The detection of a human being behind obstacles using radar technology has many promising applications, such as postdisaster search and rescue missions. In the case of large observation areas, the use of an unmanned aerial vehicle as the moving platform for radar is an appealing approach. However, this task is challenging because human vital signs provide a relatively weak signal compared with background noise and clutter. The drone's instability also negatively affects the obtained radar signal. Thus, this article presents a new signal processing method to extract and enhance respiration signals from drone-mounted radar. The method works by extracting the analytic signal representation and applying the subspace component segregation. Laboratory experiments in a controlled environment show that the proposed method can suppress a considerable level of vibration generated by unbalanced motor motion and enhance the respiration signal from radar data. A field experimental study using an octocopter in hovering mode confirms the method performs well in real-world conditions.

Ground-penetrating radar is an effective tool for the exploration of subsurface and obscured objects by exploiting the electromagnetic wave characteristics. However, some problems in the measurement process produce missing traces. The resulting incomplete data create difficulties in visualization, interpretation, and analysis. This article proposes a lightweight deep learning method to reconstruct the missing traces. The model is a simplification of a well-known convolutional neural network model, U-Net, which performs well in this context. We use a multi-and-wide kernel size, shallow convolutional layers, a reduced encoding-decoding step, and a minimized feature map size to minimize the model while maintaining high performance. In this study, we focus on the case of inspection of reinforced concrete, where the examined structure consists of a series of rebar and/or void cracks. The numerical and experimental field data results indicate that the method can reconstruct the missing traces with high accuracy. The proposed method also shows comparable performance with U-Net, although the model size is more than 45 times smaller, and its computation speed is more than three times faster. Thus, the proposed method is promising for use in a wide range of applications and devices.

This paper presents the switching CA/OS CFAR using neural network for improving the radar target detection in non-homogeneous environment. This method uses one of between CA-CFAR and OS-CFAR as output threshold depends on the nearest value with the output of neural network. The neural network used in this research is the Multi-Layer Perceptron (MLP) consisted of two hidden layers. The input of neural network was as many as 3 consisted of CA and OS CFAR and Cell Under Test (CUT) value. The pattern of those inputs will be classified and recognized by the neural network by the training to calculate the preliminary threshold. That threshold will be compared to CA and OS CFAR to select the best final threshold. The method was examined with three simulated common radar cases including homogeneous background, multi target and clutter wall environment. The experiments show that the proposed method is capable to select properly based on the best performance of both CA and OS CFAR in homogeneous and non-homogeneous environments.

Ground penetrating radar (GPR) is widely applied for civil engineering, such as for building health assessments or pavement inspections. In these applications, information on the material permittivity is important for internal structure inspection and characterization. However, in the case of a large building area, a lightweight, fast and effective permittivity measurement technique is required. In addition, the estimation method for different GPR types and range configurations must be generalized for adaptability and wide applicability. This paper proposes a material permittivity estimation technique for air-coupled GPR that effectively works for mobile and large observation missions. The method employs the peak ratio of analytic representation signals between the antenna direct coupling and the material surface reflection. An interpolation algorithm is applied to the reference characteristic curves generated by the finite-difference time-domain technique to adapt the change in radar range configuration. The proposed method has been tested to estimate the permittivity in three different construction materials and measure two metal rebar depths inside reinforced concrete. The experimental results show that the method works with acceptable accuracy. Therefore, the method is sufficiently promising to be utilized in real applications, especially those that require adaptability and mobility, such as ground and aerial vehicle-based radar systems.

The rapid progression in wireless networking has suggested dynamic spectrum allocation to increase spectrum utilization. Cognitive radio (CR) is a key technology to enable dynamic spectrum access. Spectrum sensing provide spectrum awareness to CR system. Although energy detection (ED) is commonly used for spectrum sensing method, it gives poor performance under low noise environment and also suffers from noise uncertainty. Eigenvalue based detection is used to overcome ED drawbacks in this paper. Simulation results are presented for evaluating detection performance with respect to the received SNR and threshold parameters such as desired probability of false alarm (Pf), number of samples (N) and smoothing factor (L). It is shown in all cases that higher SNR value and threshold parameters setting give better probability of detection. In addition, the effect of different pulse shaping filters on probability of detection are investigated.