Analysis of RBF cascade network for sparse signal recovery and application in telemetry

Analysis of cascade network consisting of RBF nodes and least square error minimization block for compressed sensing recovery of sparse signals is presented in this paper. The proposed algorithm radial basis function cascade network for sparse signal recovery uses the L₀ norm optimization, L₂ least square method and feedback network model to improve the signal recovery performance and computational time over the existing ANN based and SL0 algorithms. The recovery of spike and pulse current measurement from simulated compressed sensed data for Telemetry application is demonstrated using the new algorithm. The Simulink model for compressed sensing data acquisition process, sparse signal recovery results and algorithm performance evaluation are presented.