## 1 Introduction  
Composite structures have been increasingly used in various aircraft structures due to their superior stiffness and weight characteristics. However, composite structures are susceptible to low velocity impacts, which can cause internal damage that will lead to a significant reduction of the local structural strength [1]. Therefore, impact monitoring has become an important task of structural health monitoring (SHM) to ensure the safety of these applications. Recently, sensor array signal processing-based methods have emerged as a set of new promising SHM methods. [PERSON] et al. constructed two Z-shaped clusters at different positions for acoustic source localization in anisotropic plates [2]. [PERSON] et al. proposed an acoustic emission source localization approach based on beamforming with two uniform linear arrays [3]. He developed a method for localizing two acoustic emission sources simultaneously based on beamforming and singular value decomposition. [PERSON] and [PERSON] proposed a statistical super-resolution technique called the multiple signal classification (MUSIC) algorithm published in Nature Communications,and demonstrated that the MUSIC algorithm had a comparable or better performance in comparison with other localization techniques [4]. Because it has such advantages, the MUSIC algorithm was developed for the impact monitoring of plate-like structures. By processing the whole sensor array outputs using the MUSIC algorithm, the transfer function and material properties of the structure are not needed [5]. Since the MUSIC algorithm belongs to an eigen-structure and sub-space approach, it can effectively extract the key features of signals received and successfully detect the source signal under a low signal-to-noise ratio [6]. Some developments have been published by applying this method to process the sensor array signals in the structural health monitoring area. [PERSON] and [PERSON] [7] presented a far-field MUSIC algorithm-based method to estimate the direction of arrival (DOA) of single elastic waves on an aluminum plate using a uniform circular array. [PERSON] et al. [8] employed the far-field MUSIC algorithm to estimate the DOA of one impact on an aluminum plate. [PERSON] et al. presented a single frequency component-based re-estimated MUSIC algorithm to reduce the localization error caused by the anisotropy of the complex composite structure [9]. To deal with the near-field monitoring problem, the authors in previous work proposed the near-field 2D-MUSIC algorithm-based impact localization method for a composite plate using Gabor wavelet transform [10,11]. However, in previous MUSIC-based impact location methods, the narrowband signals at a particular central frequency had to be extracted from the wideband Lamb waves induced by each impact, and the specific center frequency had to be obtained after carefully analyzing the impact signal, which is time consuming. EMD is a new time-frequency analysis technique which can decompose the complicated signal into a set of complete and almost orthogonal components named intrinsic mode functions (IMFs) [12]. The IMFs represent the natural oscillatory mode embedded in the signal and work as the basis functions, which are determined by the signal itself [13]. Thus, it is a self-adaptive signal processing method that can be applied to a nonlinear and non-stationary process to decompose it into stationary signals. Generally, EMD cannot accurately extract fault features because of the mode mixing phenomena. [PERSON] and [PERSON] proposed a new ensemble EMD (EEMD) method to reduce mode mixing [14]. However, the EMD and its improved version EEMD are computation intensive methods, which are not suitable for on-line detection. A noise-assisted approach in conjunction with a multivariate empirical mode decomposition (MEMD) algorithm has been proposed for the computation of EMD, in order to produce localized frequency estimates at the accuracy level of instantaneous frequency [15, 16, 17].