A novel laser Doppler velocimeter and its integrated navigation system with strapdown inertial navigation

Navigation is a precondition for ocean space vehicles to work safely in polar regions. The traditional polar algorithms employ the grid strapdown inertial navigation system (SINS) as the backbone and Doppler velocity log (DVL) output velocity as measurements to constitute the integrated navigation system, of which, however, the position errors still accumulate with time. The ultra-short baseline (USBL) position system can provide position information that can be used to improve the performance of the SINS/DVL integrated system. Therefore, a grid SINS/DVL/USBL integrated algorithm for polar navigation is proposed in this paper. In order to extend the availability of the USBL and improve integration accuracy in polar regions, the USBL observation model is established based on the relative position measurement firstly. Then, a grid SINS/DVL/USBL integrated algorithm is proposed to fuse the information of these sensors with a modified Kalman filter (MKF) dealing with the sparse USBL output. Finally, a vector fault detection method, which takes the measurements as detection objects instead of the filter, is designed to locate the measurement fault and can be employed by the centralized filter to improve the fault-tolerant. Simulation and experiment results show that the proposed grid SINS/DVL/USBL integrated navigation system can further restrain SINS errors especially the position errors effectively. Meanwhile, the vector fault detection method can detect and isolate the fault measurements of centralized filter immediately and accurately. Therefore, the proposed fault-tolerant grid SINS/DVL/USBL integrated navigation algorithm can improve the reliability and accuracy of polar navigation for ocean space application.

Section: Introductionmentioning

confidence: 99%

A Fault-Tolerant Polar Grid SINS/DVL/USBL Integrated Navigation Algorithm Based on the Centralized Filter and Relative Position Measurement

Zhao

Kang

Cheng

et al. 2019

“…With the latitude increasing, the global navigation satellite system (GNSS) would suffer more and more challenges such as ionosphere scintillation, multipath effect and lack of reference stations [ 3 , 4 ]. Although the strapdown inertial navigation system (SINS) is highly autonomous and self-contained [ 5 , 6 ], the SINS navigation output contains three kinds of periodic oscillation errors and accumulated errors [ 7 , 8 , 9 ]. To restrain the SINS errors, the integrated navigation system of SINS and other external navigation information is usually applied.…”

Section: Introductionmentioning

confidence: 99%

A Novel Grid SINS/DVL Integrated Navigation Algorithm for Marine Application

Kang

Zhao

Cheng

et al. 2018

Integrated navigation algorithms under the grid frame have been proposed based on the Kalman filter (KF) to solve the problem of navigation in some special regions. However, in the existing study of grid strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated navigation algorithms, the Earth models of the filter dynamic model and the SINS mechanization are not unified. Besides, traditional integrated systems with the KF based correction scheme are susceptible to measurement errors, which would decrease the accuracy and robustness of the system. In this paper, an adaptive robust Kalman filter (ARKF) based hybrid-correction grid SINS/DVL integrated navigation algorithm is designed with the unified reference ellipsoid Earth model to improve the navigation accuracy in middle-high latitude regions for marine application. Firstly, to unify the Earth models, the mechanization of grid SINS is introduced and the error equations are derived based on the same reference ellipsoid Earth model. Then, a more accurate grid SINS/DVL filter model is designed according to the new error equations. Finally, a hybrid-correction scheme based on the ARKF is proposed to resist the effect of measurement errors. Simulation and experiment results show that, compared with the traditional algorithms, the proposed navigation algorithm can effectively improve the navigation performance in middle-high latitude regions by the unified Earth models and the ARKF based hybrid-correction scheme.

“…The inertial navigation system (INS) has been widely used for automated vehicle positioning and navigating because of its high autonomy, concealment, continuum, insusceptible climate, and the successive supply of position, velocity, and attitude (PVA) information [ 2 ]. The strapdown inertial navigation system (SINS) is simple, small in size, and is convenient in maintenance compared with plat INS (PINS) and is also very popular in vehicle navigation systems [ 3 , 4 ]. However, the random drift error of gyroscope in SINS may lead to serious accumulated navigation errors during the long operation of SINS alone.…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Vehicle Navigation System Based on Inertial and Visual Sensors

Guang

Gao

Leung

et al. 2018

The strapdown inertial navigation system (SINS) is widely used in autonomous vehicles. However, the random drift error of gyroscope leads to serious accumulated navigation errors during long continuous operation of SINS alone. In this paper, we propose to combine the Inertial Measurement Unit (IMU) data with the line feature parameters from a camera to improve the navigation accuracy. The proposed method can also maintain the autonomy of the navigation system. Experimental results show that the proposed inertial-visual navigation system can mitigate the SINS drift and improve the accuracy, stability, and reliability of the navigation system.