scholarly journals Gunshot Airborne Surveillance with Rotary Wing UAV-Embedded Microphone Array

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4271 ◽  
Author(s):  
Felipe Gonçalves Serrenho ◽  
José Antonio Apolinário ◽  
António Luiz Lopes Ramos ◽  
Rigel Procópio Fernandes
Keyword(s):  
Microphone Array ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Audio Signals ◽  
Digital Map ◽  
Field Recordings ◽  
Technological Advances ◽  
Muzzle Blast ◽  
New Applications ◽  
Rotary Wing

Unmanned aerial vehicles (UAV) are growing in popularity, and recent technological advances are fostering the development of new applications for these devices. This paper discusses the use of aerial drones as a platform for deploying a gunshot surveillance system based on an array of microphones. Notwithstanding the difficulties associated with the inherent additive noise from the rotating propellers, this application brings an important advantage: the possibility of estimating the shooter position solely based on the muzzle blast sound, with the support of a digital map of the terrain. This work focuses on direction-of-arrival (DoA) estimation methods applied to audio signals obtained from a microphone array aboard a flying drone. We investigate preprocessing and different DoA estimation techniques in order to obtain the setup that performs better for the application at hand. We use a combination of simulated and actual gunshot signals recorded using a microphone array mounted on a UAV. One of the key insights resulting from the field recordings is the importance of drone positioning, whereby all gunshots recorded in a region outside a cone open from the gun muzzle presented a hit rate close to 96%. Based on experimental results, we claim that reliable bearing estimates can be achieved using a microphone array mounted on a drone.

A fusion method based on class rotations for DNN-DoA Estimation on Spherical Microphone Array

2021 ◽  
Author(s):  
Israel Mendoza Velazquez ◽  
Yi Ren ◽  
Yoichi Haneda ◽  
Hector Manuel Perez Meana
Keyword(s):  
Microphone Array ◽  
Doa Estimation ◽  
Fusion Method

scholarly journals A Novel Coherence Reduction Method in Compressed Sensing for DOA Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Jing Liu ◽  
ChongZhao Han ◽  
XiangHua Yao ◽  
Feng Lian
Keyword(s):  
Compressed Sensing ◽  
Phased Array ◽  
Reduction Method ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Sensing Matrix ◽  
Replacement Method ◽  
Precise Estimation ◽  
Novel Method ◽  
Measurement Vector

A novel method named as coherent column replacement method is proposed to reduce the coherence of a partially deterministic sensing matrix, which is comprised of highly coherent columns and random Gaussian columns. The proposed method is to replace the highly coherent columns with random Gaussian columns to obtain a new sensing matrix. The measurement vector is changed accordingly. It is proved that the original sparse signal could be reconstructed well from the newly changed measurement vector based on the new sensing matrix with large probability. This method is then extended to a more practical condition when highly coherent columns and incoherent columns are considered, for example, the direction of arrival (DOA) estimation problem in phased array radar system using compressed sensing. Numerical simulations show that the proposed method succeeds in identifying multiple targets in a sparse radar scene, where the compressed sensing method based on the original sensing matrix fails. The proposed method also obtains more precise estimation of DOA using one snapshot compared with the traditional estimation methods such as Capon, APES, and GLRT, based on hundreds of snapshots.

scholarly journals Low Complexity Sparse Beamspace DOA Estimation Via Single Measurement Vectors for Uniform Circular Array

2021 ◽  
Author(s):  
Di Zhao ◽  
Weijie Tan ◽  
Zhongliang Deng ◽  
Gang Li
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Estimation Methods ◽  
Circular Array ◽  
Single Measurement ◽  
Signal Model ◽  
Convex Optimization Problem

Abstract In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.

scholarly journals Minimum Array Elements for Resolution of Several Direction of Arrival Estimation Methods in Various Noise-Level Environments

2018 ◽  
Vol 2 ◽  
pp. 87-94
Author(s):  
Ismail El Ouargui ◽  
Said Safi ◽  
Miloud Frikel
Keyword(s):  
Gaussian Noise ◽  
Noise Level ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Minimum Number ◽  
Informative Signals

The resolution of a Direction of Arrival (DOA) estimation algorithm is determined based on its capability to resolve two closely spaced signals. In this paper, authors present and discuss the minimum number of array elements needed for the resolution of nearby sources in several DOA estimation methods. In the real world, the informative signals are corrupted by Additive White Gaussian Noise (AWGN). Thus, a higher signal-to-noise ratio (SNR) offers a better resolution. Therefore, we show the performance of each method by applying the algorithms in different noise level environments.

Comparison of Estimation Methods of Room Impulse Responses in Local Region Using Small Number of Microphones

2021 ◽  
Vol 263 (2) ◽  
pp. 4598-4607
Author(s):  
Haruka Matsuhashi ◽  
Izumi Tsunokuni ◽  
Yusuke Ikeda
Keyword(s):  
Optimization Problems ◽  
Microphone Array ◽  
Estimation Methods ◽  
Impulse Responses ◽  
Multiple Points ◽  
Acoustic Characteristics ◽  
Plane Wave Decomposition ◽  
Acoustic Techniques ◽  
Wave Decomposition ◽  
Reflection Component

Measurements of Room Impulse Responses (RIRs) at multiple points have been used in various acoustic techniques using the room acoustic characteristics. To obtain multi-point RIRs more efficiently, spatial interpolation of RIRs using plane wave decomposition method (PWDM) and equivalent source method (ESM) has been proposed. Recently, the estimation of RIRs from a small number of microphones using spatial and temporal sparsity has been studied. In this study, by using the measured RIRs, we compare the estimation accuracies of RIRs interpolation methods with a small number of fixed microphones. In particular, we consider the early and late reflections separately. The direct sound and early reflection components are represented using sparse ESM, and the late reflection component is represented using ESM or PWDM. And then, we solve the two types of optimization problems: individual optimization problems for early and late reflections decomposed by the arrival time and a single optimization problem for direct sound and all reflections. In the evaluation experiment, we measured the multiple RIRs by moving the linear microphone array and compare the measured and estimated RIRs.

Efficiency Assessment of University-Industry Collaboration

2021 ◽  
pp. 155-175
Author(s):  
Ebru Yüksel Haliloğlu
Keyword(s):  
Industrial Development ◽  
Academic Research ◽  
Data Envelopment ◽  
Academic Knowledge ◽  
Technological Advances ◽  
Industry Collaboration ◽  
Close Cooperation ◽  
Using Data ◽  
New Applications ◽  
University Industry

Today, in addition to teaching and research roles, universities are one of major drivers of economic development and technological progress in society. To propagate technological innovation and industrial development, to implement output of academic research in practice universities should be in close cooperation with industry. University-industry collaborations have various benefits both for universities and industry. Universities gain additional funds for academic research, apply academic knowledge to industry; industry benefits from skilled human resources, new applications, and technological advances. Since university-industry collaborations have great mutual benefits for all partners, it is important to administer these operations effectively. Therefore, it is central to develop some efficiency indicators and efficiency measurement methods so that productive projects can be selected and funded more. This study aims to outline a framework on determinants of university-industry collaboration efficiency and construct a benchmark model to evaluate it using data envelopment analysis.

An IoT-Based Platform for Rehabilitation Monitoring and Biosignal Identification

2018 ◽  
Vol 6 (1) ◽  
pp. 1-19
Author(s):  
Volkhard Klinger
Keyword(s):  
Embedded System ◽  
Medical Technology ◽  
State Of The Art ◽  
Technological Advances ◽  
Measurement Signal ◽  
The Embedded System ◽  
Prosthesis Control ◽  
New Applications ◽  
The Given ◽  
The Internet Of Things

This article describes how as a result of technological advances of the embedded system, the Internet-of-Things (IoT) has created a wealth of new applications and tailored solutions, even in the area of health and medical technology. The integration of state-of-the-art IoT-systems in an existing prototype platform for biosignal acquisition, identification, and prosthesis control provides new applications for prevention and rehabilitation monitoring. This article concentrates on an IoT-based platform for rehabilitation monitoring and biosignal identification. The IoT-characteristics for the application in the area of medical technology are discussed and the integration of such IoT-modules in the given architecture is introduced. Based on this extended architecture, new applications in the field of biosignal measurement, signal processing and biosignal monitoring are presented. Some results of a rehabilitation monitoring system, based on a self-designed IoT-module, integrated in the whole platform, are shown.

scholarly journals Two 2-D DOA Estimation Methods with Full and Partial Generalized Virtual Aperture Extension Technology

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Riheng Wu ◽  
Yangyang Dong ◽  
Zhenhai Zhang ◽  
Le Xu
Keyword(s):  
Data Model ◽  
Doa Estimation ◽  
Nonsingular Matrix ◽  
Eigenvalue Decomposition ◽  
Estimation Methods ◽  
Sparse Reconstruction ◽  
Invariance Property ◽  
Reconstruction Method ◽  
Uniqueness Of Solutions ◽  
Moderate Complexity

We address the two-dimensional direction-of-arrival (2-D DOA) estimation problem for L-shaped uniform linear array (ULA) using two kinds of approaches represented by the subspace-like method and the sparse reconstruction method. Particular interest emphasizes on exploiting the generalized conjugate symmetry property of L-shaped ULA to maximize the virtual array aperture for two kinds of approaches. The subspace-like method develops the rotational invariance property of the full virtual received data model by introducing two azimuths and two elevation selection matrices. As a consequence, the problem to estimate azimuths represented by an eigenvalue matrix can be first solved by applying the eigenvalue decomposition (EVD) to a known nonsingular matrix, and the angles pairing is automatically implemented via the associate eigenvector. For the sparse reconstruction method, first, we give a lemma to verify that the received data model is equivalent to its dictionary-based sparse representation under certain mild conditions, and the uniqueness of solutions is guaranteed by assuming azimuth and elevation indices to lie on different rows and columns of sparse signal cross-correlation matrix; we then derive two kinds of data models to reconstruct sparse 2-D DOA via M-FOCUSS with and without compressive sensing (CS) involvements; finally, the numerical simulations validate the proposed approaches outperform the existing methods at a low or moderate complexity cost.

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