Simulation of an Inertial Acoustic Navigation System With Range Aiding for an Autonomous Underwater Vehicle

Lee, Pan-Mook; Jun, Bong-Huan; Kim, Ki‐Hun; Lee, Jihong; Aoki, Taro; Hyakudome, Tadahiro

doi:10.1109/joe.2006.880585

Cited by 122 publications

(46 citation statements)

References 16 publications

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“…To verify the accuracy of the proposed system, its estimated position is compared NO. 3 with that of the ESKF-based integrated navigation system (Lee et al, 2007;Miller et al, 2010;Shabani et al, 2013).…”

Section: O H a M M A D S H A B A N I A N D A S G H A R G H O L A M mentioning

confidence: 99%

Improved Underwater Integrated Navigation System using Unscented Filtering Approach

Shabani

2015

J. Navigation

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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.2. Underwater Integrated Navigation System. 3. Error State Kalman Filter.4. Unscented Filtering.

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Section: O H a M M A D S H A B A N I A N D A S G H A R G H O L A M mentioning

confidence: 99%

Improved Underwater Integrated Navigation System using Unscented Filtering Approach

Shabani

2015

J. Navigation

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show abstract

“…Inertial navigation uses the acceleration of the vehicle and integrates this twice. This is more accurate then the velocity measurement, but the initialization can be difficult (Lee et al, 2007). The problem with both systems is that the position error increases as the distance traveled increases.…”

Section: Controls and Navigationmentioning

confidence: 99%

“…For operations near the seabed, Doppler velocity logs (DVLs) can be used to measure the vehicle's velocity with respect to the ground. With these measurements the accuracy of the position estimation by the Kalman filter can improve greatly (Leonard et al, 1998;Lee et al, 2007). A DVL measures the Doppler shift of sonar signals reflected by the ground to obtain the velocity (Keary et al, 1999).…”

Section: Sensors and Instrumentationmentioning

confidence: 99%

The State-of-Art of Underwater Vehicles - Theories and Applications

Wang¹,

Engelaar²,

Chen³

et al. 2009

Mobile Robots - State of the Art in Land, Sea, Air, and Collaborative Missions

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“…: +886 2 33662688. which results in the unbounded estimation error increasing over time [10,11]. This shortcoming can be overcome if the IMU is corrected periodically based on information obtained from other sensors such as a global positioning system (GPS) [12], a leg-pose sensor [13], a vision system [14], or a range sensor [15]. The present study employed an electric compass sensor and estimated legged odometry as the aiding sensing devices.…”

Section: Introductionmentioning

confidence: 99%

“…The Kalman filter has been widely used in INS state estimation [8,[12][13][14][15][16]19,20]. It combines all available measurement data with prior knowledge about the sensing device and the system to produce an optimal state estimation that statistically minimizes the error [21,22].…”

Section: Introductionmentioning

confidence: 99%