Budiman Putra Asma'Ur Rohman

Ground-penetrating radar (GPR) is widely used for subsurface inspection, however, missing traces can significantly degrade image quality and interpretation reliability, particularly in consecutive missing-trace scenarios. This paper proposes a lightweight modified U-Net architecture for reconstructing sparse and consecutive missing traces in GPR images. The proposed model employs a shallower encoder–decoder structure, single convolution blocks, and larger convolution kernels to reduce computational complexity while maintaining reconstruction capability. Experimental results using synthetic and real reinforced-concrete GPR datasets demonstrate that the proposed method achieves reconstruction performance comparable to deep learning-based baseline models while significantly reducing model complexity, storage size, training time, and inference time. In addition, the proposed framework shows strong robustness under both random and consecutive missing-trace conditions, making it suitable for practical and resource-constrained GPR imaging applications.

Continuous human vital sign monitoring is essential for medical purpose. To make this system possible to be easily and widely applied, low manufacturing costs are preferred. Besides, to maintain the patient's comfort, noncontact monitoring is recommended. Therefore, this letter proposes a noncontact respiration monitoring system employing an ultra-low-cost continuous wave radar. An integration with a signal processing technique to extract human vital signs with high accuracy has been proposed by employing several processing steps that work sequentially, including Hilbert transform and variational mode decomposition. The experimental evaluations using various target ranges, respiration rates, and strengths confirm the reliability and accuracy of the proposed method. These indicate that the proposed system is feasible enough to be applied in real applications with appropriate integration.

Autonomous radar interference is a challenging problem in autonomous vehicle systems. Interference signals can decrease the signal-to-interference-noise ratio (SINR), and this condition decreases the performance detection of autonomous radar. This paper exploits a neural network and signal decomposition to detect and mitigate radar interference in autonomous vehicle applications. A neural network (NN) with four inputs, one hidden layer, and one output is trained with various signal-to-noise (SNR), interference radar bandwidth, and sweep time of autonomous radar. Four inputs of NN represent SNR, mean, total harmonic distortion (THD), and root means square (RMS) of the received radar signal. Variational mode decomposition (VMD) and zeroing based on a constant false alarm rate (CFAR-Z) are used to mitigate radar interference. VMD algorithm is applied to decompose interference signals into multi-frequency sub-band. As a result, the proposed neural network can detect radar interference, and NN-VMD-CFAR-Z can increase SINR up to 2dB higher than the NN-CFAR-Z algorithm.

Ground penetrating radar (GPR) is a non-destructive tool for exploring an object buried underground. Currently, GPR is also considered for reinforced concrete inspection. However, the image produced by GPR can not be easily interpreted. Besides, the large observation of building concrete inspection also motivates the researchers to fastening and easing radar image interpretation. Thus, this research proposes a new method to translate GPR scattering data image to its internal structure visualization. The proposed employs a convolutional autoencoder model using amplitude and phase radar data as input of the algorithm. As evaluation, in this stage, we perform numerical analysis by using finite-difference time-domain-based synthetic data that considers three cases: concrete with rebar, concrete with crack, and concrete with rebar and crack. All of those cases are simulated with randomized dimensions and positions that is possible in the real applications. Compared with the baseline method, our method shows superiority, especially in the semantic segmentation perspective. The parameter size of the proposed model is also much smaller, around one-third of the previous method. Therefore, the method is feasible enough to be implemented in real applications addressing an automatic internal structure reinforced concrete visulaization

Radar is one of the sensors that have significant attention to be implemented in an autonomous vehicle since its robustness under many possible environmental conditions such as fog, rain, and poor light. However, the implementation risks interference because of transmitting and/or receiving radar signals from/to other vehicles. This interference will increase the floor noise that can mask the target signal. This paper proposes multiplicative-adaptive filtering and Hilbert transform to mitigate the interference effect and maintain the target signal detectability. The method exploited the trade-off between the step-size and sidelobe effect on the least mean square-based adaptive filtering to improve the target detection accuracy, especially in the long-range case. The numerical analysis on the millimeter-wave frequency modulated continuous wave radar with multiple interferers concluded that the proposed method could maintain and enhance the target signal even if the target range is relatively far from the victim radar.

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.

This paper describes phase parameter extraction from ultra-wideband (UWB) radar response and investigate the effectiveness of it for non-destructive inspection of concrete. The phase parameter introduced in this study contains information on the phase-shift of scattering by a corroded rebar and is effective for estimating the corrosion stage of the rebar. Simulation results indicate that the corrosion rate of the rebar can be estimated by using the extracted phase parameter even when the commonly used correlation coefficient is not effective.

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.