Autonomous Underwater Vehicle Navigation

Miller, Paul A.; Farrell, Jay A.; Zhao, Yuanyuan; Djapic, Vladimir

doi:10.1109/joe.2010.2052691

Cited by 254 publications

(125 citation statements)

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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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“…In this section we describe how the navigations state is estimated using an error state formulation of the Kalman filter given by Farrell 1 , et. al.…”

Section: Local Filtermentioning

confidence: 99%

“…Once all LBL measurements arrive the navigation state is corrected using the error state and then the error state is set to zero. The update equations are Kalman filter equations given in 1,2 .…”

Section: Time Update Equationsmentioning

confidence: 99%

Fusion of Redundant Aided-inertial Sensors with Decentralised Kalman Filter for Autonomous Underwater Vehicle Navigation

Awale¹,

Hablani²

2015

DSJ

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Most submarines carry more than one set of inertial navigation system (INS) for redundancy and reliability. Apart from INS systems, the submarine carries other sensors that provide different navigation information. A major challenge is to combine these sensors and INS estimates in an optimal and robust manner for navigation. This issue has been addressed by Farrell 1 . The same approach is used in this paper to combine different sensor measurements along with INS system. However, since more than one INS system is available onboard, it would be better to use multiple INS systems at the same time to obtain a better estimate of states and to provide autonomy in the event of failure of one INS system. This would require us to combine the estimates obtained from local filters (one set of INS system integrated with external sensors), in some optimal way to provide a global estimate. Individual sensor and IMU measurements cannot be accessed in this scenario. Also, autonomous operation requires no sharing of information among local filters. Hence a decentralised Kalman filter approach is considered for combining the estimates of local filters to give a global estimate. This estimate would not be optimal, however. A better optimal estimate can be obtained by accessing individual measurements and augmenting the state vector in Kalman filter, but in that case, corruption of one INS system will lead to failure of the whole filter. Hence to ensure satisfactory performance of the filter even in the event of failure of some INS system, a decentralised Kalman filtering approach is considered.

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“…They are used in DR [18,19], in combined LBL/DR navigation systems [20,21], or as an aiding input to an INS [10,22]. In some cases, upward-looking DVLs have been used to measure vehicle velocity relative to overhead ice [23].…”

Section: Doppler Velocity Logmentioning

confidence: 99%

“…In [22,78], Farrell uses an error state EKF for sensor fusion aboard an AUVaiding an inertial navigation system (INS) with velocity measurements from a DVL, heading measurements from a magnetometer, depth measurements from a pressure sensor, and two-way travel time measurements from four LBL transponders 5 . The measurement delay compensation uses a wait-and-correct strategy.…”

Section: Handling Delayed Measurementsmentioning

confidence: 99%

Contributions to automated realtime underwater navigation

Stanway¹

2012

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This dissertation presents three separate-but related-contributions to the art of underwater navigation. These methods may be used in postprocessing with a human in the loop, but the overarching goal is to enhance vehicle autonomy, so the emphasis is on automated approaches that can be used in realtime. The three research threads are: i) in situ navigation sensor alignment, ii) dead reckoning through the water column, and iii) model-driven delayed measurement fusion. Contributions to each of these areas have been demonstrated in simulation, with laboratory data, or in the field-some have been demonstrated in all three arenas.The solution to the in situ navigation sensor alignment problem is an asymptotically stable adaptive identifier formulated using rotors in Geometric Algebra. This identifier is applied to precisely estimate the unknown alignment between a gyrocompass and Doppler velocity log, with the goal of improving realtime dead reckoning navigation. Laboratory and field results show the identifier performs comparably to previously reported methods using rotation matrices, providing an alignment estimate that reduces the position residuals between dead reckoning and an external acoustic positioning system. The Geometric Algebra formulation also encourages a straightforward interpretation of the identifier as a proportional feedback regulator on the observable output error. Future applications of the identifier may include alignment between inertial, visual, and acoustic sensors.The ability to link the Global Positioning System at the surface to precision dead reckoning near the seafloor might enable new kinds of missions for autonomous underwater vehicles. This research introduces a method for dead reckoning through the water column using water current profile data collected by an onboard acoustic Doppler current profiler. Overlapping relative current profiles provide information to simultaneously estimate the vehicle velocity and local ocean current-the vehicle velocity is then integrated to estimate position. The method is applied to field data using online bin average, weighted least squares, and recursive least squares implementations. This demonstrates an autonomous navigation link between the surface and the seafloor without any dependence on a ship or external acoustic tracking systems. 3Finally, in many state estimation applications, delayed measurements present an interesting challenge. Underwater navigation is a particularly compelling case because of the relatively long delays inherent in all available position measurements. This research develops a flexible, model-driven approach to delayed measurement fusion in realtime Kalman filters. Using a priori estimates of delayed measurements as augmented states minimizes the computational cost of the delay treatment. Managing the augmented states with time-varying conditional process and measurement models ensures the approach works within the proven Kalman filter framework-without altering the filter structure or requiring any ad-hoc adjust...

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Autonomous Underwater Vehicle Navigation

Cited by 254 publications

References 11 publications

Improved Underwater Integrated Navigation System using Unscented Filtering Approach

Improved Underwater Integrated Navigation System using Unscented Filtering Approach

Fusion of Redundant Aided-inertial Sensors with Decentralised Kalman Filter for Autonomous Underwater Vehicle Navigation

Contributions to automated realtime underwater navigation

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