Seismic Target Classification Using a Wavelet Packet Manifold in Unattended Ground Sensors Systems

Huang, Jingchang; Zhou, Qianwei; Zhang, Xin; Song, Enliang; Li, Baoqing; Yuan, Xia

doi:10.3390/s130708534

Cited by 35 publications

(14 citation statements)

References 28 publications

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“…We include here some references in this area to quantify the state of the art results that can be expected in this related research scenario, in which various strategies for the design of the PRS (mainly feature extraction and classification) have been used [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61]. Table 1 shows the main features of those signal classification systems, in terms of the sensing method, feature extraction, classification algorithm employed, classification task, and classification accuracy.…”

Section: Machine/vehicle Classification From Other Sensing Systemsmentioning

confidence: 99%

Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review

Tejedor

Macías-Guarasa

Martins³

et al. 2017

Applied Sciences

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Abstract:There is an increasing interest in researchers and companies on the combination of Distributed Acoustic Sensing (DAS) and a Pattern Recognition System (PRS) to detect and classify potentially dangerous events that occur in areas above fiber optic cables deployed along active pipelines, aiming to construct pipeline surveillance systems. This paper presents a review of the literature in what respect to machine learning techniques applied to pipeline surveillance systems based on DAS+PRS (although its scope can also be extended to any other environment in which DAS+PRS strategies are to be used). To do so, we describe the fundamentals of the machine learning approaches when applied to DAS systems, and also do a detailed literature review of the main contributions on this topic. Additionally, this paper addresses the most common issues related to real field deployment and evaluation of DAS+PRS for pipeline threat monitoring, and intends to provide useful insights and recommendations in what respect to the design of such systems. The literature review concludes that a real field deployment of a PRS based on DAS technology is still a challenging area of research, far from being fully solved.

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Section: Machine/vehicle Classification From Other Sensing Systemsmentioning

confidence: 99%

Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review

Tejedor

Macías-Guarasa

Martins³

et al. 2017

Applied Sciences

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“…T HE unattended ground sensor (UGS) system, consisting of a lot of wireless sensor nodes, is usually employed in wild environments to acquire military intelligence about intruding targets through detecting and processing their image, acoustic and infrared signals [1]- [3]. Target classification is one of the most important and demanding technologies for UGS [2].…”

Section: Introductionmentioning

confidence: 99%

Design of an Acoustic Target Classification System Based on Small-Aperture Microphone Array

Huang

Zhang

Guo

et al. 2015

IEEE Trans. Instrum. Meas.

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The acoustic recognition module of the unattended ground sensor (UGS) system applied in wild environments is faced with the challenge of complicated noise interference. In this paper, a small-aperture microphone array (MA)-based acoustic target classification system, including the system hardware architecture and classification algorithm scheme, is designed as a node-level sensor for the application of UGS in noisy situation. Starting from the analysis of signature of the acoustic signal in wild environments and the merits of small-aperture array in noise reduction, a closely arranged microelectromechanical systems MA is designed to improve the signal quality. Considering the similarities between speaker discrimination and acoustic target recognition, a classification algorithm scheme, consisting of a simplified Mel-frequency cepstrum coefficients and the Gaussian mixture model, is developed to distinguish acoustic targets' patterns. The proposed classification algorithm has been implemented on embedded system after being tested on training datasets. By combining the small-aperture array and low-complexity classification algorithm, the presented acoustic classification prototype system is portable and efficient. To demonstrate the efficiency of the design, the prototype system is verified in a practical situation with the employment of wheeled and tracked vehicles. Evaluation of the system performances in comparison with other state-of-the-art methods indicates that the proposed design is practical for the acoustic target classification and may be widely adopted by UGS.Index Terms-Acoustic classification system, Gaussian mixture model, Mel-frequency cepstrum coefficients, microelectromechanical systems, noise, small-aperture array.

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“…The fixed-point observation technique is widely employed in UGS and ITS by installing an inductive loop sensor [7], ultrasonic sensor [8,9], seismic sensor [10] and camera [11]. However, these sensing systems suffer from complicated installation, expensive maintenance costs and high power consumption.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Counting and Moving Direction Identification Based on Small-Aperture Microphone Array

Zhang

Guo

et al. 2017

Sensors

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The varying trend of a moving vehicle’s angles provides much important intelligence for an unattended ground sensor (UGS) monitoring system. The present study investigates the capabilities of a small-aperture microphone array (SAMA) based system to identify the number and moving direction of vehicles travelling on a previously established route. In this paper, a SAMA-based acoustic monitoring system, including the system hardware architecture and algorithm mechanism, is designed as a single node sensor for the application of UGS. The algorithm is built on the varying trend of a vehicle’s bearing angles around the closest point of approach (CPA). We demonstrate the effectiveness of our proposed method with our designed SAMA-based monitoring system in various experimental sites. The experimental results in harsh conditions validate the usefulness of our proposed UGS monitoring system.

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Seismic Target Classification Using a Wavelet Packet Manifold in Unattended Ground Sensors Systems

Cited by 35 publications

References 28 publications

Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review

Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review

Design of an Acoustic Target Classification System Based on Small-Aperture Microphone Array

Vehicle Counting and Moving Direction Identification Based on Small-Aperture Microphone Array

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