Detection Of Buried Targets Research Articles

Detection of Buried Nonlinear Targets Using DORT

Ground-penetrating radars (GPR) based on a variety of techniques have been proposed to improve the performance of buried target (e.g., landmines, threat devices) detection. However, the small radar cross section (RCS) of small electronic devices poses difficulties for target detection, especially when they are buried in lossy and inhomogeneous media. This paper presents a novel buried nonlinear target detection method based on the decomposition of the time-reversal operator (DORT) that uses a multistatic system to overcome the limitations of conventional GPR. Using harmonic radar, which detects the harmonic responses scattered from electronic devices, and DORT processing, which enables focusing/imaging of the detected target, the detection performance is verified by conducting simulation and measurements. The overall results demonstrate that the proposed method achieves accurate detection of buried targets with small RCS.

Scattering enhancement of partially buried sphere at the water-sand interfaces due to reflected subsonic Rayleigh waves

In view of the development needs of maritime security, the detecting of buried targets over long distances at low frequencies needs to be solved urgently. Through well designed experiments at low frequency (ka = 5–10), this paper presents a study on the scattering echoes from polymethylmethacrylate (PMMA) spheres in partially buried cases (50%, 75% and 100% buried), and the propagation characteristics of subsonic Rayleigh surface waves on the surface of PMMA spheres at the water-sand interface can be clearly observed. It is shown that the fine sand medium with larger impedance brings about smaller circumferential phase and group velocities of subsonic Rayleigh waves on the surface of the PMMA sphere; the resonance peak caused by subsonic Rayleigh waves is shifted toward the low frequency with the deepening of the burial depth, and the offset can be predicted by the circumferential phase velocity of the subsonic Rayleigh wave in different media and burial depth of PMMA sphere, and the experimental and finite element simulation results validate the shift of the Rayleigh resonance peak. The research results of this paper can provide certain reference value for the detection of low frequency buried targets.

Application of a parametric array over a mid-frequency band (4–10 kHz) –measurements of bottom backscattering strength

Study on characteristics of bottom backscattering at midfrequency (usually referring to 1–10 kHz) is essential for underwater acoustic communication, subbottom structure detection, buried target detection, and antisubmarine sonar. Measurements of bottom backscattering strength in the grazing angle range of 70°–90° over a wide mid-frequency band of 4–10 kHz were taken using a parametric array transmitter system and an omnidirectional hydrophone. Details of the parametric array transmitter system are introduced. The experimental site locates in the South Yellow Sea of China, where the water depth is 37 m. Core data showed that the sediment was fine sand mixed with a small amount of shell fragments. The backscattering strength was extracted from monostatic backscattering data according to the sonar equation. A calibration experiment indicated that the distance from the parametric array to the seafloor should be larger than a conversion distance, where the difference-frequency source level and beamwidth of the parametric array tended to be stable, to efficiently generate low-frequency difference-frequency waves used to measure the bottom backscattering strength. The backscattering strength increases with the increase of the grazing angle, reaching a peak at the grazing angle of 90°. In the point of frequency, the frequency dependence of the backscattering strength is weak. The characteristics of bottom backscattering agree with a sandy site with similar sediment properties near the experimental site.

Improved Detection of Buried Elongated Targets by Dual-Polarization GPR

Ground-penetrating radar (GPR) has been widely applied to the detection and delineation of buried targets in the subsurface. Compared with conventional single-channel GPR, polarimetric GPR has been proven to possess an improved ability to detect and characterize an elongated object in the subsurface. This letter proves that the scattering signals from a cylinder in two orthogonal polarization channels have a phase difference of about 90° when its diameter-to-wavelength ratio is about 0.05–0.33. Consequently, a polarization-difference imaging method, which shifts the VV component by −90° and subtracts it from the HH component, is proposed for the improved detection and imaging of a subsurface elongated object. Its effectiveness is verified by numerical, laboratory, and field tests on buried rebars and pipes. The signal-to-clutter ratios of the reconstructed GPR images can be improved by up to 4.5 dB by considering the phase difference between the dual-polarization components.

GPR Image Recovery Effect on Faster R-CNN-Based Buried Target Detection

Measurements acquired through ground-penetrating radar (GPR) may contain missing information that needs to be recovered before the implementation of any post-processing method, such as target detection, since buried target detection methods fail and cannot produce desired results if the input GPR image contains missing information. This study proves that the recovery of missing information in a GPR image has a direct influence on the performance of subsequent target detection methods. Thus, state-of-the-art matrix completion methods are applied to the GPR image with missing information in both pixel- and column-wise cases with different missing rates, such as 30% and 50%. After the GPR image is successfully recovered, the faster region-based convolutional neural network (Faster R-CNN) target detection method is applied. The performance correlation between matrix completion accuracy and the target detection method’s confidence score is analyzed using both quantitative and visual results. The obtained results demonstrate the importance of GPR image recovery prior to any post-processing implementation, such as target detection.

Buried target detection method for ground penetrating radar based on deep learning

Deep learning method has been extensively applied to ground penetrating radar two-dimensional profile (GPR B-SCAN) hyperbola detection recently. We propose a B-SCAN image feature extraction method based on the constraints of the GPR physical model, and further detect the weak boundary feature curve of the target in the local space. A deep convolutional neural network (DCNN) is first designed to extract high-level semantic features from B-SCAN images to remove direct wave. Next, a multiscale feature fusion DCNN is used to extract the features of the B-SCAN image with the direct wave removed, and the classifier network is used to identify the hyperbola of the upper boundary feature of the target. Finally, according to the hyperbola, the local space corresponding to the target in the B-SCAN image is determined. On this basis, the amplitude and phase information of the scattered electric field are used to segment the lower boundary characteristic curve of the target through convolution operation. Experimental results on simulation and field data show that feature information of the buried target in the GPR B-SCAN image can be efficiently extracted when the proposed method is adopted.

Experimental Validation of a Microwave Imaging Method for Shallow Buried Target Detection by Under-Sampled Data and a Non-Cooperative Source.

In microwave imaging, it is often of interest to inspect electrically large spatial regions. In these cases, data must be collected over a great deal of measurement points which entails long measurement time and/or costly, and often unfeasible, measurement configurations. In order to counteract such drawbacks, we have recently introduced a microwave imaging algorithm that looks for the scattering targets in terms of equivalent surface currents supported over a given reference plane. While this method is suited to detect shallowly buried targets, it allows one to independently process all frequency data, and hence the source and the receivers do not need to be synchronized. Moreover, spatial data can be reduced to a large extent, without any aliasing artifacts, by properly combining single-frequency reconstructions. In this paper, we validate such an approach by experimental measurements. In particular, the experimental test site consists of a sand box in open air where metallic plate targets are shallowly buried a (few ) under the air/soil interface. The investigated region is illuminated by a fixed transmitting horn antenna, whereas the scattered field is collected over a planar measurement aperture at a fixed height from the air-sand interface. The transmitter and the receiver share only the working frequency information. Experimental results confirm the feasibility of the method.

Acoustic scattering of buried stainless steel spheres: Theoretical analysis and experimental verification

The resonance characteristics of elastic acoustic scattering play an important role for buried target detection and identification. Taking a stainless steel sphere as the target, the acoustic scattering of the target under free-field and buried conditions are calculated using the corresponding COMSOL models. The influences of the burial layer on the Rayleigh wave and Whispering-Gallery wave are analyzed using the partial-wave representation of the form function and the dispersion curve. Compared to the scattering in free-field, the Rayleigh resonance peaks are significantly suppressed under the considered burial layer, while less affecting the Whispering-Gallery resonances. The method of isolation and identification of resonance (MIIR) is used to process the experimental data and hence the elastic resonances in free-field and buried conditions are obtained. The elastic resonances obtained in free-field are mainly Rayleigh resonance peaks, while the Whispering-Gallery resonances are the elastic characteristics under the buried condition. Moreover, using the fitting analysis of the two types of resonances, the calculated expressions for the target radius estimation under the two conditions are obtained, and verified from experimental results.

Depth Sensitivity of Apparent Magnetic Susceptibility Measurements using Multi-coil and Multi-frequency Electromagnetic Induction

Apparent magnetic susceptibility (MSa) as recorded by electromagnetic induction (EMI) instruments could offer relevant information about non-soil subsurface features. It is less affected by natural soil properties than its prominent counterpart, i.e., apparent electrical conductivity (ECa). Hence, MSa is generally a promising approach to investigate artificial inclusions and structures in soil. However, while the origin depth of EMI based ECa is widely accepted, the depth sensitivity (DS) of MSa measurements remains poorly understood. The depth interpretation of MSa is particularly challenging due to negative values especially for objects that are randomly distributed over different depths. Here we assessed the performance of both multi-coil (MC) and multi-frequency (MF) EMI sensors for identifying and determining the DS of MSa measurements in shallow soils through detection of buried small targets of known conductivity. Two experiments were conducted in a sandy loam podzolic soil in western Newfoundland, Canada. Materials of different conductivities, including metal and plastic targets, were buried at depths between 20 and 80 cm. Three inter-coil separations (32, 71 and 118 cm) of the MC sensor and four factory-calibrated frequencies (18, 38, 49 and 80 kHz) of the MF sensor were tested in both horizontal and vertical coil orientations. The MC sensor clearly detected all four metal targets from three coil separations in both coil orientations while the MF sensor identified more anomalies than targets limiting its information value. Based on the measurements from MC and the theoretical DS function, a criterion was developed and validated to assess the potential depth origin of MSa. We found that negative or less than the background values occur, if the depth of the target is shallower than 0.36 times the coil distance of the employed EMI sensor. According to this criterion, the depth origins of metallic targets were correctly identified under the assumption of low induction numbers, even if values were negative.

Parametric Acoustic Array and Its Application in Underwater Acoustic Engineering.

As a sound transmitting device based on the nonlinear acoustic theory, parametric acoustic array (PAA) is able to generate high directivity and low frequency broadband signals with a small aperture transducer. Due to its predominant technical advantages, PAA has been widely used in a variety of application scenarios of underwater acoustic engineering, such as sub-bottom profile measurement, underwater acoustic communication, and detection of buried targets. In this review paper, we examine some of the important advances in the PAA since it was first proposed by Westervelt in 1963. These advances include theoretical modelling for the PAA, signal processing methods, design considerations and implementation issues, and applications of the PAA in underwater acoustic engineering. Moreover, we highlight some technical challenges which impede further development of the PAA, and correspondingly give a glimpse on its possible extension in the future. This article provides a comprehensive overview of some important works of the PAA and serves as a quick tutorial reference to readers who are interested to further explore and extend this technology, and bring this technology to other application areas.

Airborne Multi-Channel Ground Penetrating Radar for Improvised Explosive Devices and Landmine Detection

An improved Ground Penetrating Radar (GPR) system on board an Unmanned Aerial Vehicle (UAV) is presented in this contribution. The system has been designed for the detection and imaging of buried targets and, in particular, landmines and Improvised Explosive Devices (IEDs). Resting on the hardware and architecture of a previous aerial platform, in the proposed system the scanning area is increased and the detection capabilities are improved. These improvements are achieved by employing two receiving antennas and new processing techniques that increase the Signal-to-Clutter Ratio of the GPR images. Besides, parameters affecting the GPR image resolution, such as the flight speed and the amount of measurements that can be processed together using Synthetic Aperture Radar (SAR) techniques, are also studied. The developed system exhibits several advantages: safety and faster scanning speeds, together with the capability to detect both metallic and non-metallic targets, as shown in the examples presented in this contribution.

Autonomous Airborne 3D SAR Imaging System for Subsurface Sensing: UWB-GPR on Board a UAV for Landmine and IED Detection

This work presents an enhanced autonomous airborne Synthetic Aperture Radar (SAR) imaging system able to provide full 3D radar images from the subsurface. The proposed prototype and methodology allow the safe detection of both metallic and non-metallic buried targets even in difficult-to-access scenarios without interacting with the ground. Thus, they are particularly suitable for detecting dangerous targets, such as landmines and Improvised Explosive Devices (IEDs). The prototype is mainly composed by an Ultra-Wide-Band (UWB) radar module working from Ultra-High-Frequency (UHF) band and a high accuracy dual-band Real Time Kinematic (RTK) positioning system mounted on board an Unmanned Aerial Vehicle (UAV). The UAV autonomously flies over the region of interest, gathering radar measurements. These measurements are accurately geo-referred so as to enable their coherent combination to obtain a well-focused SAR image. Improvements in the processing chain are also presented in order to deal with some issues associated to UAV-based measurements (such as non-uniform acquisition grids) as well as to enhance the resolution and the signal to clutter ratio of the image. Both the prototype and the methodology were validated with measurements, showing their capability to provide high-resolution 3D SAR images.

Feature Extraction for Acoustic Scattering from a Buried Target

Elastic acoustic scattering is important for buried target detection and identification. For elastic spherical objects, studies have shown that a series of narrowband energetic arrivals follow the first specular one. However, in practice, the elastic echo is rather weak because of the acoustic absorption, propagation loss, and reverberation, which makes it difficult to extract elastic scattering features, especially for buried targets. To remove the interference and enhance the elastic scattering, the de-chirping method was adopted here to address the target scattering echo when a linear frequency modulation (LFM) signal is transmitted. The parameters of the incident signal were known. With the de-chirping operation, a target echo was transformed into a cluster of narrowband signals, and the elastic components could be extracted with a band-pass filter and then recovered by remodulation. The simulation results indicate the feasibility of the elastic scattering extraction and recovery. The experimental result demonstrates that the interference was removed and the elastic scattering was visibly enhanced after de-chirping, which facilitates the subsequent resonance feature extraction for target classification and recognition.

Three-dimensional Imaging Sonar Signal Processing System Based on Blade Server

The three-dimensional high-resolution imaging sonar uses multi-beam technology combined with synthetic aperture technology to achieve centimeter-level imaging of small underwater targets. It has a good imaging effect on underwater anchoring, sinking mine and buried mine target detection and underwater topography, and has a wide range of military and civil engineering applications. The system has a large amount of data transmission and computation, including multiple functional modules such as synthetic aperture, high resolution array processing, image processing, and target recognition. The software architecture is complex and real-time system development faces serious challenges. The blade servers have been widely used in the telecommunications, financial and other big data processing fields as for the high efficiency, stability and autonomy. This paper introduces a cluster 3D imaging signal processing system based on x240m5 blade server. The message queue communication (ZMQ) is used to realize the stable transmission of large data volume. At the same time, the mixed programming technology of C language and Matlab language combines the convenience of real-time and software integration, which greatly shortens the development cycle. The system runs stably and realizes the imaging recognition of the small spheres and their clump weights in the simulated target water.

A Large Comparison of Feature-Based Approaches for Buried Target Classification in Forward-Looking Ground-Penetrating Radar

Forward-looking ground-penetrating radar (FLGPR) has recently been investigated as a remote sensing modality for buried target detection (e.g., landmines). In this context, raw FLGPR data is beamformed into images and then computerized algorithms are applied to automatically detect subsurface buried targets. Most existing algorithms are supervised, meaning they are trained to discriminate between labeled target and non-target imagery, usually based on features extracted from the imagery. A large number of features have been proposed for this purpose, however thus far it is unclear which are the most effective. The first goal of this work is to provide a comprehensive comparison of detection performance using existing features on a large collection of FLGPR data. Fusion of the decisions resulting from processing each feature is also considered. The second goal of this work is to investigate two modern feature learning approaches from the object recognition literature: the bag-of-visual-words and the Fisher vector for FLGPR processing. The results indicate that the new feature learning approaches outperform existing methods. Results also show that fusion between existing features and new features yields little additional performance improvements.

Application of time-reversal-based processing techniques to enhance detection of GPR targets

In this paper we analyze the performance of time-reversal (TR) techniques in conjunction with various Ground Penetrating Radar (GPR) pre-processing methods aimed at improving detection of subsurface targets. TR techniques were first developed for ultrasound applications and, by exploiting the invariance of the wave equation under time reversal, can yield features such as superresolution and statistical stability. The TR method was examined here using both synthetic and actual GPR field data under four different pre-processing strategies on the raw data, namely: mean background removal, eigenvalue background removal, a sliding-window space-frequency technique, and a noise-robust spatial differentiator along the scan direction. Depending on the acquisition mode, it was possible to determine with good precision the position and depth of the studied targets as well as, in some cases, to differentiate the targets from nearby clutter such as localized geological anomalies. The proposed methodology has the potential to be useful when applied to GPR data for the detection of buried targets.

Modelling and Simulation Resolution of Ground-Penetrating Radar Antennas

The problem of resolution in antenna ground-penetrating radar (GPR) is very important for the investigation and detection of buried targets. We should solve this problem with software or a numeric method. The purposes of this paper are the modelling and simulation resolution of antenna radar GPR using three antennas, arrays (as in the software REFLEXW), the antenna dipole (as in GprMax2D), and a bow-tie antenna (as in the experimental results). The numeric code has been developed for study resolution antennas by scattered electric fields in mode B-scan. Three frequency antennas (500, 800, and 1,000 MHz) have been used in this work. The simulation results were compared with experimental results obtained by Rial and colleagues under the same conditions.

Effects of using inclined parametric echosounding on sub-bottom acoustic imaging and advances in buried object detection

This study reports an adaptation of a parametric echosounder system using 15 kHz as secondary frequency to investigate the angular response of sub-bottom backscatter strength of layered mud, providing a new method for enhanced acoustic detection of buried targets. Adaptions to achieve both vertical (0°) and non-vertical inclination (1– 15°, 30°, 45° and 60°) comprise mechanical tilting of the acoustic transducer and electronic beam steering. Data were acquired at 18 m water depth at a study site characterized by a flat, muddy seafloor where a 0.1 m diameter power cable lies 1–2 m below the seafloor. Surveying the cable with vertical incidence revealed that the buried cable can hardly be discriminated against the backscatter strength of the layered mud. However, the backscatter strength of layered mud decreases strongly at >3±0.5° incidence and the layered mud echo pattern vanishes beyond 5°. As a consequence, the backscatter pattern of the buried cable is very pronounced in acoustic images gathered at 15°, 30°, 45° and 60° incidence. The size of the cable echo pattern increases linearly with incidence. These effects are attributed to reflection loss from layered mud at larger incidence and to the scattering of the 0.1 m diameter buried cable. Data analyses support the visual impression of superior detection of the cable with an up to 2.6-fold increase of the signal-to-noise ratio at 40° incidence compared to the vertical incidence case.

Determination of Background Distribution for Ground-Penetrating Radar Data

Ground-penetrating radars (GPRs) show promising results for subsurface buried target detection. However, the online detection as the GPR scans a region is a difficult problem, and the best performance requires to know the characteristics of the clutter and noise which affect the used test statistics in detection. In statistical detection methods developed for GPR, mostly Gaussian clutter assumption is used mainly due to its simplicity. In this letter, a low-complexity goodness-of-fit test suitable for online GPR detection is applied to experimental GPR data sets to determine the best clutter distribution defining the data test statistic. The distributions of A-scan energies after background subtraction are determined from different experimental data taken over notarget regions. The obtained results show that the GPR clutter for the tested experimental data is mainly gamma distributed than Gaussian. The demonstrated procedure can be applied to any GPR data set for the determination of the background distribution, for target detection, and for selecting detection thresholds properly for GPR applications and more realistic GPR clutter generation simulations.

Blind Separability of Reverberation and Target Echo Based on Spatial Correlation

The detection of buried targets in shallow water is a tough task in the presence of sea-bottom reverberation. Because both target echo and reverberation are caused by the transmitted signal, they are mixed together in both time domain and frequency domain, which makes traditional signal processing methods inefficient. Blind Source Separation (BSS) is expected to isolate the reverberation from the target echo. However, the feasibility should be proved before separation. In this paper, a method based on spatial correlation is proposed to determine whether reverberation and target echo can be separated as different sources. Then, considering the nonstationarity of the reverberation, SONS (Second Order Nonstationary Source Separation) is applied to separate the original received signals. The sea experiment result shows that BSS is not only feasible but also valid to separate target echo and reverberation, and the target echo after BSS is of higher SRR which makes further process more credible.