Efficient cross-correlation via sparse representation in sensor networks
Proceedings of the 11th international conference on Information Processing in Sensor Networks
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Cross-correlation is a popular signal processing technique used for obtaining reliable range information. Recently, a practical and efficient implementation of cross-correlation (via sparse approximation) was demonstrated on resource constrained wireless sensor network platforms, where the key idea was to compress the received signal samples, and transfer them to central device where the range information was retrieved by l1-minimization. Although, this mechanism yields accurate ranging results, its applicability is limited due to its slow execution speed and inaccurate recovery of the correlation peak magnitude, which implicitly provides the useful measure of signal-to-noise ratio. In this work, we propose Fast Gradient Projection (F-GP), a new l1-minimization algorithm, which overcomes the existing limitations, and provides fast and accurate ranging.