Next generation marine precision navigation system

Rice, H.; Mendelsohn, L.; Aarons, Richard N; Mazzola, D.

doi:10.1109/plans.2000.838303

Cited by 32 publications

(13 citation statements)

References 1 publication

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“…To the best of our knowledge, prior to 2004 there were only a few references mentioning the utilization of terrain navigation by military submarines of the US Navy (e.g., and ) and some publications on the development of prototype systems for autonomous underwater vehicles. Examples of the latter are and .…”

Section: The Terrain‐aided Navigation Problemmentioning

confidence: 99%

Robust particle filter formulations with application to terrain‐aided navigation

Teixeira

Quintas

Maurya

et al. 2016

Adaptive Control & Signal

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Summary The work described in this paper is motivated by the need to develop efficient and robust estimation filters with application to terrain‐aided navigation of underwater robotic vehicles. One of the main problems addressed is the development of navigation particle filters that can deal with the scarcity of landmarks and the terrain ambiguity that characterize vast regions of the ocean floor. As a contribution to solve this problem, the paper proposes three novel particle filter algorithms and assesses their estimation efficiency and robustness to non‐informative measurements using two well‐known benchmarking tests. The performance of the new filters in these tests demonstrates their potential to solve a class of nonlinear problems that include, but are not limited to, the type of underwater navigation problem that motivated the present work. Our study concludes by examining the performance of the filters in terms of determining the position and velocity of an autonomous underwater vehicle in the presence of unknown ocean currents. When applied to terrain‐aided navigation, the novel particle filter formulations formulations mitigate filter divergence issues frequently caused by terrain symmetries and are more robust than other well‐known versions when used in scenarios with poor terrain information. The theoretical developments presented and the results obtained in simulations are validated using real data acquired during tests with an autonomous marine robot. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: The Terrain‐aided Navigation Problemmentioning

confidence: 99%

Robust particle filter formulations with application to terrain‐aided navigation

Teixeira

Quintas

Maurya

et al. 2016

Adaptive Control & Signal

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“…The first point assumes one I-point of the first contour determined by Step 1. Then the circle with the first point as center and D N as radius is constructed, and the last point can be sought as the crossing point (possibly one (x 1 i , y 1 i ) with several (x N ik , y N ik )) between the circle and the last isogram. The process can be expressed by (5).…”

Section: Iesilmentioning

confidence: 99%

“…The technique of Underwater geoMagnetic Navigation (UMN) [1] recently has become a hot-point in the research area of navigation. This trend will develop further with the unreliability of the Global Navigation Satellite System (GNSS) increasing [2], and with the requirements growing in some special conditions (e.g., submarine exploration) [3].…”

Section: Introductionmentioning

confidence: 99%

Hausdorff-Based RC and IESIL Combined Positioning Algorithm for Underwater Geomagnetic Navigation

Lin

2010

EURASIP J. Adv. Signal Process.

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This paper presents a primitive solution with novel scheme and algorithm for Underwater geoMagnetic Navigation (UMN), which now occurs as the hot-point in the research field of navigation. UMN as an independent or supplementary technique can theoretically supply accurate locations for marine vehicles, but in practice there are plenty of restrictions for UMN's application (e.g., geomagnetic daily variation). After analysis of the theoretical model of geomagnetic positioning in the correlationmatching mode from the viewpoint of pattern recognition, this paper proposed an appropriate matching scenario and a combined positioning algorithm for UMN. The subalgorithm of Hausdorff-based Relative Correlation (RC) corresponding to the pattern classification module implements the coarse positioning, and the subalgorithm of Isograms Equidistance-Segmenting the Intersection Lines (IESILs) associated with the module of feature extraction continues the fine positioning. The experiments based on the simulation platform and the real-surveyed data both validate the new algorithm, and its efficiency and accuracy are also discussed. It can be concluded that the work introduced in this paper gives an initial and real validation of UMN's potentiality.

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“…Nowadays, the preferred high performance navigator for the surface ship and attack submarine is based on RLG technology [4]. RLG-based strap-down inertial navigation systems(RSINS) do not achieve the performance levels of gimbaled stable platform inertial navigation systems, such as ESG-based inertia units, but their significantly fewer moving parts make them more rugged and reliable, and the ability to the units' large amount production has resulted in significant cost advantages.…”

Section: Introductionmentioning

confidence: 99%

Research on Auto Compensation Technique of Strap-down Inertial Navigation Systems

Zhang

Lian

et al. 2009

2009 International Asia Conference on Informatics in Control, Automation and Robotics

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Inertial measurement unit(IMU), consisted of three mutually-perpendicular Ring Laser Gyros(RLG) and three accelerometers, is an important part of RLG -based strapdown inertial navigation systems(SINS). RLG provides measurement of change in vehicle attitude or its turn rate with respect to inertial space. Accelerometer provides measurement of specific force. These measurements provided by gyros and accelerometers are calculated to obtain the desired attitude, velocity and position information with respect to a pre-defined reference frame. So the accuracy of SINS clearly depends on the accuracy of the gyros and accelerometers. In order to improve the accuracy of SINS, auto compensation techniques of rotation is used. With IMU rotating about one or two axis of the vehicle, the bias errors of gyros and accelerometers may be modulated into periodical variable signal. So the modulated sensor errors reduce SINS errors. The theory of rotation auto compensation technique is analyzed. Single channel error propagation equations based on rotation compensation are derived. The simulation experiments of the SINS are carried out. Analytic results and the simulation results indicate that, with the help of rotation auto compensation, the effect of gyro's random drift and accelerometer's bias on SINS errors is greatly reduced.

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Next generation marine precision navigation system

Cited by 32 publications

References 1 publication

Robust particle filter formulations with application to terrain‐aided navigation

Robust particle filter formulations with application to terrain‐aided navigation

Hausdorff-Based RC and IESIL Combined Positioning Algorithm for Underwater Geomagnetic Navigation

Research on Auto Compensation Technique of Strap-down Inertial Navigation Systems

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