## 1 Introduction  
The ultrasonic Lamb wave technique has been successfully applied in the nondestructive testing (NDT) of plates. It has the advantage of a long propagation distance, which is particularly suitable for testing large areas through the thickness of the waveguide [1; 2; 3; 4]. However, several challenges still exist in Lamb wave nondestructive testing. Lamb waves are multimodal waves within a frequency region with a dispersive nature because their velocities are frequency dependent. In the actual testing, Lamb wave signals have different modes for any given plate thickness and excitation frequency, and the dispersion of each mode is different; that is, different frequency components of a specific propagation mode propagate at different speeds, which leads to the signal stretching in time and reducing the spatial resolution [5]. It can be seen from the dispersion characteristics that the phase velocities of some modes are very close under certain excitation conditions. Lamb wave signals are often overlapped in both time and frequency domains, and signal interpretations are often difficult.  
In recent years, many signal processing methods have been applied to the dispersion compensation and mode separation of Lamb waves [6; 7; 8; 9; 10]. Hilbert-Huang transform (HHT) [9] obtained high time-frequency resolution through empirical mode decomposition [10] and Hilbert spectrum. [PERSON] et al. [11] used the spectral phase and signal delay function to model the guided wave dispersion and used the single-mode compensation function to compensate the mixed guided wave signals, making the single-mode dispersion signal back to the excitation signal. They effectively extracted each of the multimodal guided waves. However, in the actual measurement, the specific excitation signals are unknown, resulting in a large error in the separation result. To separate dispersive multimodal Lamb waves overlapped in time and frequency domains, the separation method based on dispersion compensation and fractional differential [12; 13; 14] is presented. [PERSON] et al. [15] and [PERSON] et al. [16] proposed the wavelet ridge algorithm to extract the time-frequency ridges of modes, reconstructing their waveforms with their respective time-frequency information. The ridges extracted for adjacent modes with similar dispersion characteristics are often overlapped, which increases the difficulty of identification. [PERSON] et al. [17] proposed the multimodal guided wave signal separation in long bones based on the time-frequency blind signal separation. The experiment only derives the relevant group velocity problem based on the separated signal, and does not give an intuitive separation result to the actual measurement signal. Moreover, the time-frequency distribution may be interfered with by factors such as cross terms, resulting in a decrease in the accuracy of the separation result.  
In order to separate multimodal overlapped Lamb waves effectively, this paper proposes a mode separation method by means of the combination of dispersion compensation with independent component analysis (ICA) [18] of fourth-order cumulant. ICA is a type of blind signal separation method, and it can avoid the influence of cross terms in the time-frequency distribution method. In the proposed method, taking two-mode overlapped signals as an example, the single-mode dispersion compensation is performed according to the measured distance difference between the two sets of signals. The two sets of signals are returned to the same distance. The fourth-order cumulant ICA method is further used to process the Lamb wave signals of different superposition situations at the same distance. The corresponding mode signal contained in the two sets of signals at the same position is separated. The different modes are compensated and separated successively, thereby achieving the multimodal signal separation.