Error Correction Capability of GPS/INS Integrated Navigation System for Guided Rockets

2013 ◽  
Vol 325-326 ◽  
pp. 1053-1057
Author(s):  
Wei Wei Bian ◽  
Liang Ming Wang ◽  
Chuan Bing Ding ◽  
Yang Zhong
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Measurement Information ◽  
Attitude Measurement ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Error Correction Capability ◽  
Trajectory Simulation ◽  
Simulation Results ◽  
Navigation Method

In order to improve the guidance accuracy of long-range rockets, a GPS/INS integrated navigation method with combination of position, velocity and attitude was applied. The GPS/INS integrated navigation system taking the position and velocity from INS and attitude from GPS as observables was studied. The error model of system was established and the Kalman filter was designed. A 6-DOF trajectory simulation was put forward and the correction capability of the INS measurement error by using GPS attitude measurement information was analyzed. The simulation results verify the feasibility and effectiveness of the integrated navigation method.

Kalman/Particle Filter for Integrated Navigation System

2013 ◽  
Vol 756-759 ◽  
pp. 2142-2146 ◽  
Author(s):  
Zhun Jiao ◽  
Rong Zhang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Navigation System ◽  
The Other ◽  
New Method ◽  
Integrated Navigation ◽  
Computation Complexity ◽  
Integrated Navigation System ◽  
Simulation Results

Particle filter is introduced. Since the particle filter would bring hard computation, a new Kalman/Particle mixed filter used on SINS/GPS integrated navigation system was proposed. The new method divides the system into two sub-models, one is linear, the other one is nonlinear, and then implement Kalman filter and particle filter separately. The simulation results show that their performance is almost equal, but the computation complexity of the Kalman/particle filter is much lower than traditional particle filter.

Transfer Alignment for MEMS Integrated Navigation System Based on H∞ Filter

2014 ◽  
Vol 490-491 ◽  
pp. 886-890
Author(s):  
Xing Zhi Zhang ◽  
Kun Peng He ◽  
Chen Yang Wang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Kalman Filters ◽  
Inertial Navigation System ◽  
Digital Simulation ◽  
Integrated Navigation ◽  
Misalignment Angle ◽  
Transfer Alignment ◽  
Integrated Navigation System ◽  
Simulation Results

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.

scholarly journals Indoor Vision/INS Integrated Mobile Robot Navigation Using Multimodel-Based Multifrequency Kalman Filter

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yuan Xu ◽  
Tongqian Liu ◽  
Bin Sun ◽  
Yong Zhang ◽  
Siamak Khatibi ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Navigation System ◽  
Robot Navigation ◽  
Visual Navigation ◽  
Mobile Robot Navigation ◽  
Integrated Navigation ◽  
Position Information ◽  
Integrated Navigation System ◽  
Navigation Method

In order to further improve positioning accuracy, this paper proposes an indoor vision/INS integrated mobile robot navigation method using multimodel-based multifrequency Kalman filter. Firstly, to overcome the insufficient accuracy of visual data when a robot turns, a novel multimodel integrated scheme has been investigated for the mobile robots with Mecanum wheels which can make fixed point angled turns. Secondly, a multifrequency Kalman filter has been used to fuse the position information from both the inertial navigation system and the visual navigation system, which overcomes the problem that the filtering period of the integrated navigation system is too long. The proposed multimodel multifrequency Kalman filter gives the root mean square error (RMSE) of 0.0184 m in the direction of east and 0.0977 m in north, respectively. The RMSE of visual navigation system is 0.8925 m in the direction of east and 0.9539 m in north, respectively. Experimental results show that the proposed method is effective.

Model aided airborne integrated navigation system based on an improved square-root unscented H∞ filter

2018 ◽  
Vol 41 (5) ◽  
pp. 1290-1300
Author(s):  
Jieliang Shen ◽  
Yan Su ◽  
Qing Liang ◽  
Xinhua Zhu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Unscented Kalman Filter ◽  
Statistical Characteristics ◽  
Small Scale ◽  
Strong Nonlinearity ◽  
Square Root ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Integrated Navigation System

An inertial navigation system (INS) aided with an aircraft dynamic model (ADM) is developed as a novel airborne integrated navigation system, coping with the absence of a global navigation satellite system. To overcome the shortcomings of the conventional linear integration of INS/ADM based on an extended Kalman filter, a nonlinear integration method is proposed. Fast-update ADM makes it possible to utilize a direct filtering method, which employs nonlinear INS mechanics as system equations and a nonlinear ADM as observation equations, substituting the indirect filtering based on linear error equations. The strong nonlinearity generally calls for an unscented Kalman filter to accomplish the fusion process. Dealing with the model uncertainty, the inaccurate statistical characteristics of the noise and the potential nonpositive definiteness of the covariance matrix, an improved square-root unscented H∞ filter (ISRUHF) is derived in the paper, in which the robust factor [Formula: see text] is further expanded into a diagonal matrix [Formula: see text], to improve the accuracy and robustness of the integrated navigation system. Corresponding simulations as well as real flight tests based on a small-scale fixed-wing aircraft are operated and ISRUHF shows superiority compared with the commonly used fusion algorithm.

scholarly journals The Design and Implementation of an Inertial GNSS Odometer Integrated Navigation System Based on a Federated Kalman Filter for High-Speed Railway Track Inspection

2021 ◽  
Vol 11 (11) ◽  
pp. 5244
Author(s):  
Xinchun Zhang ◽  
Ximin Cui ◽  
Bo Huang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
High Speed ◽  
Measurement Accuracy ◽  
Railway Track ◽  
Filter Method ◽  
Integrated Navigation ◽  
High Speed Railway ◽  
Track Geometry ◽  
Integrated Navigation System

The detection of track geometry parameters is essential for the safety of high-speed railway operation. To improve the accuracy and efficiency of the state detector of track geometry parameters, in this study we propose an inertial GNSS odometer integrated navigation system based on the federated Kalman, and a corresponding inertial track measurement system was also developed. This paper systematically introduces the construction process for the Kalman filter and data smoothing algorithm based on forward filtering and reverse smoothing. The engineering results show that the measurement accuracy of the track geometry parameters was better than 0.2 mm, and the detection speed was about 3 km/h. Thus, compared with the traditional Kalman filter method, the proposed design improved the measurement accuracy and met the requirements for the detection of geometric parameters of high-speed railway tracks.

Studies of the integrated navigation system correction in the absence of a satellite signal

2021 ◽  
Author(s):  
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Operation Mode ◽  
Satellite System ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Correction Signal ◽  
Satellite Signal ◽  
Navigation Information

The schemes of navigation systems correction are considered. The operation mode of the aircraft during navigation is analyzed. An adaptive modification of the linear Kalman filter is used to correct the navigation information. An algorithm for predicting a correction signal based on a neural network in the event of a loss of a SNS correction signal is formed. Experimental results show the effectiveness of the algorithm. Keywords aircraft; inertial navigation system; satellite system; Kalman filter; neural networks; genetic algorithm

scholarly journals A New Robust Kalman Filter for SINS/DVL Integrated Navigation System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 51386-51395 ◽  
Author(s):  
Li Luo ◽  
Yonggang Zhang ◽  
Tao Fang ◽  
Ning Li
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Robust Kalman Filter

scholarly journals A SINS/BDS Integrated Navigation Method Based on Classified Weighted Adaptive Filtering

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Xuchao Kang ◽  
Guangjun He ◽  
Xingge Li
Keyword(s):  
Adaptive Filtering ◽  
Navigation System ◽  
Fault Tolerant ◽  
Hypothesis Test ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Covariance Matching ◽  
Uncertain Model ◽  
Navigation Accuracy ◽  
Navigation Method

Aiming at the problem that the accuracy and stability of SINS/BDS integrated navigation system decrease due to uncertain model and observation anomalies, a SINS/BDS integrated navigation method based on classified weighted adaptive filtering is proposed. Firstly, the innovation covariance matching technology is used to detect whether there is any abnormality in the system as a whole. Then the types of anomalies are distinguished by hypothesis test. Different types of anomalies have different effects on state estimation. Based on the dynamic changes of innovation, different adaptive weighting methods are adopted to correct navigation information. The simulation results show that this method can effectively improve the fault-tolerant performance of integrated navigation system in complex environment with unknown anomaly types. When both model anomalies and observation anomalies exist, the speed and position accuracy are increased by 42% and 24% compared with the standard KF, 38% and 22% compared with the innovation orthogonal adaptive filtering, which has higher navigation accuracy.

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