This paper presents the development of a lightweight and low-power Ground Penetrating Radar (GPR) to detect buried landmines in harsh terrain, using an Unmanned Aerial Vehicle (UAV). Despite the fact that GPR airborne systems have been already used for a while, there has yet been no focus on the UAV autonomy, which depends on the payload itself. Therefore, the contribution of this work is the introduction of a lightweight and low-power consumption GPR system, which is based on the Stepped Frequency Continuous Wave (SFCW) radar principle. The Radio Frequency (RF) transceiver represents an improved implementation of the super-heterodyne architecture, which currently offers higher sensitivity. This is achieved by combining analog and digital processing techniques. The experimental results showed that the developed system can detect both metallic and plastic buried targets. Target detection with a scanning height up to about 0.5 m shows good applicability in an unstructured, harsh environment, which is typical of mined terrain. The proposed system still needs some improvements for a fully operational system regarding different aspects of scanning speeds and soil properties such as moisture content.

This paper proposes a new-wavelet-based synthetic aperture radar (SAR) image despeckling algorithm using the sequential Monte Carlo method. A model-based Bayesian approach is proposed. This paper presents two methods for SAR image despeckling. The first method, called WGGPF, models a prior with Generalized Gaussian (GG) probability density function (pdf) and the second method, called WGMPF, models prior with a Generalized Gaussian Markov random field (GGMRF). The likelihood pdf is modeled using a Gaussian pdf. The GGMRF model is used because it enables texture parameter estimation. The prior is modeled using GG pdf, when texture parameters are not needed. A particle filter is used for drawing particles from the prior for different shape parameters of GG pdf. When the GGMRF prior is used, the particles are drawn from prior in order to estimate noise-free wavelet coefficients and for those coefficients the texture parameter is changed in order to obtain the best textural parameters. The texture parameters are changed for a predefined set of shape parameters of GGMRF. The particles with the highest weights represents the final noise-free estimate with corresponding textural parameters. The despeckling algorithms are compared with the state-of-the-art methods using synthetic and real SAR data. The experimental results show that the proposed despeckling algorithms efficiently remove noise and proposed methods are comparable with the state-of-the-art methods regarding objective measurements. The proposed WGMPF preserves textures of the real, high-resolution SAR images well.

The despeckling of synthetic aperture radar images using two different convolutional neural network architectures is presented in this paper. The first method presents a novel Siamese convolutional neural network with a dilated convolutional network in each branch. Recently, attention mechanisms have been introduced to convolutional networks to better model and recognize features. Therefore, we propose a novel design for a convolutional neural network using an attention mechanism for an encoder–decoder-type network. The framework consists of a multiscale spatial attention network to improve the modeling of semantic information at different spatial levels and an additional attention mechanism to optimize feature propagation. Both proposed methods are different in design but they provide comparable despeckling results in subjective and objective measurements in terms of correlated speckle noise. The experimental results are evaluated on both synthetically generated speckled images and real SAR images. The methods proposed in this paper are able to despeckle SAR images and preserve SAR features.

This paper presents the design of a high-voltage pulse-based radar and a supervised data processing method for soil moisture estimation. The goal of this research was to design a pulse-based radar to detect changes in soil moisture using a cross-hole approach. The pulse-based radar with three transmitting antennas was placed into a 12 m deep hole, and a receiver with three receive antennas was placed into a different hole separated by 100 m from the transmitter. The pulse generator was based on a Marx generator with an LC filter, and for the receiver, the high-frequency data acquisition card was used, which can acquire signals using 3 Gigabytes per second. Used borehole antennas were designed to operate in the wide frequency band to ensure signal propagation through the soil. A deep regression convolutional network is proposed in this paper to estimate volumetric soil moisture using time-sampled signals. A regression convolutional network is extended to three dimensions to model changes in wave propagation between the transmitted and received signals. The training dataset was acquired during the period of 73 days of acquisition between two boreholes separated by 100 m. The soil moisture measurements were acquired at three points 25 m apart to provide ground truth data. Additionally, water was poured into several specially prepared boreholes between transmitter and receiver antennas to acquire additional dataset for training, validation, and testing of convolutional neural networks. Experimental results showed that the proposed system is able to detect changes in the volumetric soil moisture using Tx and Rx antennas.

The paper presents a modified adaptation algorithm for the super-twisted sliding mode controller structure, based on the barrier function method. The aim of the paper is to reduce the chattering phenomena of the controller, which limited the use of the controller in different applications. The chattering phenomena are mostly caused by the overestimated controller gain due to the assumed disturbance bound, which is mostly inaccurate. The chattering origins are also the unknown parasitic dynamic of the system and discrete implementation of the controller. The proposed method with the Barrier function is used to alleviate the chattering phenomena with the adaptation of the controller parameters. The novelty of the method is using an adaptation procedure only in prescribed regions of the sliding variable, otherwise, the adaptation is not used. The advantage of the method is the proper rejection of the chattering phenomena in the vicinity of the manifold of the sliding variable, regardless of the order of the system. With proper selection of the adaptation boundary, the effect of discrete implementation, especially for a longer sampling time of the algorithm, can be suppressed efficiently, as well as the effect of the overestimated controller parameters. The proposed method is verified and compared with a standard version of the algorithm in simulation and real-time environments.