Extraction Of Environmental Characteristics Based On Compressive Sensing In Wireless Networks

Compressed sensing is a new data acquisition program first proposed by the Donoho and Candes in 2006. Under the theory of compressed sensing signal it can be made much lower than the Nyquist sampling rate (Nyquist) sampling rate and meet certain conditions, to a very low error rate to recover the original signal, which can greatly reduce sampling costs. Distinguishing Locations Across Perimeters based on the Wireless Link Signature and received signal strength and indoor positioning and other wireless network environment application need to collect a lot of data to the original environmental characteristics or experiments, but a lot of time collecting data resources, time and space limited, which we need to use the limited conditions to get more data to improve the accuracy of the application.In order to use the limited accuracy of the conditions to get more sampling data to improve the application, we propose environmental characteristics in a wireless network based on compressed sensing extraction program. The program with a small amount of data compressed sensing algorithm to recover the amount of data a lot more than their own, while the study compared the pros and cons of a variety of algorithms, sparse representation of data and propose estimate method based on statistics.Papers proposed scheme is implemented in MATLAB. Simulation results show that:compressed sensing algorithm can be implemented by a small number of environmental features to recover the additional environmental characteristics under certain conditions, and to achieve a certain degree of accuracy in comparison with the actual measured values.