Brigade Shandong Branch of the Armed Police Marine Police CorpsQingdao 266033China

Aiming at the problem that the traditional Unscented Kalman Filtering(UKF) algorithm can't solve the problem that the measurement covariance matrix is unknown and the measured value contains outliers,this paper proposes a robust adaptive UKF algorithm based on Support Vector Regression(SVR).The algorithm combines the advantages of support vector regression with small samples,nonlinear learning ability and online estimation capability of adaptive algorithm based on ***,the SVR model is trained by using the innovation in the sliding window,and the new innovation is *** the deviation between the estimated innovation and the measured innovation exceeds a given threshold,then measured innovation will be replaced by the predicted innovation,and then the processed innovation is used to calculate the measurement noise covariance matrix using the adaptive estimation *** experiments and measured data experiments show that SVRUKF is significantly better than the traditional UKF,robust UKF and adaptive UKF algorithms for the case where the covariance matrix is unknown and the measured values have outliers.

关键词： integrated navigation Support vector regression Unscented Kalman filter Robust filter Adaptive filter

Model-based robust estimation and fault detection for MEMS-INS/GPS integrated navigation systems

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Chinese Journal of Aeronautics 2014年第4期27卷 947-954页

作者： Miao Lingjuan Shi Jing School of Automation Beijing Institute of Technology School of Automation Northwestern Polytechnical University

In micro-electro-mechanical system based inertial navigation system（MEMS-INS）/global position system（GPS） integrated navigation systems, there exist unknown disturbances and abnormal measurements. In order to obtain high estimation accuracy and enhance detection sensitivity to faults in measurements, this paper deals with the problem of model-based robust estimation（RE） and fault detection（FD）. A filter gain matrix and a post-filter are designed to obtain a RE and FD algorithm with current measurements, which is different from most of the existing priori filters using measurements in one-step delay. With the designed filter gain matrix, the H-infinity norm of the transfer function from noise inputs to estimation error outputs is limited within a certain range; with the designed post-filter, the residual signal is robust to disturbances but sensitive to faults. Therefore, the algorithm can guarantee small estimation errors in the presence of disturbances and have high sensitivity to faults. The proposed method is evaluated in an integrated navigation system, and the simulation results show that it is more effective in position estimation and fault signal detection than priori RE and FD algorithms.

关键词： Fault detection Inertial navigation systems integrated navigation Micro-electro-mechanicalsystem Robust estimation

SINS/CNS/GPS integrated navigation algorithm based on UKF

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Journal of Systems Engineering and Electronics 2010年第1期21卷 102-109页

作者： Haidong Hu Xianlin Huang Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin 150001 E R. China

A new nonlinear algorithm is proposed for strapdown inertial navigation system （SINS）/celestial navigation system （CNS）/global positioning system （GPS） integrated navigation systems. The algorithm employs a nonlinear system error model which can be modified by unscented Kalman filter （UKF） to give predictions of local filters. And these predictions can be fused by the federated Kalman filter. In the system error model, the rotation vector is introduced to denote vehicle＇s attitude and has less variables than the quaternion. Also, the UKF method is simplified to estimate the system error model, which can both lead to less calculation and reduce algorithm implement time. In the information fusion section, a modified federated Kalman filter is proposed to solve the singular covariance problem. Specifically, the new algorithm is applied to maneuvering vehicles, and simulation results show that this algorithm is more accurate than the linear integrated navigation algorithm.

关键词： navigation system integrated navigation unscented Kalman filter federated Kalman filter strapdown inertial navigation system celestial navigation system global psitioning system.

INS-GNSS integrated navigation Algorithm Based on TransGAN

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Intelligent Automation & Soft Computing 2023年第7期37卷 91-110页

作者： Wang Linxuan Kong Xiangwei Xu Hongzhe Li Hong Chinese Flight Test Establishment Xi’an710089China Xi’an Jiaotong University Xi’an710049China

With the rapid development of autopilot technology,a variety of engi-neering applications require higher and higher requirements for navigation and positioning accuracy,as well as the error range should reach centimeter *** navigation systems such as the inertial navigation system(INS)and the global navigation satellite system(GNSS)cannot meet the navigation require-ments in many cases of high mobility and complex *** the purpose of improving the accuracy of INS-GNSS integrated navigation system,an INS-GNSS integrated navigation algorithm based on TransGAN is *** of all,the GNSS data in the actual test process is applied to establish the data ***,the generator and discriminator are *** the model structure of generator transformer,the generator is constructed by multi-layer transformer encoder,which can obtain a wider data perception *** generator and discriminator are trained and optimized by the production countermeasure network,so as to realize the speed and position error compensa-tion of ***,when GNSS works normally,TransGAN is trained into a high-precision prediction model using INS-GNSS *** trained Trans-GAN model is emoloyed to compensate the speed and position errors for *** the test analysis ofﬂight test data,the test results are compared with the performance of traditional multi-layer perceptron(MLP)and fuzzy wavelet neural network(WNN),demonstrating that TransGAN can effectively correct the speed and position information when GNSS is interrupted,with the high accuracy.

关键词： GNSS INS TransGAN integrated navigation