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| Online Estimation of Sparse Time-varying Signals with Chaotic Compressive Sensing |
| Chen Sheng-yao Xi Feng Liu Zhong |
| Department of Electronic Engineering, Nanjing University of Science and Technology, Nanjing 210094, China |
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Abstract Chaotic Compressive Sensing (ChaCS) is a nonlinear compressive sensing approach using chaos systems. This paper extends the ChaCS to perform the online estimation of sparse time-varying signals. An online estimation structure is proposed and a sparsity-constrained recursive least-squares objective function is formulated. The sparse time-varying signals are estimated through iterative reweighted nonlinear least-square algorithm by minimizing the objective function. The Henon system is taken as examples to expose the estimation performance of frequency sparse time-varying signals. Numerical simulations illustrate the effectiveness of the proposed method.
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Received: 26 June 2011
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Corresponding Authors:
Liu Zhong
E-mail: eezliu@mail.njust.edu.cn
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2012, Vol. 34 
