The design and implementation of a self-calibrating distributed acoustic sensing platform

Abstract

We present the design, implementation, and evaluation of the Acoustic Embedded Networked Sensing Box (ENSBox), a platform for prototyping rapid-deployable distributed acoustic sensing systems, particularly distributed source localization. Each ENSBox integrates an ARM processor running Linux and supports key facilities required for source localization: a sensor array, wireless network services, time synchronization, and precise self-calibration of array position and orientation. The ENSBox's integrated, high precision self-calibration facility sets it apart from other platforms. This self-calibration is precise enough to support acoustic source localization applications in complex, realistic environments: e.g., 5 cm average 2D position error and 1.5 degree average orientation error over a partially obstructed 80x50 m outdoor area. Further, our integration of array orientation into the position estimation algorithm is a novel extension of traditional multilateration techniques. We present the result of several different test deployments, measuring the performance of the system in urban settings, as well as forested, hilly environments with obstructing foliage and 20-30 m distances between neighboring nodes.

Modeling and Optimizing Positional Accuracy Based on Hyperbolic Geometry for the Adaptive Radio Interferometric Positioning System

“…The major differences among them are the calibration methods and the usage of signal sources, such as sonic, ultrasonic, infrared, camera, RF, etc. For example, Acoustic ENSBox [16] develops a distribution acoustic sensing platform, which an acoustic embedded networked sensing box can be rapidly deployed and perform self-calibration. It claims to achieve 5 centimeters positional accuracy in a partially obstructed 80m×50m outdoor.…”

“…To address this need, there have been many sensor network localization systems utilizing different sensing techniques, e.g., MoteTrack [9], Cricket [10], Spotlight [13], APIT [14], ENSBox [16], etc. Among them, the radio interferometric positioning (RIP) method from the NEST project [1] has shown a promising, exciting location sensing technique for sensor network applications.…”

A Survey on Localization for Mobile Wireless Sensor Networks

“…The localization techniques use diverse hardware, algorithms and signal modalities, which can be categorized along several different dimensions. We start by describing the three phases typically used in localization ( [26], [27], [28]): (1) coordination, (2) measurement, and (3) position estimation. We then focus on other aspects of MWSN-based localization, such as the effects of mobility, centralized versus distributed processing, and the environment.…”

Whistle: Synchronization-Free TDOA for Localization

“…Existing work basically use three types of measurements of acoustic signals: TDOA [24], [19], [32], [9], DOA (direction of arrival) [22], [14], [12], [15] and received signal energy [25], [26], [4], [21], [3].…”