Simplified acoustic localization by linear arrays for wireless sensor networks

Astapov, Sergei; Preden, Jürgo-Sören; Berdnikova, Julia

doi:10.1109/icdsp.2013.6622741

Cited by 11 publications

(9 citation statements)

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“…In WSN, however, collecting raw signals is a real challenge, especially if the number of nodes is large and signal frames are long. To overcome problems associated with communicating signal frames we apply a simplified localization approach of Initial Search Region Reduction (ISRR), recently developed by our research team [1]. The main idea behind ISRR lies in maximally confining the region of acoustic source disposition as a preliminary procedure to SRP-PHAT or other localization method [6].…”

Section: A Acoustic Source Localizationmentioning

confidence: 99%

“…Acoustic signals are used for both acoustic source localization and identification. For localization a recently developed method of Initial Search Region Reduction (ISRR) is used [1] to manage localization on low power hardware of smart sensors. Magnetic and PIR sensor readings complement acoustic information in detection of vehicles and foot soldiers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Object detection for military surveillance using distributed multimodal smart sensors

Astapov

Preden

Ehala

et al. 2014

2014 19th International Conference on Digital Signal Processing

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This paper considers an autonomous ground Intelligence, Surveillance and Reconnaissance (ISR) system comprising of multiple distributed, wirelessly communicating smart sensors. The ISR system, in turn, is a part of a larger System of Systems (SoS) consisting of aerial, manned, etc. surveillance systems and information collection centers. The smart sensors of the ISR system perform environment monitoring using different modalities and exchange object detection and identification results to assess the situation and provide other SoS components with this information. In the paper we discuss using acoustic, magnetic and Passive Infrared (PIR) sensor information for target detection and identification. We also propose an approach of distributed acoustic source localization and a method of velocity estimation using PIR data. For sensor communication an asynchronous adhoc WSN configuration is proposed. The system is implemented on low power smart sensors utilizing Atmel ATmega128RFA1 processors with integrated 2.4GHz IEEE 802.15.4 compliant radio transceivers.

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Section: A Acoustic Source Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Object detection for military surveillance using distributed multimodal smart sensors

Astapov

Preden

Ehala

et al. 2014

2014 19th International Conference on Digital Signal Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The application of these functions optimizes the SRC process by greatly reducing the number of SRP evaluations for reduced regions of acoustic source search [19].…”

Section: Application Of Srp-phat With Src To Isrr Estimatesmentioning

confidence: 99%

Optimized Acoustic Localization with SRP-PHAT for Monitoring in Distributed Sensor Networks

Astapov¹,

Berdnikova²,

Preden³

2013

International Journal of Electronics and Telecommunications

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Abstract-Acoustic localization by means of sensor arrays has a variety of applications, from conference telephony to environment monitoring. Many of these tasks are appealing for implementation on embedded systems, however large dataflows and computational complexity of multi-channel signal processing impede the development of such systems. This paper proposes a method of acoustic localization targeted for distributed systems, such as Wireless Sensor Networks (WSN). The method builds on an optimized localization algorithm of Steered Response Power with Phase Transform (SRP-PHAT) and simplifies it further by reducing the initial search region, in which the sound source is contained. The sensor array is partitioned into sub-blocks, which may be implemented as independent nodes of WSN. For the region reduction two approaches are handled. One is based on Direction of Arrival estimation and the other -on multilateration. Both approaches are tested on real signals for speaker localization and industrial machinery monitoring applications. Experiment results indicate the method's potency in both these tasks.

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“…Depending on the measurements, the conventional localization algorithms for multiple sources in the literature can be roughly classified into the following categories: (1) the Global Positioning System (GPS) [4], (2) the directly received data [2], [6], [22], [26], [28], (3) the received signal strength (RSS) [10], [8], [17], [23], [29], (4) the time-ofarrival (TOA) or the time-difference-of-arrival (TDOA) [30], [35] and (5) the direction-of-arrival (DOA) [18], [36]. For a more detailed survey on the existing methods, one may refer to [12], [14], and [32].…”

Section: Introductionmentioning

confidence: 99%

Multiple Sources Localization by the WSN Using the Direction-of-Arrivals Classified by the Genetic Algorithm

Zhang

Yu-hua

2019

IEEE Access

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Simultaneously locating multiple sources passively in the wireless sensor networks (WSN) is challenging in the internet of things (IoT) applications, where reducing the computation and communication load is of great importance due to the requirement on real-time processing and the energy constraint. This is especially true when the number of sources or the number of sensor nodes is large. In this paper, a localization algorithm to estimate multiple sources' positions in the three-dimensional space is proposed. With the direction-of-arrival (DOA) estimates for multiple sources obtained at each sensor node, it is crucial to discriminate which estimate corresponds to which source. To save the computation resources, a classification method based on the genetic algorithm is proposed to handle the multiple sources. A fitness function is designed to assess the clustering of the DOA estimates. Extensive simulations are carried out to analyze the algorithm performance under the various settings. Numerical examples show that the proposed method could lower the computational burden by orders of magnitude compared to the conventional method, without significantly sacrificing the estimation accuracy.

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Simplified acoustic localization by linear arrays for wireless sensor networks

Cited by 11 publications

References 11 publications

Object detection for military surveillance using distributed multimodal smart sensors

Object detection for military surveillance using distributed multimodal smart sensors

Optimized Acoustic Localization with SRP-PHAT for Monitoring in Distributed Sensor Networks

Multiple Sources Localization by the WSN Using the Direction-of-Arrivals Classified by the Genetic Algorithm

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