Underwater Vehicle Positioning Based on Time of Arrival Measurements from a Single Beacon

Casey, Thomas A.; Guimond, B.; Hu, James C.

doi:10.1109/oceans.2007.4449186

Cited by 46 publications

(20 citation statements)

References 2 publications

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“…Compared from Equations (12) and (17), it can be seen that the degree of nonlinearity of the measurement equation in the "5-sv" model is much severer than that of traditional models. In essence, the "5-sv" model has converted the problem of estimating two unknowns (x k and y k ) from one measurement (r k ) into the problem of estimating three unknowns (x k , y k and v e,k ) from one measurement (T t k ).…”

Section: Comparison With Traditional Modelmentioning

confidence: 98%

Adaptive Kalman Filter Based Single Beacon Underwater Tracking With Unknown Effective Sound Velocity

Deng

Zhu

et al. 2018

2018 IEEE 8th International Conference on Underwater System Technology: Theory and Applications (USYS)

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In the single-beacon underwater tracking system, vehicles rely on slant range measurements from an acoustic beacon to bound errors accumulated by dead reckoning. Ranges are usually obtained based on a presumed known effective sound velocity (ESV). Since the ESV is difficult to determine accurately, traditional methods suffer from large positioning error. By treating the unknown ESV as a state variable, a novel single-beacon tracking model (the so called "5-sv" model) and an extended Kalman filter (EKF)-based solution method have been discussed to solve the problem of ESV estimation. However, due to the uncertainty of underwater acoustic propagation, the probabilistic characteristics of the ESV uncertainty and acoustic measurement noise are unknown and varying both with time and location. EKF, which runs with presupposed noise parameters, cannot describe the practical noise specifications. To overcome the divergence issue of EKF-based single-beacon tracking methods, this paper proposes an adaptive Kalman filter-based single-beacon tracking algorithm which employs the "5-sv" model as the baseline model. Through numerical examples using simulated and field data, both the filter and smoother results show that while implementing the proposed algorithm, the tracking accuracy can be significantly improved, and the estimated noise parameter agrees well with its true value.

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Section: Comparison With Traditional Modelmentioning

confidence: 98%

Adaptive Kalman Filter Based Single Beacon Underwater Tracking With Unknown Effective Sound Velocity

Deng

Zhu

et al. 2018

2018 IEEE 8th International Conference on Underwater System Technology: Theory and Applications (USYS)

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show abstract

“…On the other hand, least mean square method is optimal for Gaussian noise and acoustic pings suffer of non-Gaussian noise. Casey and Thomas [3] defined an intuitive estimator, based on least mean square errors, relying also on very accurate dead-reckoning.…”

Section: Initializationmentioning

confidence: 99%

“…We address the problem of navigation and control in a coupled way in order to guarantee observability. Some works have been performed in single beacon navigation [1], [3]. Both implementations include Kalman filter estimation, being different on the initialization process.…”

Section: Introductionmentioning

confidence: 99%

Single beacon navigation: Localization and control of the MARES AUV

Ferreira

Matos

Cruz

2010

Oceans 2010 MTS/Ieee Seattle

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This paper addresses the simultaneous localization and control of an AUV using a single acoustic beacon. To determine its horizontal position, the AUV fuses distances to the single beacon with dead reckoning data, heading and longitudinal velocity. A particle filter and an extended Kalman filter are implemented and compared in terms of performances. As this is an ill-posed problem, special guidance laws are derived so that the overall horizontal positioning error remains bounded. Besides presenting the derivation of such guidance and control laws, as well as procedures to estimate the horizontal position, we also demonstrate the performance of the proposed system by means of simulation results.978-1-4244-4333-8/10/$25.00 ©2010 IEEE

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“…In (Larsen 2000), the author describes a Synthetic Long Base-Line (SLBL) navigation algorithm, which makes use of a single LBL in combination with a high performance dead-reckoning navigation system. In (Casey & Hu 2007) the authors describe an Extended Kalman Filter (EKF) for localization of an AUV using a single beacon, and in (Saude & Aguiar 2009) combining the dead-reckoning information with multiple range measurements taken at different instants of time from the vehicle to a single beacon, a robust estimation algorithm was proposed for vehicle localization in the presence of unknown ocean currents. In (Olson et al 2006), a range only beacon localization algorithm is presented, which assumes no prior knowledge of beacons' locations.…”

Section: Introductionmentioning

confidence: 99%

Underwater localization and mapping: observability analysis and experimental results

Bayat¹,

Aguiar

2014

Industrial Robot: An International Journal

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Purpose-We investigate the observability properties of the process of simultaneous localization and mapping of an Autonomous Underwater Vehicle (AUV), a challenging and important problem in marine robotics, and illustrate the derived results through computer simulations and experimental results with a real AUV. Design/methodology/approach-We address the single/multiple beacon observability analysis of the process of simultaneous localization and mapping of an Autonomous Underwater Vehicle (AUV) by deriving the nonlinear mathematical model that describes the process; then applying a suitable coordinate transformation, and subsequently a time-scaling transformation to obtain a Linear Time Varying (LTV) system. The AUV considered is equipped with a set of inertial sensors, a depth sensor, and an acoustic ranging device that provides relative range measurements to a set of stationary beacons. The location of the beacons do not need to be necessarily known and in that case, we are also interested to localize them. Numerical tests and experimental results illustrate the derived theoretical results. Findings-We show that, if either the position of one of the beacons or the initial position of the AUV is known, then the system is at least locally weakly observable, in the sense that the set of indistinguishable states from a given initial configuration contains a finite set of isolated points. The simulations and experiments results illustrate the findings. Originality/value-In the single and multiple beacon case and for manoeuvres with constant linear and angular velocities both expressed in the body-frame, known as trimming or steady-state trajectories, we derive conditions under which it is possible to infer the state of the resulting system (and in particular the position of the AUV). We also describe implementation of an advanced continuous time constrained minimum energy observer combined with multiple model techniques. Numerical tests and experimental results illustrate the derived theoretical results.

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Underwater Vehicle Positioning Based on Time of Arrival Measurements from a Single Beacon

Cited by 46 publications

References 2 publications

Adaptive Kalman Filter Based Single Beacon Underwater Tracking With Unknown Effective Sound Velocity

Adaptive Kalman Filter Based Single Beacon Underwater Tracking With Unknown Effective Sound Velocity

Single beacon navigation: Localization and control of the MARES AUV

Underwater localization and mapping: observability analysis and experimental results

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