Cross water–air interface acoustic and electromagnetic integrated communication (AEIC) technology refers to an underwater speaker that excites the water surface micro-amplitude wave (WSAW) on the water’s surface, and millimeter wave radar detects the vibrations of the WSAW to realize the transmission of information. The research on cross-media communication meets many challenges due to the large amplitude of the water surface disturbance and the small amplitude of the WSAW. In this paper, a novel sea wave interference suppression method based on improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet transform (WT) is presented. This method divides the phase change into different intrinsic mode functions (IMFs) and obtains a reconstructed scale of the WSAW signal through wavelet decomposition and correlation procession to separate the WSAW signal and the sea wave interference. It is proved to be better than the reference filtering method by experiment. By using this novel method, the bit error rate (BER) of the communication system can be reduced effectively.

Distributed array radar provides new prospects for three-dimensional (3D) inverse synthetic aperture radar (ISAR) imaging. The accuracy of image registration, as an essential part of 3D ISAR imaging, affects the performance of 3D reconstruction. In this paper, the imaging process of distributed array ISAR is proposed according to the imaging model. The ISAR images of distributed array radar at different APCs have different distribution of scatters. When the local distribution of scatters for the same target are quite different, the performance of the existing ISAR image registration methods may not be optimal. Therefore, an image registration method is proposed by integrating the feature-based method and the area-based method. The proposed method consists of two stages: coarse registration and fine registration. In the first stage, a dominant scatters model is established based on scale-invariant feature transform (SIFT). In the second stage, sub-pixel precision registration is achieved using the local correlation matching method. The effectiveness of the proposed method is verified by comparison with other image registration methods. The 3D reconstruction of the registered experimental data is carried out to assess the practicability of the proposed method.