Abstract
Target tracking is usually a challenging application for wireless sensor networks(WSNs) because it is always computation-intensive and requires real-time processing. Thispaper proposes a practical target tracking system based on the auto regressive movingaverage (ARMA) model in a distributed peer-to-peer (P2P) signal processing framework.In the proposed framework, wireless sensor nodes act as peers that perform target detection,feature extraction, classification and tracking, whereas target localization requires thecollaboration between wireless sensor nodes for improving the accuracy and robustness.For carrying out target tracking under the constraints imposed by the limited capabilities ofthe wireless sensor nodes, some practically feasible algorithms, such as the ARMA modeland the 2-D integer lifting wavelet transform, are adopted in single wireless sensor nodesdue to their outstanding performance and light computational burden. Furthermore, aprogressive multi-view localization algorithm is proposed in distributed P2P signalprocessing framework considering the tradeoff between the accuracy and energyconsumption. Finally, a real world target tracking experiment is illustrated. Results fromexperimental implementations have demonstrated that the proposed target tracking systembased on a distributed P2P signal processing framework can make efficient use of scarceenergy and communication resources and achieve target tracking successfully.
Highlights
Wireless sensor networks (WSNs) are being envisioned and developed for a variety of applications involving monitoring and manipulation of the physical world in a tetherless fashion Typically, each individual sensor node can sense in multiple modalities but has limited signal processing and communication capabilities
For reducing the computation complexity in estimating the auto regressive (AR) and moving average (MA) coefficients, the AR coefficients ai is estimated by using the robust singular value decomposition (SVD) based linear predictive coding algorithm (LPCA) method, while the MA coefficients bi are estimated by minimizing the error between the actual measurements and the weighted impulse response sequence generated by the estimated denominator coefficients as usual [22]
From the comparison of tracking performance, time delay and energy consumption between distributed P2P framework, distributed client/server framework and centralized client/server framework, it is obvious that the proposed distributed P2P framework and the proposed combined tracking system based on background subtraction, 2-D integer lifting wavelet transform (ILWT), support vector machine (SVM), auto regressive moving average (ARMA) model and multi-view localization algorithms can succeed in robust multi-target tracking in WSNs
Summary
Wireless sensor networks (WSNs) are being envisioned and developed for a variety of applications involving monitoring and manipulation of the physical world in a tetherless fashion Typically, each individual sensor node can sense in multiple modalities but has limited signal processing and communication capabilities. We focus our research efforts on the implementation of a visual multi-view target tracking system in WSNs with a spotlight on distributed peer-to-peer (P2P) signal processing and the specific algorithms which can be successfully adopted under the constraints imposed by the limited communication and computational abilities of the sensor nodes as well as their finite battery life. Regressive moving average (ARMA) model based target tracking are carried out in each sensor node, while multi-view localization algorithm is implemented with the collaboration between wireless sensor nodes in a distributed P2P signal processing framework.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
