An experimental wideband chaotic ground penetrating radar is proposed to locate underground pipes. A chaotic signal with a bandwidth of 1.56 GHz is utilized as the probe signal. The localization of the pipes is achieved by correlating the chaotic echo signal with its delayed duplicate and back-projection algorithm. Experimental results demonstrate that plastic pipe, metallic pipe, and multiple pipes can be located with a range resolution of 10 cm. Limited by the height of the sand, the detectable range is estimated to be 0.7 m for both the plastic pipes and the metallic pipes when the transmitting power is −12 dBm. The proposed system has the potential to detect buried pipes, and it is suitable for geological and civil engineering applications.

Chaos through-wall imaging radar has attracted wide attention due to its inherent low probability of detection/interception, strong anti-jamming, and high resolution. However, the target response is usually overwhelmed by strong clutter. This paper proposes an imaging-then-decomposition method based on two-stage robust principal component analysis (RPCA) to remove the clutter and recover the target image. The proposed method firstly focuses the energy of the preprocessing data by the back-projection imaging algorithm; then, it performs matrix decomposition on the full and the sparse component of the focused data, in succession, by the RPCA algorithm. Simulation and experimental results show that the proposed method can suppress the clutter dramatically and indicate human targets distinctly. Compared with the traditional methods, it has effectiveness and superiority in improving the signal-to-clutter ratio.

A high-resolution leaky coaxial cable (LCX) sensor for perimeter intrusion detection is proposed and experimentally demonstrated. In our proposed sensor system, a wideband Boolean-chaos signal is used as the probe signal, and a pair of leaky coaxial cables (LCXs) is applied for transmitting the probe signal and receiving the echo signal, respectively. By correlating the chaotic echo signal with its delayed duplicate and comparing the correlation traces before and after intrusion, the intruder can be accurately located. Experimental results demonstrate the proposed sensor can simultaneously detect multiple intruders. The range resolution reaches 30 cm, whilst the dynamic range can achieve 50 dB. In addition, this sensor possesses the excellent anti-interference performance to the noise and uncorrelated chaotic signal, which makes it show robust performance in the detection environment with noise or multiple chaotic LCX sensors cooperation.

Ground-penetrating radar (GPR) is an effective tool for underground object detection, but its data interpretation remains a great challenge. In this letter, we propose a novel underground object classification algorithm using deep 3-D convolutional networks (C3D) and multiple mirror encoding (MME) for 3-D GPR data. Although deep learning technique has been applied to interpret the GPR data, most of the existing methods are based on GPR B-scans and have a relatively low accuracy since the reflections from various subsurface targets present similar hyperbolic patterns in B-scans. To improve the classification accuracy, we use 3-D GPR data as training data for C3D to capture the spatio-temporal features between parallel B-scans. Since 3-D GPR data including single object has different sizes in consideration of actual sizes of objects, they are rearranged by the MME method to enhance spatio-temporal features, as well as to satisfy the requirement of the network input. Experimental results demonstrate that the proposed method outperforms the state-of-the-art B-scan-based methods.

A Golay-based ultra wideband ground penetrating for underground pipes location is proposed and experimentally demonstrated. Golay complementary codes with the code length of 1024 and frequency of 1 GHz are used as the probe signals. The two-dimensional image of the buried pipes is achieved by a correlation method and a back-projection algorithm. The experimental results show that both the plastic pipe and metallic pipe can be located with a range resolution of 10 cm. Furthermore, as the Golay complementary sequences are a pair of complementary sequences, the sum of their correlation function yields twice the value of the peak at the target position and zero elsewhere. Thus, compared with the stepped frequency signal radar or chaotic signal radar, the Golay-based radar can significantly improve the signal–noise ratio and has the capability of deep detection.

Clutter removal is of vital importance to underground detection via ground penetrating radar (GPR) since the target response is usually overwhelmed by strong clutter. The low-rank and sparse decomposition (LRSD) method, which decomposes the GPR data into a low-rank background matrix and a sparse target matrix, has been applied in GPR. However, the existing LRSD-based methods, such as robust principal component analysis (RPCA) and robust non-negative matrix factorization, are sensitive to the regularization parameter or have high computation complexity. In this letter, a novel clutter removal based on factor group-sparse regularization is proposed. It uses a nonconvex matrix factorization as a surrogate for the matrix rank rather than the nuclear norm as in the RPCA. It has good efficiency and robustness to the parameters. Simulation and experimental results demonstrate that the proposed method has higher performance in terms of the improvement factor values and lower computational complexity than the state-of-the-art methods.