scholarly journals Key Techniques of SINS/DVL Integrated Navigation System

2021 ◽  
Vol 2095 (1) ◽  
pp. 012034
Author(s):  
Wanli Li ◽  
Mingjian Chen ◽  
Yun Li
Keyword(s):  
Navigation System ◽  
Autonomous Underwater Vehicles ◽  
Great Influence ◽  
Doppler Velocity ◽  
Integrated Navigation ◽  
Key Factors ◽  
Basic Principles ◽  
Integrated Navigation System ◽  
Navigation Accuracy ◽  
Key Techniques

Abstract Doppler Velocity Log (DVL) aided Strapdown Inertial Navigation System (SINS) is commonly used for the applications of Autonomous Underwater Vehicles (AUVs). In lack of other aiding sensors, how to maintain precise integrated navigation is still a challenge issue. In this paper, the structure and basic principles of the SINS is presented. Alignment calibration of SINS and DVL, fast in-motion alignment and data fusion are key factors which has great influence on the navigation accuracy. Research efforts taken in these fields in recent years are surveyed. This paper may provide a firm foundation for the researchers in related areas.

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.

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.

Application of Federated Filter to AUV Based on Terrain-Aided SINS

2014 ◽  
Vol 711 ◽  
pp. 338-341 ◽  
Author(s):  
Qi Wang ◽  
Cheng Shan Qian ◽  
Zi Jia Zhang ◽  
Chang Song Yang
Keyword(s):  
Kalman Filtering ◽  
Navigation System ◽  
Autonomous Underwater Vehicles ◽  
Inertial Navigation ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Filtering Method ◽  
Long Time ◽  
Federated Filtering ◽  
Strapdown Inertial Navigation

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.

scholarly journals A Fault-Tolerant Filtering Algorithm for SINS/DVL/MCP Integrated Navigation System

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaosu Xu ◽  
Peijuan Li ◽  
Jian-juan Liu
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Fault Tolerant ◽  
Doppler Velocity ◽  
Observation Data ◽  
Integrated Navigation ◽  
Fusion Algorithm ◽  
Data Set ◽  
Integrated Navigation System ◽  
Short Period

The Kalman filter (KF), which recursively generates a relatively optimal estimate of underlying system state based upon a series of observed measurements, has been widely used in integrated navigation system. Due to its dependence on the accuracy of system model and reliability of observation data, the precision of KF will degrade or even diverge, when using inaccurate model or trustless data set. In this paper, a fault-tolerant adaptive Kalman filter (FTAKF) algorithm for the integrated navigation system composed of a strapdown inertial navigation system (SINS), a Doppler velocity log (DVL), and a magnetic compass (MCP) is proposed. The evolutionary artificial neural networks (EANN) are used in self-learning and training of the intelligent data fusion algorithm. The proposed algorithm can significantly outperform the traditional KF in providing estimation continuously with higher accuracy and smoothing the KF outputs when observation data are inaccurate or unavailable for a short period. The experiments of the prototype verify the effectiveness of the proposed method.

scholarly journals Improved Underwater Integrated Navigation System using Unscented Filtering Approach

2015 ◽  
Vol 69 (3) ◽  
pp. 561-581 ◽  
Author(s):  
Mohammad Shabani ◽  
Asghar Gholami
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Doppler Velocity ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Measurement Models ◽  
Error State ◽  
Filtering Approach

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.

scholarly journals Modified Sage-Husa Adaptive Kalman Filter-Based SINS/DVL Integrated Navigation System for AUV

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ruixin Liu ◽  
Fucheng Liu ◽  
Chunning Liu ◽  
Pengchao Zhang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Autonomous Underwater Vehicle ◽  
Positive Definiteness ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Integrated Navigation System ◽  
The Stability ◽  
Navigation Accuracy ◽  
Navigation Errors

This paper presents a modified Sage-Husa adaptive Kalman filter-based SINS/DVL integrated navigation system for the autonomous underwater vehicle (AUV), where DVL is employed to correct the navigation errors of SINS that accumulate over time. When negative definite items are large enough, different from the positive definiteness of noise matrices which cannot be guaranteed for the conventional Sage-Husa adaptive Kalman filter, the proposed modified Sage-Husa adaptive Kalman filter deletes the negative definite items of adaptive update laws of the noise matrix to ensure the convergence of the Sage-Husa adaptive Kalman filter. In other words, this method sacrifices some filtering precision to ensure the stability of the filter. The simulation tests are implemented to verify that expected navigation accuracy for AUV can be obtained using the proposed modified Sage-Husa adaptive Kalman filter.

scholarly journals Low Cost Integrated Navigation System for Unmanned Vessel

2017 ◽  
Vol 24 (s3) ◽  
pp. 110-115
Author(s):  
Changsong Yang ◽  
Qi Wang
Keyword(s):  
Navigation System ◽  
Physical Simulation ◽  
Low Cost ◽  
Measurement Unit ◽  
Doppler Velocity ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Fast Drift ◽  
Navigation Errors ◽  
Navigation Information

Abstract Large errors of low-cost MEMS inertial measurement unit (MIMU) lead to huge navigation errors, even wrong navigation information. An integrated navigation system for unmanned vessel is proposed. It consists of a low-cost MIMU and Doppler velocity sonar (DVS). This paper presents an integrated navigation method, to improve the performance of navigation system. The integrated navigation system is tested using simulation and semi-physical simulation experiments, whose results show that attitude, velocity and position accuracy has improved awfully, giving exactly accurate navigation results. By means of the combination of low-cost MIMU and DVS, the proposed system is able to overcome fast drift problems of the low cost IMU.

Multirate Adaptive Kalman Filter for Marine Integrated Navigation System

2016 ◽  
Vol 70 (3) ◽  
pp. 628-647 ◽  
Author(s):  
Narjes Davari ◽  
Asghar Gholami ◽  
Mohammad Shabani
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Dynamic Environment ◽  
Statistical Information ◽  
Doppler Velocity ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Integrated Navigation System ◽  
Imperfect Knowledge ◽  
Marine Navigation

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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