Fault-tolerant and energy efficient cross-layer design for wireless sensor networks
International Journal of Sensor Networks
Emergency guiding and monitoring applications in indoor 3D environments by wireless sensor networks
International Journal of Sensor Networks
Efficient information compression in sensor networks
International Journal of Sensor Networks
Sparse Bayesian learning for basis selection
IEEE Transactions on Signal Processing
Water temperature sensing with microtomography
International Journal of Sensor Networks
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Recently, Compressive Sensing has become a highly attractive technique for SAR imaging since it outperforms existing methods. In this paper, a Sparse Bayesian Learning based approach in Compressive Sensing framework is proposed for SAR sensor network imaging to reduce the required number of sensors and to obtain super-resolution in the elevation direction. Specifically, the Trench-Zohar inversion is also adapted to the normal Sparse Bayesian Learning algorithm to reduce the computation time and storage requirements. The advanced efficiency of the proposed approach is validated by results achieved from different simulations.