Compress sensing algorithm for estimation of signals in sensor networks

Abstract

In this research, we present a data recovery scheme for wireless sensor networks. In some sensor networks, each node must be able to recover the complete information of the network, which leads to the problem of the high cost of energy in communication and storage of information. We proposed a modified gossip algorithm for acquire distributed measurements and communicate the information across all nodes of the network using compressive sampling and Gossip algorithms to compact the data to be stored and transmitted through a network. The experimental results on synthetic data show that the proposed method reconstruct better the signal and in less iterations than with a similar method using a thresholding algorithm.

ADMM–Net: A Deep Learning Approach for Parameter Estimation of Chirp Signals Under Sub-Nyquist Sampling

“…The difference in the estimation accuracy between components is caused by the difference in modulation parameters, which is attributed to Eqn. (8). The threshold value is nearly the same as that of the mono-component chirp signal in Experiment 1, which indicates that ADMM-Net still maintains high noise resistance and information recovery ability to multi-component signals.…”

“…, θ (p L ) T , L = 200, p = 0.01 : 0.01 : 2, andk andf 0 are obtained through Eqn. (8). The evaluation criterion is the mean absolute percentage error as follows:…”

“…Compressed sensing (CS) has brought new ideas to chirp signal processing. Most applications of CS theory for signal processing are for accurate reconstruction of signals [8]- [11]. However, in the application of CS theory for signal parameter estimation, with the help of a specific parameter matching dictionary, the estimation task can be achieved without complete reconstruction of signals [12]- [17].…”