Initial Evaluation of the New Real-time Tracking Gradiometer Designed for Small Unmanned Underwater Vehicles

Allen, George I.; Sulzberger, G.; Bono, J.; Pray, J.S.; Clem, T.R.

doi:10.1109/oceans.2005.1640046

Cited by 28 publications

(18 citation statements)

References 11 publications

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“…The target positions and moments were found using an algorithm that was developed at NSWC PC and previously described [8]. Briefly, the algorithm uses overlapping segments of magnetic gradient data to estimate the positions Figure 3.…”

Section: Resultsmentioning

confidence: 99%

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Demonstration of the Real-Time Tracking Gradiometer for Buried Mine Hunting While Operating From a Small Unmanned Underwater Vehicle

et al. 2006

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In many undersea conditions, optical sensors and sonar can be used to discriminate against sea mines. However, there are many conditions where these sensors are insufficient. For example, when a mine is fully buried these sensors are of little help. Under these conditions, additional sensor technologies are required. Since it is not affected by the medium, a technology of choice is magnetics.In the late 1990's a "T-shaped" gradiometer with a 12-inch baseline was developed. It became known as the RTG. Measurements performed in the nonmagnetic facility at the Naval Surface Warfare Center Panama City (NSWC-PC) demonstrated good localization capabilities and it was selected to become part of an ONR initiative to replace the human diver with Unmanned Underwater Vehicles (UUVs) using custom designed payload modules in high-risk mission areas. In the early 2000's the land-based RTG was refitted for underwater applications and integrated with the Florida Atlantic University's Buried Object Scanning Sonar (BOSS). Both were operated from a towed nonmagnetic sled where they demonstrated the ability to localize on buried undersea magnetic targets. The collection of simultaneous magnetic and acoustic data provided the opportunity to apply sensor fusion.While the towed nonmagnetic sled was an ideal magnetic platform, it was unsuited for the shallow water operations required by the Navy. In response to those requirements, both RTG and BOSS were redesigned to fit on newly developed UUVs, such as the 12.75"-diameter Bluefin 12. As expected the UUVs magnetic platform noise level was considerably higher due to the increased number of magnetic noise sources on an active autonomous vehicle and the closer placement of the RTG to these noise sources. To mitigate this increased noise, a magnetic noise cancellation system using magnetometers and current sensors, strategically placed within the control section of the UUV, was implemented.The initial underwater shake down of this entirely new system occurred in August 2005. This demonstrated, for the first time, autonomous control of the RTG by the Bluefin 12. Sea tests continued during 2006, collecting simultaneous data from the RTG, BOSS and a simple optical camera. These co-registered data have been used to demonstrate the common detection and localization of buried targets. This paper focuses on the 2006 sea testing of the system and the initial analysis of the data from the fluxgate-based RTG.

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Section: Resultsmentioning

confidence: 99%

“…Two additional connectors on the RTG electronics bottle are for auxiliary sensors. More extensive details about integration is given in an earlier paper [8]. The auxiliary sensors were not used in the 2006 sea trials because there was no room for them in the production Bluefin 12.…”

Section: Rtg Sensor Head Rtg System Electronicsmentioning

confidence: 99%

Demonstration of the Real-Time Tracking Gradiometer for Buried Mine Hunting While Operating From a Small Unmanned Underwater Vehicle

et al. 2006

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“…The wing BOSS was designed [17] to scan for buried underwater objects using two 1-m hydrophone arrays mounted on the wings of the Bluefin 12 [18] unmanned underwater vehicle (UUV). Each wing contains 20 hydrophone channels, yielding a 40-channel strip array used to collect the sonar returns.…”

Section: A Boss System and Collected Data Setmentioning

confidence: 99%

“…In the frequency domain, this process yields (17) where " " stands for the complex conjugate operation. Now, the effects of the transmit signal can be removed by an inverse filter using (18) where is a small quantity used to avoid singularity problems. Finally, to remove the correlated noise effects, windowing is done in the time (or frequency) domain.…”

Section: B Data Preprocessing and Feature Extractionmentioning

confidence: 99%

Buried Underwater Object Classification Using a Collaborative Multiaspect Classifier

Cartmill

Wachowski

Azimi-Sadjadi

2009

IEEE J. Oceanic Eng.

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Abstract-In this paper, a new collaborative multiaspect classification system (CMAC) is introduced, which utilizes a group of collaborative decision-making agents capable of producing a highconfidence final decision based on features obtained over multiple aspects. It is also shown how CMAC can be modified to perform multiaspect classification using a decision feedback (DF) strategy. The system is then applied to a buried underwater target classification problem. The results show that CMAC provides excellent multiple-ping classification of mine-like objects while reducing the number of false alarms compared to other multiple-ping classification fusion systems such as nonlinear decision-level fusion (DLF).

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“…The wing BOSS was designed [8] to scan for buried underwater objects using two 1m hydrophone arrays mounted on the wings of the Bluefin 12 [9] unmanned underwater vehicle (UUV). Each wing contains 20 hydrophone channels, yielding a 40-channel strip array used to collect the sonar returns.…”

Section: A Boss System and Collected Data Setmentioning

confidence: 99%

A likelihood-based decision feedback system for multi-aspect classification of underwater targets

Wachowski

Azimi-Sadjadi

2009

2009 International Joint Conference on Neural Networks

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This paper presents a new method for multiaspect/ping classification of underwater objects using sonar data. This system uses decision feedback to form a likelihood ratio for making a high confidence final decision based not only upon the data of the current ping but also the final decisions made at several previous pings. The system is then applied to an underwater target classification problem. Test results on a buried object scanning sonar (BOSS) database collected for different objects and in different conditions show the promise of the proposed method for multi-aspect underwater target discrimination.

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Initial Evaluation of the New Real-time Tracking Gradiometer Designed for Small Unmanned Underwater Vehicles

Cited by 28 publications

References 11 publications

Demonstration of the Real-Time Tracking Gradiometer for Buried Mine Hunting While Operating From a Small Unmanned Underwater Vehicle

Demonstration of the Real-Time Tracking Gradiometer for Buried Mine Hunting While Operating From a Small Unmanned Underwater Vehicle

Buried Underwater Object Classification Using a Collaborative Multiaspect Classifier

A likelihood-based decision feedback system for multi-aspect classification of underwater targets

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