Hunting sea mines with UUV-based magnetic and electro-optic sensors

Sulzberger, G.; Bono, J.; Manley, Richard J.; Clem, T.R.; Vaizer, L.; Holtzapple, Richard

doi:10.23919/oceans.2009.5422086

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…Reference [2] describes a tightly integrated instantiation of an autonomous agent called CARACaS (Control Architecture for Robotic Agent Command and Sensing) developed at JPL (Jet Propulsion Laboratory, Pasadena, USA) that was designed to address many of the issues for survivable ASV/AUV control and to provide adaptive mission capabilities (see Figure 1). Missions naturally suited for utilization include traverse, mapping, and potentially neutralizing mine fields [5,6], as displayed in Figure 3 from the study in reference [7] for the Phoenix vehicle in Figure 3b. The development of adaptive and learning systems has a long, distinguished lineage in the literature with many optional techniques available to choose from.…”

Section: Figurementioning

confidence: 99%

Impacts of Discretization and Numerical Propagation on the Ability to Follow Challenging Square Wave Commands

Koo

Travis

Sands

2022

JMSE

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This study determines the threshold for the computational rate of actuator motor controllers for unmanned underwater vehicles necessary to accurately follow discontinuous square wave commands. Motors must track challenging square-wave inputs, and identification of key computational rates permit application of deterministic artificial intelligence (D.A.I.) to achieve tracking to a machine-precision degree of accuracy in direct comparison to other state-of-art approaches. All modeling approaches are validated in MATLAB simulations where the motor process is discretized at varying step-sizes (inversely proportional to computational rate). At a large step-size (fast computational rate), discrete D.A.I. shows a mean error more than three times larger than that of a ubiquitous model-following approach. Yet, at a smaller step size (slower computational rate), the mean error decreases by a factor of 10, only three percent larger than that of continuous D.A.I. Hence, the performance of discrete D.A.I. is critically affected by the sampling period for discretization of the system equations and computational rate. Discrete D.A.I. should be avoided when small step-size discretization is unavailable. In fact, continuous D.A.I. has surpassed all modeling approaches, which makes it the safest and most viable solution to future commercial applications in unmanned underwater vehicles.

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Section: Figurementioning

confidence: 99%

Impacts of Discretization and Numerical Propagation on the Ability to Follow Challenging Square Wave Commands

Koo

Travis

Sands

2022

JMSE

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“…Underwater target (object) recognition has a great variety of research purposes: monitoring underwater life sustainability, underwater gas pipeline leak detection as an industrial or environmental prevention application, identifying the presence of manufactured archeological objects for archeological research, underwater detection for mineral exploration, among others [1][2][3][4]. In addition, military activities such as Mine Counter Measures (MCM) [5] and Search and Rescue (SAR) operations [6] may also take advantage of this capability.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Layer Perceptron Model for Underwater Object Recognition

Silveira¹,

Monteiro²,

Rocha³

et al. 2023

JBTH

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The more human gets interested in sea exploration, the more research to detect objects under the water is done. Therefore, the ability to detect, classify, recognize and track all kinds of objects is evolving daily. This paper aims to introduce a computer model for underwater object classification and recognition based on a Multilayer Perceptron network. The model was constructed with a mixed dataset for the training phase, combining artificial and natural objects, and it reached approximately 99.97% classifying accuracy.

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“…[2]. With the development of underwater equipment, the application of accustom-magnet joint detection, positioning, and target search has become more and more extensive, marine magnetic information has been paid more and more attention by various countries [3].…”

Section: Introductionmentioning

confidence: 99%

A Marine Magnetometer Based on TMR

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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TMR (tunneling magnetoresistance) has the characteristics of high sensitivity, large linear range, low power consumption, and easy miniaturization, and has received great attention in the field of miniaturized and high-performance magnetic sensors. Widely used in marine magnetic field detection, earthquake monitoring, industrial control, and other fields. In this paper, A TMR marine magnetometer with AC modulation and magnetic field feedback compensation is proposed, designed, and manufactured, which can be used for the measurement of weak marine magnetic fields. After testing, its measurement range is ±100,000nT, the noise fluctuation level is 0.27nT, and nonlinearity level reaches 0.07%. It works by means of ac modulation and real-time compensation of feedback magnetic field, which greatly reduces magnetic hysteresis and increases the linearity. Compared with the nonlinearity of TMR material (more than 20% within ±1Oe, more than 7% within ±0.3Oe), it has been significantly improved.

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Hunting sea mines with UUV-based magnetic and electro-optic sensors

Cited by 12 publications

References 4 publications

Impacts of Discretization and Numerical Propagation on the Ability to Follow Challenging Square Wave Commands

Impacts of Discretization and Numerical Propagation on the Ability to Follow Challenging Square Wave Commands

A Multi-Layer Perceptron Model for Underwater Object Recognition

A Marine Magnetometer Based on TMR

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