Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

Knowing number of nodes is the precondition of their locating and estimation of other parameters in wireless sensor network, actually it can be evaluated according to the signal received by the sensor array. The popular algorithms are only appropriate for the uncorrelated signals and circumstance of Gaussian white noise, the estimation precision will deteriorate at some harsh environments. Hence a new algorithm for determining coherent node number based on multiple feature extraction in wireless sensor network is presented in this article. First, perform spatial difference smoothing to the array data. Then the eigenvalues and eigenvectors can be acquired by eigen-decomposition, consequently multiple features which are used for training are constructed. Finally, back propagation neural network and particle swarm optimization are exploited for calculating node number. Simulation results demonstrate that the algorithms has good performances under the background of colored noise and small samples.