Journal articles on the topic 'Inertial navigation; Kalman filter'
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1
Zhang, Xing Zhi, Kun Peng He, and Chen Yang Wang. "Transfer Alignment for MEMS Integrated Navigation System Based on H∞ Filter." Applied Mechanics and Materials 490-491 (January 2014): 886–90. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.886.
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The transfer alignment of strapdown inertial units were proposed that use the H filter to estimate the misalignment of the slave INS (inertial navigation system) relative to the master INS. Characteristics of the H filter in transfer alignment were studied in detail by checking digital simulation results obtained by using the H and Kalman filters. The results shows that the misalignment angle obtained with the H filter converge faster and closer to the exact values than do those obtained with the Kalman filter. The H filter is more robust than the Kalman filter in transfer alignment for MEMS integrated navigation system.
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Wang, Ning. "Satellite/Inertial Navigation Integrated Navigation Method Based on Improved Kalman Filtering Algorithm." Mobile Information Systems 2022 (May 19, 2022): 1–9. http://dx.doi.org/10.1155/2022/4627111.
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With the continuous development of positioning technology in today’s world, the accuracy requirements for navigation and positioning are also getting higher and higher. Global Positioning and Navigation System (GPS) can provide high-precision long-term navigation and positioning information. However, it has a strong dependence on the external environment, which means that it is easily disturbed by environmental changes and affects the accuracy of navigation and positioning and even leads to positioning failure. The inertial navigation system (INS) is an autonomous navigation system. It uses sensors to measure the specific force and angular velocity of the carrier for positioning and navigation, which means that it is less affected by the environment. However, the inertial navigation device will produce a certain initial error due to the restriction of the manufacturing level, and the error will increase with time, so the inertial navigation method is not suitable for long-term navigation. Therefore, it is of great practical significance to realize satellite/inertial navigation integrated navigation by combining the respective advantages of satellite navigation and inertial navigation methods and avoiding their respective disadvantages. This paper is aimed at studying the satellite/inertial navigation integrated navigation method based on the improved Kalman filter algorithm. The satellite inertial navigation integrated navigation experiment is carried out based on the improved Kalman filter algorithm. In the experiment, the noise reduction experiment of the designed satellite inertial navigation system was carried out by using the filtering noise reduction function of the improved Kalman filter algorithm, and the conclusion was drawn after the experiment. The navigation accuracy of the satellite inertial navigation system is improved by a total of 2 m after the improved Kalman filter algorithm is used to filter the noise reduction.
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Zhou, Weidong, Jiaxin Hou, Lu Liu, Tian Sun, and Jing Liu. "Design and Simulation of the Integrated Navigation System based on Extended Kalman Filter." Open Physics 15, no. 1 (April 17, 2017): 182–87. http://dx.doi.org/10.1515/phys-2017-0019.
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AbstractThe integrated navigation system is used to estimate the position, velocity, and attitude of a vehicle with the output of inertial sensors. This paper concentrates on the problem of the INS/GPS integrated navigation system design and simulation. The structure of the INS/GPS integrated navigation system is made up of four parts: 1) GPS receiver, 2) Inertial Navigation System, 3) Extended Kalman filter, and 4) Integrated navigation scheme. Afterwards, we illustrate how to simulate the integrated navigation system with the extended Kalman filter by measuring position, velocity and attitude. Particularly, the extended Kalman filter can estimate states of the nonlinear system in the noisy environment. In extended Kalman filter, the estimation of the state vector and the error covariance matrix are computed by steps: 1) time update and 2) measurement update. Finally, the simulation process is implemented by Matlab, and simulation results prove that the error rate of statement measuring is lower when applying the extended Kalman filter in the INS/GPS integrated navigation system.
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Fariz, Outamazirt, Muhammad Ushaq, Yan Lin, and Fu Li. "Enhanced Accuracy Navigation Solutions Realized through SINS/GPS Integrated Navigation System." Applied Mechanics and Materials 332 (July 2013): 79–85. http://dx.doi.org/10.4028/www.scientific.net/amm.332.79.
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Strapdown Inertial Navigation Systems (SINS) displays position errors which grow with time in an unbounded manner. This degradation is due to the errors in the initialization of the inertial measurement unit, and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Improvement to this unbounded growth in errors can be made by updating the inertial navigation system solutions periodically with external position fixes, velocity fixes, attitude fixes or any combination of these fixes. The increased accuracy is obtained through external measurements updating inertial navigation system using Kalman filter algorithm. It is the basic requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertial Navigation System (SINS), Global Positioning System (GPS) is presented using a centralized linear Kalman filter.
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Qian, Kun, Jian-Guo Wang, and Baoxin Hu. "Novel Integration Strategy for GNSS-Aided Inertial Integrated Navigation." GEOMATICA 69, no. 2 (June 2015): 217–30. http://dx.doi.org/10.5623/cig2015-205.
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The conventional integration mechanism in GNSS (Global Navigation Satellite Systems) aided inertial integrated positioning and navigation system is mainly based on the continuous outputs of the navigation mechanization, the associated error models for navigation parameters, the biases of the inertial measurement units (IMU), and the error measurements. Its strong dependence on the a priori error characteristics of inertial sensors may suffer with the low-cost IMUs, e.g. the MEMS IMUs due to their low and unstable performance. This paper strives for a significant breakthrough in a compact and general integration strategy which restructures the Kalman filter by deploying a system model on the basis of 3D kinematics of a rigid body and performing measurement update via all sensor data inclusive of the IMU measurements. This novel IMU/GNSS Kalman filter directly estimates navigational parameters instead of the error states. It enables the direct use of the IMU's raw outputs as measurements in measurement updates of Kalman filter instead of involving the free inertial navigation calculation through the conventional integration mechanism. This realization makes all of the sensors in a system no longer to be differentiated between core and aiding sensors. The proposed integration strategy can greatly enhance the sustainability of low-cost navigation systems in poor GNSS and/or GNSS denied environment compared to the conventional aided error-state-based inertial navigation integration mechanism. The post-processed solutions are presented to show the success of the proposed multisensor integrated navigation strategy.
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An, Shi Qi, and Jun Kai Zhang. "The Study of Kalman Filtering Algorithm in the Initial Alignment of Strapdown Inertial Navigation System." Applied Mechanics and Materials 740 (March 2015): 596–99. http://dx.doi.org/10.4028/www.scientific.net/amm.740.596.
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According to the principle and the method of initial alignment of strapdown inertial navigation system, proposed based on Sage-Husa adaptive kalman filter algorithm. The measured simulation data, compared with those of kalman filtering algorithm, show that the optimized algorithm can optimize the noise estimation, revise accumulated error of strapdown inertial navigation system, and greatly improve the navigation accuracy.
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Gopaul, N. S., J. G. Wang, and B. Hu. "Discrete EKF with pairwise Time Correlated Measurement Noise for Image-Aided Inertial Integrated Navigation." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-2 (November 11, 2014): 61–66. http://dx.doi.org/10.5194/isprsannals-ii-2-61-2014.
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An image-aided inertial navigation implies that the errors of an inertial navigator are estimated via the Kalman filter using the aiding measurements derived from images. The standard Kalman filter runs under the assumption that the process noise vector and measurement noise vector are white, i.e. independent and normally distributed with zero means. However, this does not hold in the image-aided inertial navigation. In the image-aided inertial integrated navigation, the relative positions from optic-flow egomotion estimation or visual odometry are <i>pairwise</i> correlated in terms of time. It is well-known that the solution of the standard Kalman filter becomes suboptimal if the measurements are colored or time-correlated. Usually, a shaping filter is used to model timecorrelated errors. However, the commonly used shaping filter assume that the measurement noise vector at epoch <i>k</i> is not only correlated with the one from epoch <i>k</i> – 1 but also with the ones before epoch <i>k</i> – 1 . The shaping filter presented in this paper uses Cholesky factors under the assumption that the measurement noise vector is pairwise time-correlated i.e. the measurement noise are only correlated with the ones from previous epoch. Simulation results show that the new algorithm performs better than the existing algorithms and is optimal.
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Svensson, A., and J. Holst. "Integration of Navigation Data." Journal of Navigation 48, no. 1 (January 1995): 114–35. http://dx.doi.org/10.1017/s0373463300012558.
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This article treats integration of navigation data from a variety of sensors in a submarine using extended Kalman filtering in order to improve the accuracy of position, velocity and heading estimates. The problem has been restricted to planar motion. The measurement system consists of an inertial navigation system, a gyro compass, a passive log, an active log and a satellite navigation system. These subsystems are briefly described and models for the measurement errors are given.Four different extended Kalman filters have been tested by computer simulations. The simulations distinctly show that the passive subsystems alone are insufficient to improve the estimate of the position obtained from the inertial navigation system. A log measuring the velocity relative to the ground or a position determining system are needed. The improvement depends on the accuracy of the measuring instruments, the extent of time the instrument can be used and which filter is being used. The most complex filter, which contains fourteen states, eight to describe the motion of the submarine and six to describe the measurement system, including a model of the inertial navigation system, works very well.
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Wang, Qi, Cheng Shan Qian, Zi Jia Zhang, and Chang Song Yang. "Application of Federated Filter to AUV Based on Terrain-Aided SINS." Applied Mechanics and Materials 711 (December 2014): 338–41. http://dx.doi.org/10.4028/www.scientific.net/amm.711.338.
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To improve the navigation precision and reliability of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Federated Filter (FF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and Federated Filtering method is applied to the information fusion. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional Kalman filtering methods. The experiment results suggest that the Federated Filtering method is able to improve the long-time navigation precision and reliability, relative to the traditional Kalman Filtering method.
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Hide, Christopher, Terry Moore, and Martin Smith. "Adaptive Kalman Filtering for Low-cost INS/GPS." Journal of Navigation 56, no. 1 (January 2003): 143–52. http://dx.doi.org/10.1017/s0373463302002151.
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GPS and low-cost INS sensors are widely used for positioning and attitude determination applications. Low-cost inertial sensors exhibit large errors that can be compensated using position and velocity updates from GPS. Combining both sensors using a Kalman filter provides high-accuracy, real-time navigation. A conventional Kalman filter relies on the correct definition of the measurement and process noise matrices, which are generally defined a priori and remain fixed throughout the processing run. Adaptive Kalman filtering techniques use the residual sequences to adapt the stochastic properties of the filter on line to correspond to the temporal dependence of the errors involved. This paper examines the use of three adaptive filtering techniques. These are artificially scaling the predicted Kalman filter covariance, the Adaptive Kalman Filter and Multiple Model Adaptive Estimation. The algorithms are tested with the GPS and inertial data simulation software. A trajectory taken from a real marine trial is used to test the dynamic alignment of the inertial sensor errors. Results show that on line estimation of the stochastic properties of the inertial system can significantly improve the speed of the dynamic alignment and potentially improve the overall navigation accuracy and integrity.
11
Chen, Xiyuan, Yuan Xu, and Qinghua Li. "Application of Adaptive Extended Kalman Smoothing on INS/WSN Integration System for Mobile Robot Indoors." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/130508.
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The inertial navigation systems (INS)/wireless sensor network (WSN) integration system for mobile robot is proposed for navigation information indoors accurately and continuously. The Kalman filter (KF) is widely used for real-time applications with the aim of gaining optimal data fusion. In order to improve the accuracy of the navigation information, this work proposed an adaptive extended Kalman smoothing (AEKS) which utilizes inertial measuring units (IMUs) and ultrasonic positioning system. In this mode, the adaptive extended Kalman filter (AEKF) is used to improve the accuracy of forward Kalman filtering (FKF) and backward Kalman filtering (BKF), and then the AEKS and the average filter are used between two output timings for the online smoothing. Several real indoor tests are done to assess the performance of the proposed method. The results show that the proposed method can reduce the error compared with the INS-only, least squares (LS) solution, and AEKF.
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Song, Lijun, Zhongxing Duan, Bo He, and Zhe Li. "Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment." Complexity 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/3039061.
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The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.
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Xu, Yuan, and Xiyuan Chen. "Online cubature Kalman filter Rauch–Tung–Striebel smoothing for indoor inertial navigation system/ultrawideband integrated pedestrian navigation." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 232, no. 4 (May 31, 2017): 390–98. http://dx.doi.org/10.1177/0959651817711627.
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Accurate position information of the pedestrians is required in many applications such as healthcare, entertainment industries, and military field. In this work, an online Cubature Kalman filter Rauch–Tung–Striebel smoothing algorithm for people’s location in indoor environment is proposed using inertial navigation system techniques with ultrawideband technology. In this algorithm, Cubature Kalman filter is employed to improve the filtering output accuracy; then, the Rauch–Tung–Striebel smoothing is used between the ultrawideband measurements updates; finally, the average value of the corrected inertial navigation system error estimation is output to compensate the inertial navigation system position error. Moreover, a real indoor test has been done for assessing the performance of the proposed model and algorithm. Test results show that the proposed model is able to reduce the sum of the absolute position error between the east direction and the north direction by about 32% compared with only the ultrawideband model, and the performance of the online Cubature Kalman filter Rauch–Tung–Striebel smoothing algorithm is slightly better than the off-line mode.
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Wan, Miao, Zhongbin Wang, Lei Si, Chao Tan, and Hao Wang. "An Initial Alignment Technology of Shearer Inertial Navigation Positioning Based on a Fruit Fly-Optimized Kalman Filter Algorithm." Computational Intelligence and Neuroscience 2020 (October 13, 2020): 1–15. http://dx.doi.org/10.1155/2020/8876918.
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The shearer is one of the core equipment of the fully mechanized coal face. The fast and accurate positioning of the shearer is the prerequisite for its memory cutting, intelligent height adjustment, and intelligent speed adjustment. Inertial navigation technology has many advantages such as strong autonomy, good concealment, and high reliability. The accurate positioning of the shearer based on inertial navigation can not only determine its operating position but also measure the direction of movement. However, when inertial navigation is used to locate the shearer in motion, the cumulative errors will occur, resulting in inaccurate positioning of the shearer. The accuracy of the initial alignment is directly related to the working precision of the inertial navigation system. In order to improve the efficiency and accuracy of initial alignment, an improved initial alignment method is proposed in this paper, which uses a fruit fly-optimized Kalman filter algorithm for initial alignment. In order to improve the filtering performance, the fruit fly-optimized Kalman filter algorithm uses an improved fruit fly algorithm to realize the adaptive optimization of system noise variance. Finally, simulation and experiments verify the effectiveness of the fruit fly-optimized Kalman filter algorithm.
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Liu, You Ming, Tian Zhen Long Song, Kuan Li, Hong Wen Lin, and Ting Jun Li. "Application of Kalman Filtering Techniques and Simulation Analysis." Applied Mechanics and Materials 556-562 (May 2014): 4526–29. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4526.
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The principles and methods of their combined mode SINS/celestial navigation, and applying the theory of error state Kalman filter integrated navigation systems were analyzed. Derivation of a new measurement equation, written Kalman filtering process filter analysis was carried out to prove that the model has a good combination of inertial devices drift suppression effect.
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Ushaq, Muhammad, and Jian Cheng Fang. "An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems." Applied Mechanics and Materials 245 (December 2012): 323–29. http://dx.doi.org/10.4028/www.scientific.net/amm.245.323.
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Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.
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Asl, Habib Ghanbarpour, and Abbas Dehghani Firouzabadi. "Integration of the inertial navigation system with consecutive images of a camera by relative position and attitude updating." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 15 (May 31, 2019): 5592–605. http://dx.doi.org/10.1177/0954410019852818.
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This paper introduces a new method for improving the inertial navigation system errors using information provided by the camera. An unscented Kalman filter is used for integrating the inertial measurement unit data with the features’ constraints extracted from the camera’s image. The constraints, in our approach, comprise epipolar geometry of two consecutive images with more than 65% coverage. Tracking down a known feature in two consecutive images results in emergence of stochastic epipolar constraint. It emerges in the form of an implicit measurement equation of the Kalman filter. Correctly matching features of the two images is necessary for reducing the navigation system errors because they act as external information for the inertial navigation system. A new method has been presented in this study based on the covariance analysis of the matched feature rays’ intersection points on the ground, which sieves the false matched features. Then, the inertial navigation system and matched feature information is integrated through the unscented Kalman filter filter and the states of the vehicle (attitude, position, and velocity) are corrected according to the last image. In this paper, the relative navigation parameters against the absolute one have been corrected. To avoid increasing dimensions of the covariance matrix, sequential updating procedure is used in the measurement equation. The simulation results show good performance of the proposed algorithm, which can be easily utilized for real flights.
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Lee, In Seong, Jae Yong Kim, Jun Ha Lee, Jung Min Kim, and Sung Sin Kim. "Kalman Filter-Based Sensor Fusion for Improving Localization of AGV." Advanced Materials Research 488-489 (March 2012): 1818–22. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.1818.
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This paper proposes localization using sensor fusion with a laser navigation and an inertial navigation system for indoor mobile. The laser navigation is a device that measures angle and distance between the robot and the reflectors. Although it is the high-precision device for indoor global positioning, there is a problem that the accuracy of laser navigation significantly drops while moving at high speed and rapid turning. To solve this problem, the laser navigation was fused to inertial navigation system through Kalman filter. For experiment, we use omnidirectional robot with Mecanum Wheels and analyze the positioning accuracy according to driving direction of the robot.
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Wang, Qi, Dong Li, Zi Jia Zhang, and Chang Song Yang. "Application of Improved Unscented Kalman Filter to AUV Based on Terrain-Aided Strapdown Inertial Navigation System." Applied Mechanics and Materials 389 (August 2013): 758–64. http://dx.doi.org/10.4028/www.scientific.net/amm.389.758.
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To improve the navigation precision of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Improved Unscented Kalman Filter (IUKF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and improved UKF method is applied to the information fusion. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional Kalman filtering methods. The experiment results suggest that the IUKF method is able to greatly improve the long-time navigation precision, relative to the traditional information fusion method.
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Awin, Otman Ali. "Application of Extended Kalman Filter Algorithm in SDINS/GPS Integrated Inertial Navigation System." Applied Mechanics and Materials 367 (August 2013): 528–35. http://dx.doi.org/10.4028/www.scientific.net/amm.367.528.
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This paper deals with the integrated navigation system based on fusion of data from Strap Down Inertial Navigation System (SDINS) and from Global Position System (GPS). In order to increase the accuracy and reliability of navigation algorithms, these two different systems are combined. The navigation system that be analyzed is basically of INS type while GPS corrective data are obtained less frequently and these are treated as noisy measurements in an extended Kalman filter scheme. The simulation of whole system (SDINS/GPS integrated system with Kalman filter) was modeled using MATLAB package, SIMULINK© tool. The proper choice of Kalman filter parameters had taken to minimize navigation errors for a typical medium range flight scenario (Simulated test trajectory and real trajectory of vehicle motion). A prototype of a SDINS installed on a moving platform in the laboratory to collected data by many experiments to verification our SIMULINK models.
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Ahmed, Haseeb, Ihsan Ullah, Uzair Khan, Muhammad Bilal Qureshi, Sajjad Manzoor, Nazeer Muhammad, Muhammad Usman Shahid Khan, and Raheel Nawaz. "Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory." Sensors 19, no. 24 (December 5, 2019): 5357. http://dx.doi.org/10.3390/s19245357.
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Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter.
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Zhao, Bin, Qinghua Zeng, Jianye Liu, Chunlei Gao, and Tianyu Zhao. "A new polar alignment algorithm based on the Huber estimation filter with the aid of BeiDou Navigation Satellite System." International Journal of Distributed Sensor Networks 17, no. 3 (March 2021): 155014772110041. http://dx.doi.org/10.1177/15501477211004115.
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For aircrafts equipped with BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated navigation system, BeiDou Navigation Satellite System information can be used to achieve autonomous alignment. However, due to the complex polar environment and multipath effect, BeiDou Navigation Satellite System measurement noise often exhibits a non-Gaussian distribution that will severely degrade the estimation accuracy of standard Kalman filter. To address this problem, a new polar alignment algorithm based on the Huber estimation filter is proposed in this article. Considering the special geographical conditions in the polar regions, the dynamic model and the measurement model of BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated alignment system in the grid frame are derived in this article. The BeiDou Navigation Satellite System measurement noise characteristics in the polar regions are analyzed and heavy-tailed characteristics are simulated, respectively. Since the estimation accuracy of standard Kalman filter can be severely degraded under non-Gaussian noise, a Kalman filter based on the Huber estimation is designed combining grid navigation system and generalized maximum likelihood estimation. The simulation and experiment results demonstrate that the proposed algorithm has better robustness under non-Gaussian noise, and it is effective in the polar regions. By employing the proposed algorithm, the rapidity and accuracy of the alignment process can be improved.
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Tian, Ming, Zhonghong Liang, Zhikun Liao, Ruihang Yu, Honggang Guo, and Lin Wang. "A Polar Robust Kalman Filter Algorithm for DVL-Aided SINSs Based on the Ellipsoidal Earth Model." Sensors 22, no. 20 (October 17, 2022): 7879. http://dx.doi.org/10.3390/s22207879.
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Autonomous underwater vehicles (AUVs) play an increasingly essential role in the field of polar ocean exploration, and the Doppler velocity log (DVL)-aided strapdown inertial navigation system (SINS) is widely used for it. Due to the rapid convergence of the meridians, traditional inertial navigation mechanisms fail in the polar region. To tackle this problem, a transverse inertial navigation mechanism based on the earth ellipsoidal model is designed in this paper. Influenced by the harsh environment of the polar regions, unknown and time-varying outlier noise appears in the output of DVL, which makes the performance of the standard Kalman filter degrade. To address this issue, a robust Kalman filter algorithm based on Mahalanobis distance is used to adaptively estimate measurement noise covariance; thus, the Kalman filter gain can be modified to weight the measurement. A trial ship experiment and semi-physical simulation experiment were carried out to verify the effectiveness of the proposed algorithm. The results demonstrate that the proposed algorithm can effectively resist the influence of DVL outliers and improve positioning accuracy.
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Ashokaraj, Immanuel, Antonios Tsourdos, Peter Silson, and Brian White. "SENSOR BASED ROBOT LOCALISATION AND NAVIGATION: USING INTERVAL ANALYSIS AND NONLINEAR KALMAN FILTERS." Transactions of the Canadian Society for Mechanical Engineering 29, no. 2 (June 2005): 211–27. http://dx.doi.org/10.1139/tcsme-2005-0014.
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Multiple sensor fusion for robot localisation and navigation has attracted a lot of interest in recent years. This paper describes a sensor based navigation and localisation approach for an autonomous mobile robot using an interval analysis (IA) based adaptive mechanism for the non-linear Kalman filter namely the Extended Kalman filter (EKF). The map used for this study is two-dimensional and assumed to be known a priori. The robot is equipped with inertial sensors (INS), encoders and ultrasonic sensors. A non-linear Kalman filter is used to estimate the robots position using the inertial sensors and encoders. The ultrasonic sensors use an Interval Analysis (IA) algorithm for guaranteed robot localisation. Since the Kalman Filter estimates may be affected by bias, drift etc. we propose an adaptive mechanism using IA to correct these defects in estimates. In the presence of landmarks the complementary interval robot position information from the IA algorithm with uniform distribution using ultrasonic sensors is used to estimate and bound the errors in the non-linear Kalman filter robot position estimate with a Gaussian distribution.
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Jiang, De Ning, and Tulu Muluneh Mekonnen. "INS/GPS/MNS Integrated Navigation System with Federated Kalman Filtering." Advanced Materials Research 718-720 (July 2013): 1207–12. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.1207.
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A multi-sensor integrated solution that combine complementary features of the Global Positioning System (GPS), inertial navigation system (INS), and magnetometer is presented due to GPS-aided inertial navigation system (INS) provides poor observability of heading angle. In addition, Based on the principle of federated Kalman filtering and Adaptive Extended Kalman Filter, the algorithm is presented also for accuracy of positioning and attitude, rapidity, and error tolerance of the navigation system. The algorithm is implemented in the integrated navigation system. Experimental results show that the observability issue is solved and improvement in accuracy.
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Rahimi, Hossein, and Amir Ali Nikkhah. "Improving the speed of initial alignment for marine strapdown inertial navigation systems using heading control signal feedback in extended Kalman filter." International Journal of Advanced Robotic Systems 17, no. 1 (January 1, 2020): 172988141989484. http://dx.doi.org/10.1177/1729881419894849.
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In this article, a method was proposed for strapdown inertial navigation systems initial alignment by drawing on the conventional alignment method for stable platform navigation systems. When a vessel is moored, the strapdown inertial navigation system contributes to the disturbing motion. Moreover, the conventional methods of accurate alignment fail to succeed within an acceptable period of time due to the slow convergence of the heading channel in the mooring conditions. In this work, the heading was adjusted using the velocity bias resulting from the component of the angular velocity of the Earth on the east channel on the strapdown inertial navigation systems analytic platform plane to accelerate convergence in the initial alignment of navigation system. To this end, an extended Kalman filter with control signal feedback was used. The heading error was calculated using the north channel residual velocity of the strapdown inertial navigation systems analytic platform plane and was entered into an extended Kalman filter. Simulation and turntable experimental tests were indicative of the ability of the proposed alignment method to increase heading converge speed in mooring conditions.
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Yushuai Zhang, Yushuai Zhang, Jianxin Guo Yushuai Zhang, Rui Zhu Jianxin Guo, Zhengyang Zhao Rui Zhu, Feng Wang Zhengyang Zhao, and Liping Wang Feng Wang. "Pedestrian Inertial Navigation with Building Floor Plans for Indoor Environments via Particle Filter." 電腦學刊 33, no. 3 (June 2022): 017–33. http://dx.doi.org/10.53106/199115992022063303002.
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<p>With the rapid development of smart city, the indoor positioning services became more and more important. During the existing solutions, inertial measurement unit (IMU) with pedestrian dead reckoning (PDR) was a promising scheme since they did not require external equipment in the environment. However, the orientation drift of low-cost IMU limited their application in practical. To address this problem, a zero-velocity update (ZUPT) framework included Kalman filter and particle filter is designed based on the foot-based low-cost IMU and digital floor plan to provide the service of personal navigation. In the designed Smoothing for ZUPT-aided INSs framework, the Kalman filter is used to estimate the position and attitude by zero velocity correction technique. Then, the particle filter is used to improve the localization and heading accuracy by map matching. The position estimation presented in this study achieves an average position error of 1.16 m. The experimental results show that the designed framework can solve the personal navigation problem in the case of building plan information assistance and help improve the accuracy and reliability of continuous position determination of personal navigation systems effectively. </p> <p> </p>
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Wang, Lin, Wenqi Wu, Guo Wei, Jinlong Li, and Ruihang Yu. "A Novel Information Fusion Method for Redundant Rotational Inertial Navigation Systems Based on Reduced-Order Kalman Filter." MATEC Web of Conferences 160 (2018): 07005. http://dx.doi.org/10.1051/matecconf/201816007005.
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The redundant rotational inertial navigation systems can satisfy not only the high-accuracy but also the high-reliability demands of underwater vehicle on navigation system. However, different systems are usually independent, and lack of information fusion. A reduced-order Kalman filter is designed to fuse the navigation information output of redundant rotational navigation systems which usually include a dual-axis rotational inertial navigation system being master system and a single-axis rotational inertial navigation system being hot-backup system. The azimuth gyro drift of single-axis rotational inertial navigation system can be estimated by the designed filter, whereby the position error caused by that can be compensated with the aid of designed position error prediction model. As a result, the improved performance of single-axis rotational inertial navigation system can guarantee the position accuracy in the case of dual-axis system failure. Semi-physical simulation and experiment verify the effectiveness of the proposed method.
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Ushaq, Muhammad, Fang Jian Cheng, and Ali Jamshaid. "A Fault Tolerant SINS/GPS/CNS Integrated Navigation Scheme Realized through Federated Kalman Filter." Applied Mechanics and Materials 332 (July 2013): 104–10. http://dx.doi.org/10.4028/www.scientific.net/amm.332.104.
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The complementary characteristics of the Strapdown Inertial Navigation System (SINS) and external non-inertial navigation aids like Global Positioning System (GPS) and Celestial Navigation System (CNS) make the integrated navigation system an appealing and cost effective solution for various applications. SINS exhibits position errors owing to errors in initialization of the inertial measurement unit (IMU) and the inherent accelerometer biases and gyroscope drifts. SINS also suffer from diverging azimuth errors and an exponentially increasing vertical channel error. Pitch and roll errors also exhibit unbounded growth with time. To mitigate this behavior of SINS, periodic corrections are opted for through measurements from external non-inertial navigation aids. These corrections can be in the form of position fixing, velocity fixing and attitude fixing from external aids like GPS, GLONASS (Russian Satellite Navigation System), BEIDU(Chinese Satellite Navigation System) and Celestial Navigation Systems (CNS) etc. In this research work GPS and CNS are used as external aids for SINS and the navigation solutions of all three systems (SINS, GPS and CNS) are fused using Federated Kalman Filter (FKF). The FKF differs from the conventional Central Kalman Filter (CKF) because each measurement is processed in Local Filters (LFs), and the results are combined in a Master Filter (MF). FKF is a partitioned estimation method that uses a two stage data processing scheme, in which the outputs of sensor related LFs are subsequently combined by a large MF. Each LF is dedicated to a separate sensor subsystem, and uses data from the common reference such as SINS. The SINS acts as a cardinal system in the combination, and its data is also available as measurement input for the master filter. In this research work, information from the GPS and the CNS are dedicated to the corresponding LFs. Each LF provides its solutions to the master filter all information is fused together forming a global solution. Simulation for the proposed architecture has validated the effectiveness of the scheme, by showing the substantial precision improvement in the solutions of position, velocity and attitude as compared to the pure SINS or any other standalone system.
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Zhao, Di, Huaming Qian, and Dingjie Xu. "Research on a mixed prediction method to vehicle integrated navigation systems." International Journal of Advanced Robotic Systems 16, no. 6 (November 1, 2019): 172988141988525. http://dx.doi.org/10.1177/1729881419885258.
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Aiming to improve the positioning accuracy of vehicle integrated navigation system (strapdown inertial navigation system/Global Positioning System) when Global Positioning System signal is blocked, a mixed prediction method combined with radial basis function neural network, time series analysis, and unscented Kalman filter algorithms is proposed. The method is composed by dual modes of radial basis function neural network training and prediction. When Global Positioning System works properly, radial basis function neural network and time series analysis are trained by the error between Global Positioning System and strapdown inertial navigation system. Furthermore, the predicted values of both radial basis function neural network and time series analysis are applied to unscented Kalman filter measurement updates during Global Positioning System outages. The performance of this method is verified by computer simulation. The simulation results indicated that the proposed method can provide higher positioning precision than unscented Kalman filter, especially when Global Positioning System signal temporary outages occur.
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Fokin, G., and A. Vladyko. "Vehicles Positioning with the Fusion of Time of Arrival, Angle of Arrival and Inertial Measurements in the Extended Kalman Filter." Proceedings of Telecommunication Universities 7, no. 2 (June 30, 2021): 51–67. http://dx.doi.org/10.31854/1813-324x-2021-7-2-51-67.
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This work is devoted to the study of models and methods for improving posi-tioning accuracy in ultra-dense V2X/5G radio access networks for vehicles during maneuvers by combining range and angle primary measurements with measurements of inertial navigation systems in the extended Kalman filter. Onboard platformless inertial navigation system is represented by three-axis accelerometer and gyroscope modules. Integration of primary inertial measurements of acceleration and angular velocity with primary radio measurements of range and angle is carried out by converting the inertial coordinate system of the accelerometer and gyroscope into coordinate system of vehicle using quaternions. Secondary processing of inertial and radio measurements is carried out in the extended Kalman filter. The integration results show an increase in the accuracy of estimating the trajectory of a vehicle from several meters to one meter when turning at an inter-section.
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Jiang, Chen, Shu-bi Zhang, and Qiu-zhao Zhang. "A Novel Robust Interval Kalman Filter Algorithm for GPS/INS Integrated Navigation." Journal of Sensors 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/3727241.
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Kalman filter is widely applied in data fusion of dynamic systems under the assumption that the system and measurement noises are Gaussian distributed. In literature, the interval Kalman filter was proposed aiming at controlling the influences of the system model uncertainties. The robust Kalman filter has also been proposed to control the effects of outliers. In this paper, a new interval Kalman filter algorithm is proposed by integrating the robust estimation and the interval Kalman filter in which the system noise and the observation noise terms are considered simultaneously. The noise data reduction and the robust estimation methods are both introduced into the proposed interval Kalman filter algorithm. The new algorithm is equal to the standard Kalman filter in terms of computation, but superior for managing with outliers. The advantage of the proposed algorithm is demonstrated experimentally using the integrated navigation of Global Positioning System (GPS) and the Inertial Navigation System (INS).
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Liu, Ming, and Guobin Chang. "Gravity Matching Aided Inertial Navigation Technique Based on Marginal Robust Unscented Kalman Filter." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/592480.
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This paper is concerned with the topic of gravity matching aided inertial navigation technology using Kalman filter. The dynamic state space model for Kalman filter is constructed as follows: the error equation of the inertial navigation system is employed as the process equation while the local gravity model based on 9-point surface interpolation is employed as the observation equation. The unscented Kalman filter is employed to address the nonlinearity of the observation equation. The filter is refined in two ways as follows. The marginalization technique is employed to explore the conditionally linear substructure to reduce the computational load; specifically, the number of the needed sigma points is reduced from 15 to 5 after this technique is used. A robust technique based on Chi-square test is employed to make the filter insensitive to the uncertainties in the above constructed observation model. Numerical simulation is carried out, and the efficacy of the proposed method is validated by the simulation results.
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Davari, Narjes, Asghar Gholami, and Mohammad Shabani. "Multirate Adaptive Kalman Filter for Marine Integrated Navigation System." Journal of Navigation 70, no. 3 (December 21, 2016): 628–47. http://dx.doi.org/10.1017/s0373463316000801.
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In the conventional integrated navigation system, the statistical information of the process and measurement noises is considered constant. However, due to the changing dynamic environment and imperfect knowledge of the filter statistical information, the process and measurement covariance matrices are unknown and time-varying. In this paper, a multirate adaptive Kalman filter is proposed to improve the performance of the Error State Kalman Filter (ESKF) for a marine navigation system. The designed navigation system is composed of a strapdown inertial navigation system along with Doppler velocity log and inclinometer with different sampling rates. In the proposed filter, the conventional adaptive Kalman filter is modified by adaptively tuning the measurement covariance matrix of the auxiliary sensors that have varying sampling grates based on the innovation sequence. The performance of the proposed filter is evaluated using real measurements. Experimental results show that the average root mean square error of the position estimated by the proposed filter can be decreased by approximately 60% when compared to that of the ESKF.
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Ryu, Ji Hyoung, Ganduulga Gankhuyag, and Kil To Chong. "Navigation System Heading and Position Accuracy Improvement through GPS and INS Data Fusion." Journal of Sensors 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/7942963.
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Commercial navigation systems currently in use have reduced position and heading error but are usually quite expensive. It is proposed that extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) be used in the integration of a global positioning system (GPS) with an inertial navigation system (INS). GPS and INS individually exhibit large errors but they do complement each other by maximizing the advantage of each in calculating the heading angle and position through EKF and UKF. The proposed method was tested using low cost GPS, a cheap electronic compass (EC), and an inertial management unit (IMU) which provided accurate heading and position information, verifying the efficacy of the proposed algorithm.
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Xu, Tian Lai. "Adaptive Kalman Filter for INS/GPS Integrated Navigation System." Applied Mechanics and Materials 336-338 (July 2013): 332–35. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.332.
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Inertial Navigation System (INS) and Global Positioning System (GPS) are commonly integrated to overcomes each systems inadequacies and provide an accurate navigation solution. The integration of INS and GPS is usually achieved using a Kalman filter. The accuracy of INS/GPS deteriorates in condition that a priori information used in Kalman filter does not accord with the actual environmental conditions. To address this problem, an improved Sage-Husa filter is presented. In this method, the measurement noise characteristic is adjusted if and only if filtering abnormality exists, avoiding filter instability and reducing computational burden caused by adjusting noise characteristic too frequently in Sage-Husa filter. Simulations in INS/GPS integrated navigation showed improvement in positioning accuracy.
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Ran, Chang Yan, Xiang Hong Cheng, and Lei Wang. "SINS Alignment Using Velocity Matching and Simplified Cubature Kalman Filter." Applied Mechanics and Materials 615 (August 2014): 255–58. http://dx.doi.org/10.4028/www.scientific.net/amm.615.255.
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This paper proposes a novel nonlinear alignment method for the strapdown inertial navigation system (SINS). The method uses the simplified cubature Kalman filter (CKF) and velocity matching. The system equations are the navigation equation, the Euler angle differential equations and the inertial sensor measurement error models. The system equations are nonlinear, whereas the measurement equations are linear. The CKF is simplified under the condition of the linear measurement equations. The time updating cycle and measurement updating cycle are different. Finally inertial measurement unit experimental data are used to do alignment. The results show that the proposed method can effectively obtain an accurate initial attitude within a short time, thus also be considered a new and simple addition to the kit of solutions for SINS alignment.
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Xie, Lili, and Jiazhen Lu. "Optimisation-based Transfer Alignment and Calibration Method for Inertial Measurement Vector Integration Matching." Journal of Navigation 70, no. 3 (October 20, 2016): 595–606. http://dx.doi.org/10.1017/s0373463316000680.
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The traditional Kalman filtering-based transfer alignment methods largely depend on prior information for initialisation. However, the initialisation process is hard to fulfil on a moving base. In this paper, a type of inertial measurement vector integration matching for optimisation-based transfer alignment and calibration is proposed to estimate the misalignment between the Master Inertial Navigation System (MINS) and Slave Inertial Navigation System (SINS), and main inertial sensor error parameters of SINS, including bias and scale factor error. In contrast to filter techniques, the proposed method has the capability of self-initialisation and provides a new idea to solve the alignment and calibration problem. No prior information is needed. Moreover, the integration time interval for the matching inertial measurement vector is selected by considering both the observation degree of inertial sensor error parameters and the noise effect. Simulation results demonstrate that the proposed method has faster convergence and is more accurate than Kalman filter techniques.
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Shabani, Mohammad, and Asghar Gholami. "Improved Underwater Integrated Navigation System using Unscented Filtering Approach." Journal of Navigation 69, no. 3 (October 30, 2015): 561–81. http://dx.doi.org/10.1017/s0373463315000752.
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In underwater navigation, the conventional Error State Kalman Filter (ESKF) is used for combining navigation data where due to first order linearization of the nonlinear equations of the dynamics and measurements, considerable error is induced in estimated error state and covariance matrices. This paper presents an underwater integrated inertial navigation system using the unscented filter as an improved nonlinear version of the Kalman filter family. The designed system consists of a strap-down inertial navigation system accompanying Doppler velocity log and depth meter. In the proposed approach, to use the nonlinear capabilities of the unscented filtering approach the integrated navigation system is implemented in a direct approach where the nonlinear total state dynamic and and measurement models are utilised without any linearization. To our knowledge, no results have been reported in the literature on the experimental evaluation of the unscented-based integrated navigation system for underwater vehicles. The performance of the designed system is studied using real measurements. The results of the lake test show that the proposed system estimates the vehicle's position more accurately compared with the conventional ESKF structure.
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Mahboub, Vahid, and Dorsa Mohammadi. "A Constrained Total Extended Kalman Filter for Integrated Navigation." Journal of Navigation 71, no. 4 (February 26, 2018): 971–88. http://dx.doi.org/10.1017/s0373463318000012.
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In this contribution, an improved Extended Kalman Filter (EKF), named the Total Extended Kalman Filter (TEKF) is proposed for integrated navigation. It can consider the neglected random observed quantities which may appear in a dynamic model. In particular, this paper will consider the case of vision-based navigation. This algorithm is equipped with quadratic constraints and makes use of condition equations. The paper will show that the refined data from different sensors including a Global Positioning System (GPS) receiver, an Inertial Navigation System (INS) and remote sensors can be fused into a Constrained Total Extended Kalman Filter (CTEKF) algorithm. The CTEKF algorithm is applied to a case study in the Guilan province in the north of Iran. The results show the efficiency of the proposed algorithm.
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Awale, Vaibhav, and Hari B. Hablani. "Fusion of Redundant Aided-inertial Sensors with Decentralised Kalman Filter for Autonomous Underwater Vehicle Navigation." Defence Science Journal 65, no. 6 (November 10, 2015): 425. http://dx.doi.org/10.14429/dsj.65.8874.
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<p class="p1">Most submarines carry more than one set of inertial navigation system (INS) for redundancy and reliability. Apart from INS systems, the submarine carries other sensors that provide different navigation information. A major challenge is to combine these sensors and INS estimates in an optimal and robust manner for navigation. This issue has been addressed by Farrell1. The same approach is used in this paper to combine different sensor measurements along with INS system. However, since more than one INS system is available onboard, it would be better to use multiple INS systems at the same time to obtain a better estimate of states and to provide autonomy in the event of failure of one INS system. This would require us to combine the estimates obtained from local filters (one set of INS system integrated with external sensors), in some optimal way to provide a global estimate. Individual sensor and IMU measurements cannot be accessed in this scenario. Also, autonomous operation requires no sharing of information among local filters. Hence a decentralised Kalman filter approach is considered for combining the estimates of local filters to give a global estimate. This estimate would not be optimal, however. A better optimal estimate can be obtained by accessing individual measurements and augmenting the state vector in Kalman filter, but in that case, corruption of one INS system will lead to failure of the whole filter. Hence to ensure satisfactory performance of the filter even in the event of failure of some INS system, a decentralised Kalman filtering approach is considered.</p>
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Xiao, Ming, Liang Pan, Tianjiang Hu, and Lincheng Shen. "A Novel Fusion Scheme for Vision Aided Inertial Navigation of Aerial Vehicles." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/819565.
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Vision-aided inertial navigation is an important and practical mode of integrated navigation for aerial vehicles. In this paper, a novel fusion scheme is proposed and developed by using the information from inertial navigation system (INS) and vision matching subsystem. This scheme is different from the conventional Kalman filter (CKF); CKF treats these two information sources equally even though vision-aided navigation is linked to uncertainty and inaccuracy. Eventually, by concentrating on reliability of vision matching, the fusion scheme of integrated navigation is upgraded. Not only matching positions are used, but also their reliable extents are considered. Moreover, a fusion algorithm is designed and proved to be the optimal as it minimizes the variance in terms of mean square error estimation. Simulations are carried out to validate the effectiveness of this novel navigation fusion scheme. Results show the new fusion scheme outperforms CKF and adaptive Kalman filter (AKF) in vision/INS estimation under given scenarios and specifications.
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Navidi, Neda, René Jr Landry, Jianhua Cheng, and Denis Gingras. "A New Technique for Integrating MEMS-Based Low-Cost IMU and GPS in Vehicular Navigation." Journal of Sensors 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/5365983.
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In providing acceptable navigational solutions, Location-Based Services (LBS) in land navigation rely mostly on integration of Global Positioning System (GPS) and Inertial Navigation System (INS) measurements for accuracy and robustness. The GPS/INS integrated system can provide better land-navigation solutions than the ones any standalone system can provide. Low-cost Inertial Measurement Units (IMUs), based on Microelectromechanical Systems (MEMS) technology, revolutionized the land-navigation system by virtue of their low-cost miniaturization and widespread availability. However, their accuracy is strongly affected by their inherent systematic and stochastic errors, which depend mainly on environmental conditions. The environmental noise and nonlinearities prevent obtaining optimal localization estimates in Land Vehicular Navigation (LVN) while using traditional Kalman Filters (KF). The main goal of this paper is to effectively eliminate stochastic errors of MEMS-based IMUs. The proposed solution is divided into two main components: (1) improving noise cancellation, using advanced stochastic error models in MEMS-based IMUs based on combined Autoregressive Processes (ARP) and first-order Gauss-Markov Process (1GMP), and (2) modeling the low-cost GPS/INS integration, using a hybrid Fuzzy Inference System (FIS) and Second-Order Extended Kalman Filter (SOEKF). The results obtained show that the proposed methods perform better than the traditional techniques do in different stochastic and dynamic situations.
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Xiaoqian, Tang, Zhao Feicheng, Tang Zhengbing, and Wang Hongying. "Nonlinear Extended Kalman Filter for Attitude Estimation of the Fixed-Wing UAV." International Journal of Optics 2022 (February 1, 2022): 1–9. http://dx.doi.org/10.1155/2022/7883851.
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Flying vehicle’s navigation, direction, and control in real-time results in the design of a strap-down inertial navigation system (INS). The strategy results in low accuracy, performance with correctness. Aiming at the attitude estimation problem, many data fusion or filtering methods had been applied, which fail in many cases, which attains the nonlinear measurement model, process dynamics, and high navigation range. The main problem in unmanned aerial vehicles (UAVs) and flying vehicles is the determination of attitude angles. A novel attitude estimation algorithm is proposed in this study for the unmanned aerial vehicle (UAV). This research article designs two filtering algorithms for fixed-wing UAVs which are nonlinear for the attitude estimation. The filters are based on Kalman filters. The unscented Kalman filter (UKF) and cubature Kalman filter (CKF) were designed with different parameterizations of attitude, i.e., Euler angle (EA) and INS/unit quaternion (UQ) simultaneously. These filters, EA-UKF and INS-CKF, use the nonlinear process and measurement model. The computational results show that among both filters, the CKF attains a high accuracy, robustness, and estimation for the attitude estimation of the fixed-wing UAV.
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Ashraf, Shahrukh, Priyanka Aggarwal, Praveen Damacharla, Hong Wang, Ahmad Y. Javaid, and Vijay Devabhaktuni. "A low-cost solution for unmanned aerial vehicle navigation in a global positioning system–denied environment." International Journal of Distributed Sensor Networks 14, no. 6 (June 2018): 155014771878175. http://dx.doi.org/10.1177/1550147718781750.
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The ability of an autonomous unmanned aerial vehicle to navigate and fly precisely determines its utility and performance. The current navigation systems are highly dependent on the global positioning system and are prone to error because of global positioning system signal outages. However, advancements in onboard processing have enabled inertial navigation algorithms to perform well during short global positioning system outages. In this article, we propose an intelligent optical flow–based algorithm combined with Kalman filters to provide the navigation capability during global positioning system outages and global positioning system–denied environments. Traditional optical flow measurement uses block matching for motion vector calculation that makes the measurement task computationally expensive and slow. We propose the application of an artificial bee colony–based block matching technique for faster optical flow measurements. To effectively fuse optical flow data with inertial sensors output, we employ a modified form of extended Kalman filter. The modifications make the filter less noisy by utilizing the redundancy of sensors. We have achieved an accuracy of ~95% for all non-global positioning system navigation during our simulation studies. Our real-world experiments are in agreement with the simulation studies when effects of wind are taken into consideration.
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Pittet, Sylvain, Valérie Renaudin, Bertrand Merminod, and Michel Kasser. "UWB and MEMS Based Indoor Navigation." Journal of Navigation 61, no. 3 (June 26, 2008): 369–84. http://dx.doi.org/10.1017/s0373463308004797.
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Thanks to its physical characteristics, Ultra-wideband (UWB) is one of the most promising technologies for indoor pedestrian navigation. UWB radio localisation systems however experience multipath phenomena that decrease the precision and the reliability of the user's location. To cope with complex indoor environments, UWB radio signals are coupled with inertial measurements from Micro Electro Mechanical Sensors (MEMS) in an extended Kalman filter. Improved performances of the filter are presented and compared with reference trajectories and with pure inertial solutions. Only specific selection methods enable this improvement by detecting and removing outliers in the raw localisation data.
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Ding, Zhi Jian, Hong Cai, Hua Bo Yang, and Yuan Cao. "Attitude Matching for Gimbaled Inertial Navigation System Transfer Alignment." Applied Mechanics and Materials 190-191 (July 2012): 768–73. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.768.
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Abstract: Aiming at transfer alignment of gimbaled INS(Inertial Navigation Systems) on moving base, the paper proposes an attitude matching alignment model to calibrate the slave platform. This method is achieved by applying a Kalman filter, which based on the frame angle error equations, to estimate the fixed misalignment angle and obtain the misalignment angle. Firstly, the frame dynamics equations are introduced and the relation between the fixed angle and misalignment angle is discussed. Secondly, the frame angular error differential equations are built up via the frame angle information from the master and the slave INS platform. Lastly, the attitude matching alignment model is designed based on Kalman filter technology. The simulation results show that the proposed method can obtain an alignment accuracy of 40", and the corresponding alignment time is 30 seconds.
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Shang, Song Tian, and Wen Shao Gao. "Application of Adaptive Kalman Filter Technique in Initial Alignment of Single-Axial Rotation Strap-Down Inertial Navigation System." Advanced Materials Research 466-467 (February 2012): 617–21. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.617.
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In order to improve the accuracy of initial alignment which determines the accuracy of navigation, a Sage-Husa adaptive kalman filter algorithm is applied to SINS initial alignment of single-axis rotation system. The simulation result further shows that in the case of inaccurate statistical property of noise, the estimation accuracy of Sage-Husa adaptive kalman filter is better than the conventional kalman filter.
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Vepa, Ranjan, and Amzari Zhahir. "High-Precision Kinematic Satellite and Doppler Aided Inertial Navigation System." Journal of Navigation 64, no. 1 (November 26, 2010): 91–108. http://dx.doi.org/10.1017/s0373463310000329.
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In this paper an adaptive unscented Kalman filter based mixing filter is used to develop a high-precision kinematic satellite aided inertial navigation system with a modern receiver that incorporates carrier phase smoothing and ambiguity resolution. Using carrier phase measurements with multiple antennas, in addition to a set of typical pseudo-range estimates that can be obtained from a satellite navigation system such as GPS or GLONASS, the feasibility of generating high precision estimates of the typical outputs from an inertial navigation system is demonstrated. The methodology may be developed as a stand-alone system or employed in conjunction with a traditional strapped down inertial navigation system for purposes of initial alignment. Moreover the feasibility of employing adaptive mixing facilitates the possibility of using the system in an interoperable fashion with satellite navigation measurements.
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Chen, Mingxing, Zhi Xiong, Jun Xiong, and Rong Wang. "A hybrid cooperative navigation method for UAV swarm based on factor graph and Kalman filter." International Journal of Distributed Sensor Networks 18, no. 1 (January 2022): 155014772110647. http://dx.doi.org/10.1177/15501477211064758.
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Navigation plays an important role in the task execution of the micro-unmanned aerial vehicle (UAV) swarm. The Cooperative Navigation (CN) method that fuses the observation of onboard sensors and relative information between UAVs is a research hotspot. Aiming at the efficiency and accuracy problems of previous studies, this article proposes a hybrid-CN method for UAV swarm based on Factor Graph and Kalman filter. A global Factor Graph is used to combine Global Navigation Satellite System (GNSS) and ranging information to provide position estimations for modifying the distributed Kalman filter; distributed Kalman filter is established on each UAV to fuse inertial information and optimized position estimation to modify the navigation states. In order to provide time-consistent GNSS position information for the Factor Graph, a time synchronization filter is designed. The proposed method is tested and verified using standard Monte Carlo simulations, simulation results show that it can provide a more precise and efficient CN solution than traditional CN methods.