###### Abstract
By contrast, iterative greedy methods, proposed for handling the \(l_{0}\)-norm minimization, show advantages with low computational complexity and superior visual interpretation, and have aroused much attention. The conventional greedy methods require prior knowledge of the sparse signal and perform expensively [14; 15; 16; 17; 18]. Sparsity adaptive matching pursuit (SAMP) does not rely on sparsity level [19]. With the potential performance advancement of SAMP, an increasing  
Figure 1: Comparison of different monitoring schemes for vehicle. (**a**) The conventional monitoring scheme; (**b**) Real-time vehicle health monitoring using CS. number of researchers have explored adaptive matching pursuit algorithms [20; 21; 22; 23; 24; 25]. However, these algorithms do not employ the preselection step, i.e., making no initial estimation for sparsity level during the initial phase. This leads to poor reconstruction performance. In addition, they may hardly adopt appropriate variable step-size and do not further filter results for higher accuracy. Although these algorithms can perform reconstruction with rare prior information of signal sparsity level, the accuracy and efficiency of reconstruction still need to be greatly improved, especially for vehicle health monitoring. To overcome problems mentioned above, we propose a novel greedy algorithm called filtering-based regularized sparsity variable step-size matching pursuit (FRSVs8 MP), which is capable of signal reconstruction with requiring no prior knowledge on the sparsity level. With the proposed initial estimation approach for signal sparsity level, this algorithm integrates strategies of regularization and variable step-size adaptive, and further filters reconstruction results. To verify the efficiency of FRSVs8 MP in reconstruction, we conducted lots of experiments on the typical vehicle signals. The results demonstrate that the proposed FRSVs8 MP surpasses other greedy algorithms both in terms of accuracy and efficiency. The proliferation of smart transportation has significantly promoted explosive growth of vehicles. Due to the sophisticated and complex electronics, vehicles are inevitably susceptible to various faults. Especially, with the rapid development of intelligent sensors, large numbers of heterogeneous vehicle status data impose heavy pressures in health monitoring. In order to ensure the reliable operation of vehicles and identify potential faults efficiently, FRSVs8 MP can serve as a promising paradigm to facilitate the transmission and analysis of those data. The contributions of this paper are three-fold, as follows:  
* Firstly, we put forward an initial estimation approach for signal sparsity level to exploit structure of the sparse signal. * Secondly, we propose a novel iterative greedy algorithm relying on no prior information of signal sparsity level. The algorithm integrates strategies of regularization and variable adaptive step size. Further, the original signal can be reconstructed precisely with the filtering mechanism. * Thirdly, extensive numerical simulations are conducted to verify the performance of the proposed algorithm for real-time health monitoring. Experimental results demonstrate that FRSVs8 MP significantly outperforms the state-of-the-art greedy algorithms. The remainder of this paper is organized as follows. The basic model of CS and representative greedy algorithms are illustrated in Section 2. Section 3 gives the detailed description of sparsity level estimation approach and the proposed reconstruction algorithm. Simulation results and discussions are presented with typical disturbance signals, including synthetic sparse signals and real sparse signals in monitoring for the vehicle power system in Section 4, followed by the conclusion in Section 5.