In order to solve the mode-mixing problem of empirical mode decomposition (EMD) and overcome the difficulty of separating the noise components and signal components, a novel denoising method based on secondary complementary ensemble empirical mode decomposition (CEEMD) and time domain feature analysis is presented in this paper. In the proposed method, CEEMD is employed to solve the mode-mixing problem, then the boundary of noise dominant intrinsic mode function (IMF) components and the signal dominant IMF components is determined based on the time domain feature analysis of IMFs returned by CEEMD, whereby the noise components and the signal components are distinguished. Secondary CEEMD decomposition is performed on the noise dominant IMF component and signal dominant IMF component at the boundary to further filter the residual noise while maintain as much useful signal as possible. The experimental results of denoising the actual airborne platform data with impulse noise interference show that the proposed method can better suppress the noise interference and significantly improve the signal-to-noise ratio by effectively separating the noise and signal components.