Qichang Guo

With the increasing demand for marine biosensing and water–air collaborative rescue in national production and life, establishing a robust cross-medium communication link has become one of the hotspots. Among them, microwave acoustic cross-medium uplink communication technology has been widely studied for its advantages of being able to be used all day and in all weather, there being no need for relay, and having high concealment. The principle is to extract the frequency of the acoustic water surface waves from the phase history of the radar echoes. However, wave interference can cause discontinuity of the phase history, resulting in difficulty in extracting the acoustic water surface waves and an increase in bit error rate (BER). This article analyses the reasons for the discontinuity of phase history and innovatively proposes a method for extracting acoustic water surface waves based on phase compensation. The discontinuity points of the phase history are compensated based on whether the range bin changes. Then, low-frequency water surface fluctuations and discontinuity points are filtered out through second-order differential joint outlier removal, which can effectively reduce the influence of phase history discontinuity on time–frequency analysis and communication decoding. The effectiveness of the proposed method was verified through simulations and experiments. The experimental results indicate that the BER of the proposed method is 25% of that of the Wavelet–Kalman Filtering method. The proposed method provides a new approach for microwave acoustic cross-medium uplink communication.

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.

Cross-media communication underpins many vital applications, especially in underwater resource exploration and the biological population monitoring domains. Water surface micro-amplitude wave (WSAW) frequency detection is the key to cross-media communication, where the WSAW frequency can invert the underwater sound source frequency. However, extracting the WSAW frequency information encounters many challenges in a real environment, such as low precision and symbol synchronization, leading to inaccurately estimating the WSAW frequency. Thus, this paper proposed a WSAW frequency estimation method based on an improved RELAX algorithm, incorporating two improvements. First, adding a nonlinear filter to the RELAX kernel function compensates for the filtered gain and enhances the WSAW frequency precision. Second, the improved RELAX kernel function is combined with the generalized inner product method to obtain the time distribution of the non-stationary signals, which is convenient for decoding. Several simulations and experiments applying our method on a Ka-band frequency modulated continuous wave (FMCW) radar demonstrate that our algorithm attains a better performance than traditional methods, e.g., periodogram and the RELAX algorithm. Using the improved algorithm affords to extract the frequency information of the WSAW signal accurately with a short sampling duration, further improving the performance indicators of the communication system, such as communication rate.

Building reconstruction using high-resolution tomographic synthetic aperture radar (TomoSAR) point clouds has been very attractive in numerous applications, such as urban planning and dynamic city modeling. However, for side-looking TomoSAR, it is a challenge to reconstruct the obscured backs of buildings using traditional single-bounce scattering-based methods. It comes to our attention that the higher-order scattering points in airborne array TomoSAR point clouds may provide rich information on the backs of buildings. In this paper, the fourfold bounce (FB) scattering model of combined buildings in airborne array TomoSAR is derived, which not only explains the cause of FB scattering but also gives the distribution pattern of FB scattering points. Furthermore, a novel FB scattering-based method for the reconstruction of building backs is proposed. First, a two-step geometric constraint is used to detect the candidate FB scattering points. Subsequently, the FB scattering points are further detected by seed point selection and density estimation in the radar coordinate system. Finally, the backs of buildings can be reconstructed using the footprint inverted from the FB scattering points and the height information of the illuminated facades. To verify the FB scattering model and the effectiveness of the proposed method, the results from the simulated point clouds and the real airborne array TomoSAR point clouds are presented. Compared with the traditional roof point-based methods, the outstanding advantage of the proposed method is that it allows for the high-precision reconstruction of building backs, even in the case of poor roof points. Moreover, this paper may provide a novel perspective for the three-dimensional (3D) reconstruction of dense urban areas.

Tomographic Synthetic Aperture Radar (TomoSAR) is a breakthrough of the traditional SAR, which has the three-dimentional (3D) observation ability of layover scenes such as buildings and high mountains. As an advanced system, the airborne array TomoSAR can effectively avoid temporal de-correlation caused by long revisit time, which has great application in high-precision mountain surveying and mapping. The 3D reconstruction using TomoSAR has mainly focused on low targets, while there are few literatures on 3D mountain reconstruction. Due to the layover phenomenon, surveying in high mountain areas remains a difficult task. Consequently, it is meaningful to carry out the research on 3D mountain reconstruction using the airborne array TomoSAR. However, the original TomoSAR mountain point cloud faces the problem of elevation ambiguity. Furthermore, for mountains with complex terrain, the points located in different elevation periods may intersect. This phenomenon increases the difficulty of solving the problem. In this paper, a novel elevation ambiguity resolution method is proposed. First, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM) are combined for point cloud segmentation. The former ensures coarse segmentation based on density, and the latter allows fine segmentation of the abnormal categories caused by intersection. Subsequently, the segmentation results are reorganized in the elevation direction to reconstruct all possible point clouds. Finally, the real point cloud can be extracted automatically under the constraints of the boundary and elevation continuity. The performance of the proposed method is demonstrated by simulations and experiments. Based on the airborne array TomoSAR experiment in Leshan City, Sichuan Province, China in 2019, the 3D model of the surveyed mountain is presented. Moreover, three kinds of external data are applied to fully verify the validity of this method.