Julia Berdnikova

Simplified acoustic localization by linear arrays for wireless sensor networks

The application of wireless sensor networks (WSN) for the task of acoustic localization provides great opportunities for distributed cooperative tracking of sound sources in large areas. However WSNs are significantly more limited in terms of computational resources and power than typical computer systems. Therefore the methods applied for acoustic localization in WSN must be optimized for minimal resource consumption. This paper builds on the advances of Steered Response Power with Phase Transform (SRP-PHAT) optimization and proposes a further simplification in terms of additional minimization of the initial search volume. By using several linear microphone arrays we are able to estimate the initial region of sound source and reduce the number of computations by at least one order of magnitude. The results of several experiments on real signals confirm the achieved improvements.

A two-stage approach to 2D DOA estimation for a compact circular microphone array

Microphone arrays and, specifically, circular arrays have been used for sound source localization and multimedia applications for more than a decade. In recent years the development of compact arrays for implementation in Wireless Sensor Networks (WSN) has risen in popularity. This paper considers a 2D Direction of Arrival (DOA) estimation method for a compact circular array, equipped with additional vertically placed microphones. The proposed method is aimed at reducing the computational cost of DOA estimation for implementation on embedded hardware of WSN smart sensors. The method is compared with a well known localization algorithm of SRP-PHAT and is proven to provide adequate DOA estimates, while being more computationally effective.

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

Gunshot acoustic localization for military and civilian security systems has long been an important topic of research. In recent years the development of Wireless Sensor Network (WSN) systems of independent Unmanned Ground Sensors (UGS) performing distributed cooperative localization has grown in popularity. This paper considers a shooter localization approach based on gunshot Shockwave (SW) and Muzzle Blast (MB) event time and Direction of Arrival (DOA) information. The approach accounts for acoustic events Not-of-Interest (NOI), such as target hit noise, reflections and background noise. UGS perform gunshot acoustic event detection and DOA estimation independently; the information regarding every detected shot instance is sent through the WSN to the fusion node, which performs event identification and calculates the shooter’s position. The paper presents a solution to identifying SW and MB among NOI events at the stage of information fusion. The considered approach treats the information gathered from different UGS separately, and thus does not require precise synchronization between the UGS. For DOA estimation, an algorithm designed for circular microphone arrays is proposed and compared with the SRP-PHAT localization algorithm. It is shown to provide adequate DOA estimates, while being more computationally effective. The proposed shooter localization approach is tested on real signals, acquired during three live shooting experiments. It is shown to succeed in localizing the shooter’s position with a mean accuracy of 0.87 m for 30 shots at the range of 35 m, and just above 7 m for 37 shots at the range of 100 m.

Gunshot acoustic component localization with distributed circular microphone arrays

Gunshot acoustic localization for military and urban security systems has long been an important topic of research. In recent years the development of independent Unmanned Ground Sensors (UGS), interconnected through Wireless Sensor Networks (WSN), performing distributed cooperative localization, has grown in popularity. This paper considers a 2D Direction of Arrival (DOA) estimation method for compact circular array UGS, establishing gunshot direction at close range, and discusses problems, situated with gunshot acoustic component analysis. The proposed method is aimed at reducing the computational cost of DOA calculation for implementation on embedded hardware of WSN smart sensors. It is compared with the SRP-PHAT localization algorithm and is proven to provide adequate DOA estimates, while being more computationally effective.

Shooter localization by networked multichannel acoustic ground sensors

Gunshot acoustic localization for military and urban security systems has long been an important topic of research. In recent years the development of independent Unmanned Ground Sensors (UGS), interconnected through Wireless Sensor Networks (WSN), performing distributed cooperative localization, has grown in popularity. This paper proposes an asynchronous method of gunshot localization, performed by UGS, equipped with circular microphone arrays. Each UGS in the WSN estimates the Direction of Arrival (DOA) of acoustic events and the time delay between these events. Fusion nodes perform event identification, accounting for outliers (e.g. target hit noise), and shooter localization, based on gathered event information and WSN geometry. The approach is tested on real signals, acquired at a shooting range, and succeeds in localizing the shooters position with a mean accuracy of 0.87 meters for 30 shots.

Predictive acoustic localization and speaker tracking for distributed sensor networks

Acoustic source localization has a variety of applications, from speech signal enhancement to mobile object tracking. Localization procedures applying lightweight algorithms are appealing for implementation in distributed embedded systems. This paper builds on our previous research of simplifying acoustic localization based on the Steered Response Power with Phase Transform (SRP-PHAT) algorithm and proposes further enhancement by introducing object trajectory tracking and coordinate prediction. For these purposes we integrate Kaiman and Rao-Blackwellized particle filters into our simplified approach. Filter predictions serve as target areas for acoustic source search during the subsequent time frames, supporting localization. The approach is applied to moving speaker tracking and displays promising results.