Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Journal of Parallel and Distributed Computing
Fine-grained network time synchronization using reference broadcasts
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Speech enhancement based on a priori signal to noise estimation
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
On Computing Mobile Agent Routes for Data Fusion in Distributed Sensor Networks
IEEE Transactions on Knowledge and Data Engineering
Energy based acoustic source localization
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Acoustic target tracking using tiny wireless sensor devices
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Scalable and low-cost acoustic source localization for wireless sensor networks
UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
IEEE Communications Magazine
International Journal of Ad Hoc and Ubiquitous Computing
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This study devises and evaluates an energy-efficient distributed collaborative signal and information processing framework for acoustic target tracking in wireless sensor networks. The distributed processing algorithm is based on mobile agent computing paradigm and sequential Bayesian estimation. At each time step, the short detection reports of cluster members will be collected by cluster head, and a sensor node with the highest signal-to-noise ratio (SNR) is chosen there as reference node for time difference of arrive (TDOA) calculation. During the mobile agent migration, the target state belief is transmitted among nodes and updated using the TDOA measurement of these fusion nodes one by one. The computing and processing burden is evenly distributed in the sensor network. To decrease the wireless communications, we propose to represent the belief by parameterized methods such as Gaussian approximation or Gaussian mixture model approximation. Furthermore, we present an attraction force function to handle the mobile agent migration planning problem, which is a combination of the node residual energy, useful information, and communication cost. Simulation examples demonstrate the estimation effectiveness and energy efficiency of the proposed distributed collaborative target tracking framework.