Abstract
It is a great challenge for wireless sensor network to provide enough information for targets localization due to the limits on application environment and its nature, such as energy, communication, and sensing precision. In this paper, a multiple targets localization algorithm with sparse information (MTLSI) was proposed using compressive sensing theory, which can provide targets position with incomplete or sparse localization information. It does not depend on extra hardware measurements. Only targets number detected by sensors is needed in the algorithm. The monitoring region was divided into a plurality of small grids. Sensors and targets are randomly dropped in grids. Targets position information is defined as a sparse vector; the number of targets detected by sensor nodes is expressed as the product of measurement matrix, sparse matrix, and sparse vector in compressive sensing theory. Targets are localized with the sparse signal reconstruction. In order to investigate MTLSI performance, BP and OMP are applied to recover targets localization. Simulation results show that MTLSI can provide satisfied targets localization in wireless sensor networks application with less data bits transmission compared to multiple targets localization using compressive sensing based on received signal strengths (MTLCS-RSS), which has the same computation complexity as MTLIS.
Highlights
Dramatic advances in wireless communication and microelectromechanical-system fabrication technology have enabled the use of wireless sensor networks (WSNs)
The targets position is defined as a sparse vector in the discrete space and the number of detected targets by sensor nodes is expressed as the product of measurement matrix, sparse matrix, and sparse vector in compressive sensing theory
The positions of target are represented as a sparse vector, and the number of detected targets by sensor nodes is expressed as the product of measurement matrix, sparse matrix, and sparse vector in compressive sensing theory
Summary
Dramatic advances in wireless communication and microelectromechanical-system fabrication technology have enabled the use of wireless sensor networks (WSNs). Localization plays a vital role in wireless sensor networks design and application. Limited by environmental factors, information extraction technology, and uncertainly communication networks, the physical information for localization presents strong incompleteness [3], which brings great challenge for wireless sensor networks design. Considering the limit of the energy of wireless sensor network and information incompleteness, novel targets localization method using sparse information becomes one of hot topics in WSNs. Owing to the recent advances in sparse signal reconstruction for compressive sensing (CS), in this study, we consider the target locations as a sparse signal and reconstruct the signal using the CS technique. Considering binary-detected model, a multiple target localization algorithm with sparse information (MTLSI) is proposed using compressive sensing theory. Localization performance is analyzed with different sensing radius, nodes quantity, targets quantity, and measurement noise.
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