Ground penetrating radar (GPR), as a non-destructive and rapid detection instrument, has been widely used for underground pipeline detection. However, as the interpretation of 3-dimensional GPR images is still manually performed, the process is inefficient. Aiming at solving the challenges of automatic recognition for underground pipelines, we propose a recognition method based on a deep learning algorithm, which uses 3-dimensional GPR images and the improved 3D depth-wise separable convolution block. In order to expand the number of samples in the dataset, we propose a data augmentation method based on three-dimensional matrix rotation and use a wavelet-based denoising method to filter out the direct wave interference. To prove the effectiveness and efficiency of our method, we compared the classification performance of the improved 3D depth-wise separable convolutional block with the traditional 3D convolutional block and the ordinary 3D depth-wise separable convolutional block under the same conditions. According to the experiment’s results, the number of parameters of the method we proposed is 66.9% less than that of the traditional 3D convolution method, while the classification performance is similar. Furthermore, compared with ordinary 3D depth-wise separable convolution, our method can significantly improve the classification and recognition ability of the neural network, while the number of calculations and the number of parameters remain almost the same. This study demonstrates the effectiveness of 3D-CNN in the field of GPR image interpretation. An improved 3D depth-wise separable convolutional block is also proposed. It greatly reduces the amount of calculation and parameters while ensuring classification performance. It is better than the existing algorithms in performance. At the same time, to obtain the position and direction of the pipeline, in this study, a conic fitting method using the Canny operator is proposed to locate the vertices of B-Scan images and record their horizontal and vertical coordinates. This method can estimate the direction of the pipeline and it lays the foundation for future work such as measuring the pipeline depth.

To alleviate the shortage of spectral resources as well as to reduce the weight, volume, and power consumption of wireless systems, joint communication-radar (JCR) systems have become a focus of interest in both civil and military fields. JCR systems based on time-modulated arrays (TMAs) constitute an attractive solution as a benefit of their high degree of beam steering freedom, low cost, and high accuracy. However, their sideband radiation results in energy loss, which is an inherent drawback. Hence the energy-efficiency optimization of TMA-based JCR systems is of salient importance, but most of the existing TMA energy-efficiency optimization methods do not apply to JCR systems. To circumvent their problems, a single-sideband structure is designed for flexibly reconfigurable energy-efficient TMA beam steering. First, some preliminaries on single-sideband TMAs are introduced. Then, a closed-form expression is derived for characterizing the energy efficiency. Finally, the theoretical results are validated by simulations.

Ground penetrating radar (GPR), as a fast, efficient, and non-destructive detection device, holds great potential for the detection of shallow subsurface environments, such as urban road subsurface monitoring. However, the interpretation of GPR echo images often relies on manual recognition by experienced engineers. In order to address the automatic interpretation of cavity targets in GPR echo images, a recognition-algorithm based on Gaussian mixed model-hidden Markov model (GMM-HMM) is proposed, which can recognize three dimensional (3D) underground voids automatically. First, energy detection on the echo images is performed, whereby the data is preprocessed and pre-filtered. Then, edge histogram descriptor (EHD), histogram of oriented gradient (HOG), and Log-Gabor filters are used to extract features from the images. The traditional method can only be applied to 2D images and pre-processing is required for C-scan images. Finally, the aggregated features are fed into the GMM-HMM for classification and compared with two other methods, long short-term memory (LSTM) and gate recurrent unit (GRU). By testing on a simulated dataset, an accuracy rate of 90% is obtained, demonstrating the effectiveness and efficiency of our proposed method.