Relative Self-Calibration of Wireless Acoustic Sensor Networks Using Dual Positioning Mobile Beacon

• Published in: IEEE systems journal Vol. 12; no. 1; pp. 862 - 870
• Main Authors: Anwar, Malik Aqeel, Siddique, Abu Bakar, Tahir, Muhammad
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustic measurements; Acoustic noise; Acoustics; Angle of arrival; Calibration; direction of arrival estimation; Localization; Maximum likelihood estimation; maximum likelihood estimation (MLE); Microphones; Nickel; Position (location); RF signals; Satellite navigation systems; sensor systems and applications; Sensors; Wireless networks
• ISSN: 1932-8184; 1937-9234
• DOI: 10.1109/JSYST.2016.2564987

Sensor nodes deployed for event localization are required to be location aware. When event of interest is acoustic, node calibration (involving localization and orientation) during deployment phase become significant for accurate event localization during normal operation. This paper proposes a novel approach for node calibration in an acoustic surveillance network. Each node is equipped with three microphones and an RF transceiver. Nodes are calibrated for their relative positions and orientations by transmitting acoustic and RF signals, simultaneously, from beacon nodes at unknown locations. The acoustic signal, received by microphone array, is used for angle of arrival measurement. While an RF signal is transmitted for time difference of arrival measurement, which is used to quantify distance between beacon and sensor nodes. Proposed approach is attractive as it does not require expensive on board GPS module and compass. The effectiveness of proposed scheme is demonstrated using experimental setup comprising five sensor nodes and one beacon node. Maximum likelihood estimation is employed to determine network configuration under noisy measurements. Simulations are performed to observe the effect of network parameters, such as node density on node position estimation accuracy.