An Improved Interacting Multiple Model Algorithm for INS/DVL Integrated Navigation System

2020 ◽  
Author(s):  
Lanhua Hou ◽  
Xiaosu Xu ◽  
Yiqing Yao ◽  
Di Wang
Keyword(s):  
Navigation System ◽  
Interacting Multiple Model ◽  
Integrated Navigation ◽  
Multiple Model ◽  
Integrated Navigation System ◽  
Model Algorithm

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.

scholarly journals Robust Interacting Multiple Model Unscented Particle Filter for Navigation

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Li Xue ◽  
Yulan Han ◽  
Chunning Na
Keyword(s):  
Particle Filter ◽  
Navigation System ◽  
Measurement Information ◽  
Interacting Multiple Model ◽  
Integrated Navigation ◽  
Multiple Model ◽  
Integrated Navigation System ◽  
Filter Performance ◽  
Unscented Particle Filter ◽  
Particle Filter Algorithm

In order to solve the problems of particle degradation and difficulty in selecting importance density function in particle filter algorithm, a robust interacting multiple model unscented particle filter algorithm is presented, which is based on the advantages of interacting multiple model and particle filter algorithms. This algorithm can use the unscented transformation to get the particles that contain the latest measurement information of each model and calculate the robust equivalent weight function. This robust factor is designed to adjust the estimation and variance, and the important distribution function adaptively obtained is closer to the true distribution. Then, the particles weights can be flexibly adjusted in real time by using Euclidean distance to improve the computational efficiency during the resampling process. In addition, this filter process can comprehensively describe the uncertainty of the statistics characteristic of observation noise between different models. The diversity of available particles is increased, and the filter precision is improved. The proposed algorithm is applied to the SINS/GPS integrated navigation system, and the simulation analysis results demonstrate that the algorithm can effectively improve the filter performance and the calculation precision in positioning of integrated navigation system; thus, it provides a new method for nonlinear model filter.

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.

scholarly journals Adaptive Federated IMM Filter for AUV Integrated Navigation Systems

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6806
Author(s):  
Weiwei Lyu ◽  
Xianghong Cheng ◽  
Jinling Wang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Autonomous Underwater Vehicles ◽  
Local System ◽  
Doppler Velocity ◽  
Integrated Navigation ◽  
Multiple Model ◽  
Integrated Navigation System ◽  
Measurement Models ◽  
Federated Kalman Filter

High accuracy and reliable navigation in the underwater environment is very critical for the operations of autonomous underwater vehicles (AUVs). This paper proposes an adaptive federated interacting multiple model (IMM) filter, which combines adaptive federated filter and IMM algorithm for AUV in complex underwater environments. Based on the performance of each local system, the information sharing coefficient of the adaptive federated IMM filter is adaptively determined. Meanwhile, the adaptive federated IMM filter designs different models for each local system. When the external disturbances change, the model of each local system can switch in real-time. Furthermore, an AUV integrated navigation system model is constructed, which includes the dynamic model of the system error and the measurement models of strapdown inertial navigation system/Doppler velocity log (SINS/DVL) and SINS/terrain aided navigation (SINS/TAN). The integrated navigation experiments demonstrate that the proposed filter can dramatically improve the accuracy and reliability of the integrated navigation system. Additionally, it has obvious advantages compared with the federated Kalman filter and the adaptive federated Kalman filter.

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