Military vehicle acoustic pattern identification by distributed Ground Sensors

Astapov, Sergei; Riid, Andri; Preden, Jürgo-Sören

doi:10.1109/bec.2016.7743755

Cited by 10 publications

(8 citation statements)

References 7 publications

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“…On the other hand, the ability to suppress the wind noise is further improved by selecting large value of SMSC sub-bands for DOA estimation. In previous research, we also had proved that SMSC can reflect the signal-to-noise ratio [31]. The larger SMSC is, the higher SNR will be.…”

Section: Doa Estimation Algorithmmentioning

confidence: 92%

“…In array-signal processing, the magnitude squared coherence (MSC) is defined as the square of space magnitude coherence, which is often used for sound source detection, signal enhancement and target classification, etc. In previous study, we have already extended the MSC to wideband signal for tracking moving vehicle targets by small aperture microphone arrays [30], [31]. As shown in Figure 9(c), wind noise can be effectively distinguished from target by comparing SMSC in the low frequency region.…”

Section: B Detection Algorithmmentioning

confidence: 99%

“…For the small-aperture microphone arrays, the DOA estimation algorithm faces challenges from resolution and robustness. Previously, the broadband MUSIC algorithm based on sub-bands weighting has been proposed for DOA esti- mation [31]. On one hand, the accuracy and resolution of azimuth estimation are improved by using the wideband MUSIC algorithm.…”

Section: Doa Estimation Algorithmmentioning

confidence: 99%

“…In previous studies, our research team has designed a small-aperture microphone array system [23] for recognizing moving vehicle targets and has carried out related algorithm research in acoustic signal enhancement [24], target detection [25], [26], target classification [27], [28] and DOA estimation [29]- [31]. Based on our previous research, we aimed to design a microphone array node with ultra-low power by equipping the proposed robust practical algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Single-Mode Wild Area Surveillance Sensor With Ultra-Low Power Design Based on Microphone Array

et al. 2019

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Acoustic sensors are used as an important sensor component in wireless sensor networks (WSNs) for wild environmental monitoring, because of its small size, light weight, and low power consumption. To further utilize the superiorities of acoustic sensor, this paper aims to design a single-mode wild area surveillance sensor based on acoustic sensors, which have the advantages of practicality, low hardware power consumption, and low software complexity. The proposed algorithm can effectively suppress wind noise and improve the performance of target detection, classification and direction of arrival (DOA) estimation. The power consumption test results show that the designed micro-sensor node has low power consumption of about 13.8 mW in on-duty mode and long-term continuous monitoring capability of about 33 days. The field experiment results reveal that the node has favorable performance in detection (detection rate as high as 96% with false alarm rate under 5%), tracking (error is less than 7.6 degree), and target classification (accuracy is greater than 92.6%).

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Section: Doa Estimation Algorithmmentioning

confidence: 92%

Section: B Detection Algorithmmentioning

confidence: 99%

Section: Doa Estimation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Single-Mode Wild Area Surveillance Sensor With Ultra-Low Power Design Based on Microphone Array

et al. 2019

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“…While the SN collect physical parameters of the nearby environment, the BS collects, compiles and analyzes the data from all nodes. In fact, these networks have appeared from the need of cooperative monitoring solutions, particularly aiming at the environment [ 3 , 4 , 5 ], security [ 6 , 7 ], health and quality of life [ 8 , 9 , 10 , 11 ] and agricultural [ 12 , 13 ] applications, among many others, or for hazard detection [ 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Implementation of a WSN for Environmental Monitoring: From the Base Station to the Small Sensor Node

Oliveira

Reis²,

Caldeirinha³

2022

Sensors

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In this paper, the implementation of a Wireless Sensor Network (WSN) for environmental monitoring (EM) is presented. It includes the design, implementation and experimental characterization of a multi-sector base station (BS) antenna composed of several microstrip Quasi-Yagi elements and the implementation and experimental characterization of a reduced form factor antenna for the sensor nodes (SN). Subsequently, it reports the implementation of a WSN based on Lopy4 transceivers, using the developed BS and SN antennas. Finally, experimental results obtained on the field to evaluate the performance of the network in terms of maximum coverage distance and coverage area are presented. According to the field tests, the connectivity between the sensor nodes and the developed WSN base station is confirmed at distances above 3.5 km and for all the antenna sectors of the multi-sector BS attaining a 360∘ of field of view.

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Highly sensitive Fabry-Perot acoustic sensor based on optic fiber spherical end surface

Sun

Jiang

Wang

et al. 2023

Optical Fiber Technology

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Military vehicle acoustic pattern identification by distributed Ground Sensors

Cited by 10 publications

References 7 publications

Single-Mode Wild Area Surveillance Sensor With Ultra-Low Power Design Based on Microphone Array

Single-Mode Wild Area Surveillance Sensor With Ultra-Low Power Design Based on Microphone Array

Implementation of a WSN for Environmental Monitoring: From the Base Station to the Small Sensor Node

Highly sensitive Fabry-Perot acoustic sensor based on optic fiber spherical end surface

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