Signals Reconstruction in Heterogeneous Sensor Network with Distributed Compressive Sensing
In this paper, we reconstruct signals in heterogeneous sensor network (HSN) with distributed compressive sensing (DCS) Combining different types of measurement matrices and different numbers of measurements, we investigate three different scenarios in which HSN is used to acquiring signals for the first time. In the first scenario, there are two different types of measurement matrices. One is Gaussian measurement and the other is Fourier measurement, and each sensor applies the same numbers of measurements. In the second scenario, all sensors use the same type of measurement matrices but the number of measurements are different from each other. The third scenario combines different types of measurement matrix and distinct numbers of measurements. Our simulation results show that in Scenario I, when the common sparsity is considerable, the DCS scheme can reduce the number of measurements. In Scenario II, the reconstruction situation becomes better with the increase of the number of measurements. In both Scenario I and III, joint decoding that use different types of measurement matrices performs better than that of all Gaussian measurement matrices, but it performs worse than that of all Fourier measurement matrices. Therefore, DSC is a good compromise between reconstruction percentage and the number of measurements in HSN.