Gunshot acoustic event identification and shooter localization in a WSN of asynchronous multichannel acoustic ground sensors

Astapov, Sergei; Berdnikova, Julia; Ehala, Johannes; Kaugerand, Jaanus; Preden, Jürgo-Sören

doi:10.1007/s11045-017-0479-7

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…The methods mentioned above have been used many times in practical solutions and described in the literature: ToA [ 69 , 70 , 71 , 72 ], TDoA [ 73 , 74 ], AoA [ 75 ], and RSS [ 76 ]. The most popular are Time Difference of Arrival and Angle of Arrival.…”

Section: Acoustic Source Detection and Localization Methodsmentioning

confidence: 99%

A Survey of Sound Source Localization and Detection Methods and Their Applications

Jekateryńczuk,

Piotrowski

2023

Sensors

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This study is a survey of sound source localization and detection methods. The study provides a detailed classification of the methods used in the fields of science mentioned above. It classifies sound source localization systems based on criteria found in the literature. Moreover, an analysis of classic methods based on the propagation model and methods based on machine learning and deep learning techniques has been carried out. Attention has been paid to providing the most detailed information on the possibility of using physical phenomena, mathematical relationships, and artificial intelligence to determine sound source localization. Additionally, the article underscores the significance of these methods within both military and civil contexts. The study culminates with a discussion of forthcoming trends in the realms of acoustic detection and localization. The primary objective of this research is to serve as a valuable resource for selecting the most suitable approach within this domain.

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Section: Acoustic Source Detection and Localization Methodsmentioning

confidence: 99%

A Survey of Sound Source Localization and Detection Methods and Their Applications

Jekateryńczuk,

Piotrowski

2023

Sensors

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“…There are studies related to shooting in the military field for the measurement and identification of the small-caliber bullets orientation before it reaches the impact surface [17], and the use of acoustic location techniques of shooting to be able to identify the shooter [8]. In the forensic field, images of potential residues of particles from the shooting, taken with a hyperspectral camera, are analyzed in order to identify automatically through the use of machine learning algorithms the existence of samples of these residues [12].…”

Section: A Related Workmentioning

confidence: 99%

“…However, an exhaustive review on Web of Science and Scopus, where the keywords were used [gunshot detection, gunshot recognition, bullet recognition, bullet impact recognition, gunshot pattern, gunshot localization, bullet impact detection], shows nowadays there is no publication for DBIS through computing techniques. The few studies related to DBIS are oriented to the detection of the shooter [8], bullet impact analysis of woven fabrics [9], [10], shooting detection through accelerometer [11], and particle residue analysis [12].…”

Section: Introductionmentioning

confidence: 99%

Bullet Impact Detection in Silhouettes Using Mask R-CNN

Vilchez

Mauricio

2020

IEEE Access

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The bullet impact detection in the silhouette plays an important role in the institutions which have a shooting range, specially where are regulated the license to carry a gun and where the evaluation manual process could present distortion in the ability's evaluation of the shooter and like a consequence the license's issuance. This paper proposes a method for an automatic detection of the bullet impacts in silhouettes based on deep learning and image processing, which consist of the following steps: preprocessing, impacts detection, edge detection and evaluation results. The experiments about 600 silhouettes with 2401 bullet impacts images of the proposed and implemented method considering the models Resnet 50 and Resnet 101 for Mask R-CNN show that Resnet 50 get better results than Resnet 101, achieving 97.6 %, 99.5 %, and 97.9 %, of accuracy, precision and recall, respectively, above the methods Circular Hough Transform, Circlet Detection, Random Sample Consensus, Randomized Hough Transform, Randomized Circle Detection, Support Vector Machine, Faster R-CNN, MnasNet and YOLO. Also, the results show 100 % of effectiveness in the edge detection and the count of the detected bullet impacts.INDEX TERMS Edge detection, bullet impact detection, image processing, Mask R-CNN.

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“…The paper by Astapov et al (2017) discussed the problem of distinguishing gunshot Shockwave (SW) and Muzzle Blast (MB) gunshot events in a scenario with presence of NOI acoustic events, where the MB transient is not guaranteed to strictly follow the SW transient. A shooter localization procedure comprising gunshot acoustic event identification based on DOA information, gunshot geometry estimation and shooter position estimation was presented and verified on real-life data.…”

Section: Parameter Estimation Using Sensor Arraysmentioning

confidence: 99%

Array signal processing and systems

Liao

Madanayake

Agathoklis

2018

Multidim Syst Sign Process

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We are delighted to present this special issue of Multidimensional Systems and Signal Processing on Array Signal Processing and Systems. Sensor arrays play an important role in spatio-temporal signal processing with applications spanning across multiple fields such as electromagnetic, acoustics, ultrasonic and seismic processing systems. This plethora of possible applications has sparked a large number of new theoretical developments and array processing systems in the last few years.In the field of electromagnetic sensor (antenna) arrays, there is much interest in the electronically-steerable radio-frequency (RF) beams that can be obtained from phased-array antennas. In the microwave bands, phased-array antennas take both analog and digital signal processing approaches, and find applications in wireless base-stations, radar sensors, space communications, and radio telescopes. In the emerging mm-wave regime, modern applications of array processing are of paramount importance for wireless communications. In particular, emerging 5G systems based on massive-MIMO basestations depend on array processing to mitigate the effects of high path loss and blockages in an urban environment. Acoustic sensor (microphone) arrays are extremely useful for audio and multimedia applications, including high-fidelity sound recording, immersive multimedia and augmented/virtual reality. Ultrasound sensor arrays find extensive applications in biomedical engineering where they are used for imaging the human body and in structural health monitoring of airframes B Bin Liao

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Gunshot acoustic event identification and shooter localization in a WSN of asynchronous multichannel acoustic ground sensors

Cited by 10 publications

References 16 publications

A Survey of Sound Source Localization and Detection Methods and Their Applications

A Survey of Sound Source Localization and Detection Methods and Their Applications

Bullet Impact Detection in Silhouettes Using Mask R-CNN

Array signal processing and systems

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