Multiple model adaptive estimation algorithm for SINS/CNS integrated navigation system

2015 ◽  
Author(s):  
Fangfang Zhao ◽  
Guangqiong Zhao ◽  
Shuangfei Fan ◽  
Zhongliang Tang ◽  
Wei He
Keyword(s):  
Navigation System ◽  
Adaptive Estimation ◽  
Estimation Algorithm ◽  
Integrated Navigation ◽  
Multiple Model ◽  
Integrated Navigation System ◽  
Multiple Model Adaptive Estimation

scholarly journals Multiple Model Adaptive Rank Estimation for Integrated Navigation During Mars Entry

2016 ◽  
Vol 70 (2) ◽  
pp. 291-308 ◽  
Author(s):  
Qiang Xiao ◽  
Huimin Fu ◽  
Zhihua Wang ◽  
Yongbo Zhang
Keyword(s):  
Navigation System ◽  
Uncertain System ◽  
Measurement Unit ◽  
Filter Method ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Multiple Model ◽  
Integrated Navigation System ◽  
Rank Estimation ◽  
Three Degree Of Freedom

Accurate navigation systems are required for future pinpoint Mars landing missions. A radio ranging augmented Inertial Measurement Unit (IMU) integrated navigation system concept is considered for the Mars entry navigation. The uncertain system parameters associated with the Three Degree-Of-Freedom (3-DOF) dynamic model, and the measurement systematic errors are considered. In order to improve entry navigation accuracy, this paper presents the Multiple Model Adaptive Rank Estimation (MMARE) filter of radio beacons/IMU integrated navigation system. 3-DOF simulation results show that the performances of the proposed navigation filter method, 70·39 m estimated altitude error and 15·74 m/s estimated velocity error, fulfill the need of future pinpoint Mars landing missions.

Generalized Multiple-Model Adaptive Estimation for Mars Entry Navigation

2011 ◽  
Vol 383-390 ◽  
pp. 1190-1194 ◽  
Author(s):  
Kai Wu
Keyword(s):  
Navigation System ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Dead Reckoning ◽  
Density Model ◽  
Multiple Model ◽  
Atmospheric Density ◽  
Multiple Model Adaptive Estimation ◽  
Precision Landing ◽  
Navigation Method

In order to satisfy the needs of Mars precision landing, a Mars entry navigation method is proposed for the problem of Mars atmospheric density model uncertainty. The navigation system processes accelerometer outputs as measurements, employs an extended Kalman filter bank regulated by the generalized multiple-model adaptive estimation method. Simulation results demonstrate the navigation system can identify the real atmospheric density model automatically, and show adaptivity and robustness to the uncertainty of atmospheric density. The navigation performance is greatly improved compared with traditional dead-reckoning

Federated IMM-UKF Algorithm for Multi-Sensor Data Fusion

2013 ◽  
Vol 753-755 ◽  
pp. 2117-2120 ◽  
Author(s):  
Tian Lai Xu
Keyword(s):  
Data Fusion ◽  
Nonlinear Filtering ◽  
Adaptive Estimation ◽  
Estimation Method ◽  
Cost Effective ◽  
Estimation Algorithm ◽  
Sensor Data ◽  
Interacting Multiple Model ◽  
Integrated Navigation ◽  
Multiple Model

The accuracy of multi-sensor navigational data fusion by federated Kalman filter will be reduced in condition that the systems dynamics model is nonlinear and the noise statistical properties are unknown. To address this problem, a federated Interacting Multiple Model-Unscented Kalman Filteing (IMM-UKF) algorithm is presented. The UKF is a nonlinear estimation method which can achieve the accuracy at least to the second-order. The IMM estimation algorithm is one of the cost-effective adaptive estimation algorithm for systems involving parametric changes. The combination of IMM with UKF could deal with the problem of nonlinear filtering with uncertain noise. Simulation results show that the method can improve the accuracy of INS/GPS/odometer integrated navigation.

scholarly journals Simulation Research on Gravity-Geomagnetism Combined Aided Underwater Navigation

2012 ◽  
Vol 66 (1) ◽  
pp. 83-98 ◽  
Author(s):  
Hui Zheng ◽  
Hubiao Wang ◽  
Lin Wu ◽  
Hua Chai ◽  
Yong Wang
Keyword(s):  
Navigation System ◽  
Adaptive Estimation ◽  
Autonomous Underwater Vehicles ◽  
Weighted Average ◽  
Large Field ◽  
Geomagnetic Variation ◽  
Multiple Model ◽  
Simulation Research ◽  
Multiple Model Adaptive Estimation ◽  
Field Feature

Gravity Aided Navigation (GravAN) and Geomagnetism Aided Navigation (GeomAN) are two methods for correcting Inertial Navigation System (INS) errors of Autonomous Underwater Vehicles (AUVs) without compromising the AUV mission. One requirement for applying these methods is the relatively large field feature variations along the navigation trajectory. But in some regions with small gravity or geomagnetic variation, it is very difficult to achieve a reliable result solely by GravAN or GeomAN. If these two methods were combined, gravity and geomagnetism information could be complementary and the aided navigation ability could potentially be improved, especially in those regions when neither method is valid. Based on that concept, a Gravity and Geomagnetism Combined Aided Navigation (GGCAN) method is consequently proposed in this paper as a possible solution. The Gravity Anomaly Grid (GAG2) and Earth Geomagnetic Anomaly Grid (EMAG2) are utilized as the background databases, and then a Multiple Model Adaptive Estimation (MMAE) is adopted to obtain an optimal estimated navigation position. Furthermore, an Optimal Weight Allocation Principle (OWAP) is introduced to the combined GravAN and GeomAN methods, together with a weighted average. In simulation, two special regions in the Western Pacific Ocean were chosen to test the proposed method. The results show that GGCAN can improve the position success rate and reduce the error, compared to GravAN or GeomAN. Results indicate that the GGCAN method proposed in this study is able to improve the accuracy and reliability of an aided navigation system.

scholarly journals Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System

2018 ◽  
Vol 8 (9) ◽  
pp. 1682 ◽  
Author(s):  
Chuang Zhang ◽  
Chen Guo ◽  
Daheng Zhang
Keyword(s):  
Navigation System ◽  
Integrated System ◽  
Filter Method ◽  
Integration Scheme ◽  
Interacting Multiple Model ◽  
Integrated Navigation ◽  
Multiple Model ◽  
Integrated Navigation System ◽  
Comparison Results ◽  
On Line

The extended Kalman filter (EKF) as a primary integration scheme has been applied in the Global Positioning System (GPS) and inertial navigation system (INS) integrated system. Nevertheless, the inherent drawbacks of EKF contain not only instability caused by linearization, but also massive calculation of Jacobian matrix. To cope with this problem, the adaptive interacting multiple model (AIMM) filter method is proposed to enhance navigation performance. The soft-switching characteristic, which is provided by interacting multiple model algorithm, permits process noise to be converted between upper and lower limits, and the measurement covariance is regulated by Sage adaptive filtering on-line Moreover, since the pseudo-range and Doppler observations need to be updated, an updating policy for classified measurement is considered. Finally, the performance of the GPS/INS integration method on the basis of AIMM is evaluated by a real ship, and comparison results demonstrate that AIMM could achieve a more position accuracy.

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