Abstract
A novel sensor network source localization method based on acoustic energy measurements is presented. This method makes use of the characteristics that the acoustic energy decays inversely with respect to the square of distance from the source. By comparing energy readings measured at surrounding acoustic sensors, the source location during that time interval can be accurately estimated as the intersection of multiple hyperspheres. Theoretical bounds on the number of sensors required to yield unique solution are derived. Extensive simulations have been conducted to characterize the performance of this method under various parameter perturbations and noise conditions. Potential advantages of this approach include low intersensor communication requirement, robustness with respect to parameter perturbations and measurement noise, and low-complexity implementation.
Highlights
Distributed networks of low-cost microsensors with signal processing and wireless communication capabilities have a variety of applications [1, 2]
When the sound is propagating through the air, it is known that [15] the acoustic energy emitted omnidirectionally from a sound source will attenuate at a rate that is inversely proportional to the square of the distance
Based on the above two experiments, one may deduce the following guidelines for the proper implementation of the energy-based acoustic source localization algorithm: (i) proper definition of the sensor field where the potential target localization will lie; (ii) careful calibration of sensor gain factor; (iii) use one of the fast search algorithm MR, Gradient descent (GD), or simplex direct search (DS) method after first conducting a coarse-grained exhaustive search (ES) within the sensor field; (iv) using few reliable energy readings from a few sensor is preferred to using many unreliable energy readings from more sensors
Summary
Distributed networks of low-cost microsensors with signal processing and wireless communication capabilities have a variety of applications [1, 2]. Examples include under water acoustics, battlefield surveillance, electronic warfare, geophysics, seismic remote sensing, and environmental monitoring Such sensor networks are often designed to perform tasks such as detection, classification, localization, and tracking of one or more targets in the sensor field. This is formulated as a nonlinear optimization problem of which fast optimization search algorithms are available This proposed energy-based localization (EBL) method will potentially give accurate results at regular time interval, and will be robust with respect to parameter perturbations. It requires relatively few computations and consumes little communication bandwidth, and is suitable for low power distributed wireless sensor network applications. Gorithms applied on our energy-based localizer is reported
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