###### Abstract  
Recent years have witnessed that real-time health monitoring for vehicles is gaining importance. Conventional monitoring scheme faces formidable challenges imposed by the massive signals generated with extremely heavy burden on storage and transmission. To address issues of signal sampling and transmission, compressed sensing (CS) has served as a promising solution in vehicle health monitoring, which performs signal sampling and compression simultaneously. Signal reconstruction is regarded as the most critical part of CS, while greedy reconstruction has been a research hotspot. However, the existing approaches either require prior knowledge of the sparse signal or perform with expensive computational complexity. To exploit the structure of the sparse signal, in this paper, we introduce an initial estimation approach for signal sparsity level firstly. Then, a novel greedy reconstruction algorithm that relies on no prior information of sparsity level while maintaining a good reconstruction performance is presented. The proposed algorithm integrates strategies of regularization and variable adaptive step size and further performs filtration. To verify the efficiency of the algorithm, typical voltage disturbance signals generated by the vehicle power system are taken as trial data. Preliminary simulation results demonstrate that the proposed algorithm achieves superior performance compared to the existing methods. compressed sensing; sparsity level estimation; reconstruction; matching pursuit; health monitoring 11 2021 22 May 2021 20  
and compression, CS has been widely employed in the fields of wireless communications [5], image and data processing [6; 7], data compression [8], signal denoising [9; 10], especially in the real-time monitoring and analysis of vehicle signal quality [11; 12; 13]. Vehicle is an organic coupled composition which consists of different sophisticated subsystems. With the explosive growth of ubiquitous vehicles, large numbers of sophisticated and complex devices are continually integrated to vehicle electronics. This leads to the essential need that we should acquire a sufficiently high-quality status signal for vehicle health monitoring. To provide reliable guarantees for the healthy operation of a vehicle, real-time monitoring of status signals in the vehicle system is a promising solution. Due to the large variety of vehicle signals and requirement for long-term supervising, real-time monitoring generates huge amounts of data, which presents extremely high requirements for signal sampling and storage. The conventional monitoring scheme for vehicle is depicted in Figure 1a. The original signals are sampled and stored at a high sampling rate, which generates massive signals and imposes a heavy burden on storage and transmission [14]. In order to transmit such sampling signals to the data processing center, it requires expensive hardware resources and causes large time delay. CS theory, which performs signals sampling and compression simultaneously, can be well employed in quality analysis for vehicle system. Real-time health monitoring for vehicle using CS is demonstrated in Figure 1b. By contrast, only small numbers of measurement signals need to be transmitted after the original signals are sensed and compressed. In general, the data center has powerful computing resources to reconstruct the original signal. The transmission of these measurement signals demands limited resources and is extremely quick, which can be beneficial for the subsequent analysis and diagnosis. Reconstruction refers to the procedure of recovering the original signal from the low-dimensional measurement signal, and it is the critical part of CS. Reconstruction algorithms can be mainly divided into two directions, i.e., convex optimization algorithms and iterative greedy methods [8]. Convex optimization algorithms, such as basis pursuit (BP), require high computational complexity, which means they are not applicable in practice.