Landmine Detection Research Articles

Utilizing UAV-based hyperspectral imaging to detect surficial explosive ordnance

Across postconflict regions of the world, explosive ordnance (EO), which includes remnant antipersonnel land mines, antivehicle/tank mines, unexploded cluster munitions, improvised explosive devices, and explosive remnants of war (ERW) such as unexploded ordnance and abandoned explosive ordnance, remains a critical humanitarian concern. Clearance and land release efforts anchored on manual geophysical detection and mechanical probing methods remain painstakingly slow, expensive, and dangerous to operators. As a result, postconflict regions impacted by EO contamination significantly lag in social and economic development. Developing, calibrating, and field testing more efficient detection methods for surficial EO is a crucial task. Unpiloted aerial systems featuring advanced remote sensing capabilities are a key technology that may allow the tide to turn in the EO crisis. Specifically, recent advances in hardware design have allowed for effective deployment of small, light, and less power consuming hyperspectral imaging (HSI) systems from small unpiloted aerial vehicles (UAVs). Our proof-of-concept study employs UAV-based HSI to deliver a safer, faster, and more cost-efficient method of surface land mine and ERW detection compared to current ground-based detection methods. Our results indicate that analysis of HSI data sets can produce spectral profiles and derivative data products to distinguish multiple ERW and mine types in a variety of host environments.

Performance upgrade of a microbial explosives’ sensor strain by screening a high throughput saturation library of a transcriptional regulator

We present a methodology for a high-throughput screening (HTS) of transcription factor libraries, based on bacterial cells and GFP fluorescence. The method is demonstrated on the Escherichia coli LysR-type transcriptional regulator YhaJ, a key element in 2,4-dinitrotuluene (DNT) detection by bacterial explosives’ sensor strains. Enhancing the performance characteristics of the YhaJ transcription factor is essential for future standoff detection of buried landmines. However, conventional directed evolution methods for modifying YhaJ are limited in scope, due to the vast sequence space and the absence of efficient screening methods to select optimal transcription factor mutants. To overcome this limitation, we have constructed a focused saturation library of ca. 6.4 × 107 yhaJ variants, and have screened over 70 % of its sequence space using fluorescence-activated cell sorting (FACS). Through this screening process, we have identified YhaJ mutants exhibiting superior fluorescence responses to DNT, which were then effectively transformed into a bioluminescence-based DNT detection system. The best modified DNT reporter strain demonstrated a 7-fold lower DNT detection threshold, a 45-fold increased signal intensity, and a 40 % shorter response time compared to the parental bioreporter. The FACS-based HTS approach presented here may hold a potential for future molecular enhancement of other sensing and catalytic bioreactions.

Landmine Detection by Correlation Method in Different Environments

In this paper, the purpose is the detection of a landmine in an environment using the scattering parameter. The Ground Penetrating Radar (GPR) measures the scattering parameter. The scattering parameter is taken from a simple environment in which a landmine exists alone as a reference signal. Signals taken from the complex environment are compared with the reference signal. In fact, the presence of a landmine influences the scattering parameter. The similarity between measured signal and reference signal determines the presence of the landmine. The correlation function calculates similarity. This method is very applicable because the scattering parameter is unique.

Manyetik Anomali Yöntemi Kullanılarak Düşük Maliyetli Mayın Dedektörü Tasarımı

Buried mines pose great dangers to humans and animals around the world, which means thousands of people die each year from buried mines. Detecting and destroying these mines without harming people is an important issue. Today, these landmines are detected using different methods such as Ground Effect Radar, Electromagnetic induction, Infrared and Nuclear Quadrupole Resonance and active sensors are generally used in most of these methods. Although active sensor-based landmine detectors are often used for performance reasons, they can cause unintentional landmine explosions because they operate with transmitted and reflected signals. On the other hand, there is no such obstacle that can perform better in passive sensor-based landmine detectors depending on the design criteria. Therefore, in this study, a prototype design including passive sensor with magnetic anomaly method has been developed and shown. For the performance analysis of this detector, real landmines are used and the designed system is tested with different distance values in different soil types. The results show that the prototype produced successfully detects different types of landmines, is assertive in its lightness and only 1750 grams with its battery, providing sensitivity as well as advantages such as ease of use and low cost. It also shows the feature of being the first handheld landmine detector based on magnetic anomaly.

Development of a fully planar logarithmic spiral antenna with integrated balun in UWB GPR systems for landmines detection

ABSTRACT In this paper, a compact cavity-backed logarithmic spiral antenna with an integrated broadband planar balun is presented. This fully planar antenna is used for detection of anti-personnel (AP) landmines in GPR systems. Unlike conventional spiral antennas, which are fed by unbalanced coaxial cable or vertical baluns, and these feed lines make them bulky and frail, in this design, a microstrip-to-parallel strip balun is integrated with the spiral antenna and makes the whole structure compact, planar, and robust. Moreover, a metal cavity is utilized at the back of the antenna to prevent backward radiation and enhance the forward penetration depth to the ground. In choosing the operational frequency bandwidth, a compromise is made between the penetration depth and detection resolution. A prototype of the antenna is fabricated and its performance in landmine detection is evaluated with measurements. The measured results show that the antenna works over the frequency range of 1.5 GHz−4.5 GHz (bandwidth = 100%) with |S11| < −10 dB, a penetration depth of 10 cm in the soil with good detection resolution, gain >8 dB and axial ratio <3 dB. These factors make the proposed antenna a suitable candidate for pulse radar applications, in which low pulse distortion and wide bandwidth are key factors.

UAS-Borne Radar for Remote Sensing: A Review

Since the 1950s, radar sensors have been widely used for the monitoring of the earth’s surface. The current radars for remote sensing can be divided into two main categories: Space/aerial-borne and ground-based systems. The unmanned aerial system (UAS) could bridge the gap between these two technologies. Indeed, UAS-borne radars can perform long scans (up to 100/200 m) in a brief time (a few minutes). From the 2010s, the interest in UAS-borne radars has increased in the research community, and it has led to the development of some commercial equipment and more than 150 papers. This review aims to present a study on the state-of-the-art of UAS-borne radars and to outline the future potential of this technology. In this work, the scientific literature was categorized in terms of application, purpose of the paper, radar technology, and type of UAS. In addition, a brief review of the main national UAS regulations is presented. The review on the technological state-of-the-art shows that there is currently no standard in terms of radar technology, and that the multi-helicopter could be the most used UAS in the near future. Moreover, the UAS-borne radar can be used for several remote sensing applications: From landmine detection to smart agriculture, and from archeological survey to research and rescue applications. Finally, the UAS-borne radar appears to be a mature technology, which is almost ready for industrialization. The main developmental limit may be found in the flight regulation, which does not allow for many operations and imposes strict limits on the payload weight.

A comprehensive review on landmine detection using deep learning techniques in 5G environment: open issues and challenges

A comprehensive review on landmine detection using deep learning techniques in 5G environment: open issues and challenges

Deep reinforcement learning based trajectory optimization for magnetometer-mounted UAV to landmine detection

Unmanned aerial vehicles (UAVs) have emerged as a viable choice for data collection and landmine (LM) detection. The LM buried under the dirt or sand is detected using a UAV-mounted magnetometer in this paper. A UAV is deployed to gather data along the intended route when the magnetometer receives a signal from the LMs. During a whole round of data collection, we want to reduce the total energy consumption of the UAV-Magnetometer-LM system. To do this, we turn the energy consumption reduction issue into a limited combinatorial optimization problem by concurrently picking time slots and arranging the UAV’s visitation sequence to identify the LM. The problem of minimizing energy usage is NP-hard, making it difficult to solve optimally. In order to tackle this challenge, we used the deep reinforcement learning (DRL) based deep deterministic policy gradient (DDPG) scheme. DDPG is used to enhance the convergence speed and eliminate redundant computations. Furthermore, to improve the detection in real-time, we proposed the proximal online policy technique (POPT). Numerical results demonstrate that the proposed scheme consumes 37.14%, 31.25%, and 21.42% better results than synthetic aperture radar (SAR), convolution neural network (CNN), and double deep recurrent Q-network (DDRQN).

Method for Minefields Mapping by Imagery from Unmanned Aerial Vehicle

The paper proposes a method for minefields mapping by the centimeter resolution imagery from a copter-type unmanned aerial vehicle (UAV) which is equipped with multispectral camera and thermal infrared camera. The research methodology is the probability fusion by each sensor and the subsequent decision making on the landmine presence/absence. Models for the landmine detection in multispectral and thermal images are considered. The training sample structuration is proposed for the landmine detection reliability enhancement. The local temperature anomalies of landmine size are allocated by sliding window scanning the thermal image. The experimental performance of actual landmines detection at a special test site in Ukraine is described. The probability of correct landmine detection was 0.92 while with a false alarm probability it was 0.45.

Implementation of an Artificial Intelligence Approach to GPR Systems for Landmine Detection

Artificial Neural Network (ANN) approaches are applied to detect and determine the object class using a special set of the UltraWideBand (UWB) pulse Ground Penetrating Radar (GPR) sounding results. It used the results of GPR sounding with the antenna system, consisting of one radiator and four receiving antennas located around the transmitting antenna. The presence of four receiving antennas and, accordingly, the signals received from four spatially separated positions of the antennas provide a collection of signals received after reflection from an object at different angles and, due to this, to determine the location of the object in a coordinate system, connected to the antenna. We considered the sums and differences of signals received by two of the four antennas in six possible combinations: (1 and 2, 1 and 3, 2 and 3, 1 and 4, etc.). These combinations were then stacked sequentially one by one into one long signal. Synthetic signals constructed in such a way contain many more notable differences and specific information about the class to which the object belongs as well as the location of the searched object compared to the signals obtained by an antenna system with just one radiating and one receiving antenna. It therefore increases the accuracy in determining the object’s coordinates and its classification. The pulse radiation, propagation, and scattering are numerically simulated by the finite difference time domain (FDTD) method. Results from the experiment on mine detection are used to examine ANN too. The set of signals from different objects having different distances from the GPR was used as a training and testing dataset for ANN. The training aims to recognize and classify the detected object as a landmine or other object and to determine its location. The influence of Gaussian noise added to the signals on noise immunity of ANN was investigated. The recognition results obtained by using an ANN ensemble are presented. The ensemble consists of fully connected and recurrent neural networks, gated recurrent units, and a long-short term memory network. The results of the recognition by all ANNs are processed by a meta network to provide a better quality of underground object classification.

Lower-Weight Landmine Detection Under Various Buried Conditions Based on PGNAA and Machine Learning

The performance of a prompt gamma neutron activation analysis (PGNAA) system for lower-weight landmine detection is investigated in this study. A total of 2880 characteristic gamma-ray spectra of 10 buried samples (five explosives and five nonexplosives), within a weight range of 0.01 to 10 kg and a hidden depth of 2.5 to 15 cm, under 0%, 10%, and 20% soil moisture conditions, were generated using Monte Carlo N-Particle Code 5 (MCNP5). The conventional characteristic peak analysis method was not applicable to lower-weight sample detection. The discrimination accuracy was acceptable only under 0% soil moisture when explosives exceeded 2 kg with the discrimination accuracy exceeding 80%. Four machine learning models, including radial basis function (RBF) neural network, fully connected neural network, XGBoost, and LightGBM, were used to perform whole-spectrum analysis, and better performance was demonstrated. The discrimination accuracy exceeded 90% in most cases, and the RBF neural network was demonstrated to be the best performance (96.6% for explosives and 95.1% for nonexplosives). All four of these models were insensitive to soil moisture. The minimum detectable weight of 0.02 kg for the simulation data provided valuable reference for experimental applications. These results indicate that machine learning was an effective method for lower-weight landmine detection using PGNAA under complicated conditions.

Antenna Design Considerations for Ground Penetrating Radar Landmine Detection

The performance of very short-range radar systems, such as ground penetrating radar for antipersonnel landmine detection, is critically dependent on the impulse characteristics of the antennas that are used. Typically, a pair of individual antennas is used in the radar, one for transmit and one for receive. These antennas are usually closely spaced for reasons of both portability and close in detectability. A variety of design techniques are used to achieve their wideband performance in terms of return loss, isolation, radiation patterns, polarization, and impulse formation. Not only the antenna elements but also their surrounds form the overall antenna structure and this ensemble becomes integral to the antenna performance and design process. Both the antenna and the surrounding structure can create late time signals or time sidelobes, which are in effect a form of self-generated clutter. Minimizing the duration of the antenna impulse response assists in reducing self-generated clutter and achieving optimum radar performance. This article considers the impact of the antenna on the system performance of the radar and shows that the clutter performance of the antenna can be the prime metric of system detection rather than the noise figure, given that the self-clutter often exceeds the latter.

Robust Copula-Based Detection of Shallow-Buried Landmines With Forward-Looking Radar

We propose a technique for landmine detection using forward-looking ground-penetrating radar. The detector is applied to radar images obtained from multiple viewpoints of the region of interest and is based on a robust version of the likelihood ratio test (LRT). We incorporate the statistical dependence between multiview images into the test via copula-based model. The test is designed to maximize the worst-case performance over all feasible pairs of target and clutter distributions, thereby eliminating the need for strong assumptions about the image statistics. We evaluate the detection performance of the proposed technique for different copula functions representing the dependence structure. Using electromagnetic modeled data of shallow-buried targets under varying ground surface roughness profiles, we demonstrate the superiority of the robust copula-based detector over existing parametric and robust LRT approaches designed under the assumption of statistical independence of multiview images.

Under the Sand: Navigation and Localization of a Micro Aerial Vehicle for Landmine Detection with Ground-Penetrating Synthetic Aperture Radar

Ground-penetrating radar mounted on a micro aerial vehicle (MAV) is a promising tool to assist humanitarian landmine clearance. However, the quality of synthetic aperture radar images depends on accurate and precise motion estimation of the radar antennas as well as generating informative viewpoints with the MAV. This paper presents a complete and automatic airborne ground-penetrating synthetic aperture radar (GPSAR) system. The system consists of a spatially calibrated and temporally synchronized industrial grade sensor suite that enables navigation above ground level, radar imaging, and optical imaging. A custom mission planning framework allows generation and automatic execution of stripmap and circular GPSAR trajectories controlled above ground level as well as aerial imaging survey flights. A factor graph based state estimator fuses measurements from dual receiver real-time kinematic (RTK) global navigation satellite system (GNSS) and an inertial measurement unit (IMU) to obtain precise, high-rate platform positions and orientations. Ground truth experiments showed sensor timing as accurate as 0.8 µs and as precise as 0.1 µs with localization rates of 1 kHz. The dual position factor formulation improves online localization accuracy up to 40 % and batch localization accuracy up to 59 % compared to a single position factor with uncertain heading initialization. Our field trials validated a localization accuracy and precision that enables coherent radar measurement addition and detection of radar targets buried in sand. This validates the potential as an aerial landmine detection system.

Landmine detection via multivariate image analysis

Despite the technological advances in sensors, landmine action still causes many deaths and mutilations in several countries worldwide, and the search for efficient methods to detect mines has become a relevant issue. Thus, this work proposes a landmine detection approach using multivariate analysis of infrared (IR) images. In addition, it is proposed to monitor these images through a control chart to help identify the mine. Experiments following this approach have indicated effectiveness in the detection of landmines, enabling their safe removal.

Landmine Detection with Drones

Abstract The opposing forces often laid antitank or antipersonnel minefields during wars and other armed conflicts. However, countries those ratified the Ottawa Treaty had abandoned using antipersonnel landmines in the future, the mines laid earlier are still can be found in huge numbers buried under the soil and they cause serious injuries to several people. Safe detection of mines, other dangerous explosive devices and the precise determination of their location sometimes require specific equipment. The authors introduce certain aerial vehicles and some of their special detection devices those can be used for locating landmines from the air.

Introduction of quorum sensing elements into bacterial bioreporter circuits enhances explosives' detection capabilities.

A possible solution for the standoff detection of buried landmines is based on the use of microbial bioreporters, genetically engineered to emit a remotely detectable optical signal in response to trace amounts of explosives’ signature chemicals, mostly 2,4‐dinitrotoluene (DNT). Previously developed DNT sensor strains were based on the fusion of a DNT‐inducible gene promoter to a reporting element, either a fluorescent protein gene or a bacterial bioluminescence gene cassette. In the present study, a different approach was used: the DNT‐inducible promoter activates, in Escherichia coli, the quorum‐sensing luxI and luxR genes of Aliivibrio fischeri. N‐Acyl homoserine lactone (AHL), synthesized by LuxI, combines with LuxR and activates the bioluminescence reporter genes. The resulting bioreporter displayed a dose‐dependent luminescent signal in the presence of DNT. Performance of the sensor strain was further enhanced by manipulation of the sensing element (combining the E. coli DNT‐inducible azoR and yqjF gene promoters), by replacing the luminescence gene cassette of Photorhabdus luminescens luxCDABE with A. fischeri luxCDABEG, and by introducing two mutations, eutE and ygdD, into the host strain. DNT detection sensitivity of the final bioreporter was over 340‐fold higher than the original construct.

Enhancing DNT Detection by a Bacterial Bioreporter: Directed Evolution of the Transcriptional Activator YhaJ.

Detection of buried landmines is a dangerous and complicated task that consumes large financial resources and poses significant risks to the personnel involved. A potential alternative to conventional detection methodologies is the use of microbial bioreporters, capable of emitting an optical signal upon exposure to explosives, thus revealing to a remote detector the location of buried explosive devices. We have previously reported the design, construction, and optimization of an Escherichia coli-based bioreporter for the detection of 2,4,6-trinitrotoluene (TNT) and its accompanying impurity 2,4-dinitrotoluene (DNT). Here we describe the further enhancement of this bioreporter by the directed evolution of YhaJ, the transcriptional activator of the yqjF gene promoter, the sensing element of the bioreporter’s molecular circuit. This process resulted in a 37-fold reduction of the detection threshold, as well as significant enhancements to signal intensity and response time, rendering this sensor strain more suitable for detecting the minute concentrations of DNT in the soil above buried landmines. The capability of this enhanced bioreporter to detect DNT buried in sand is demonstrated.

Military Dogs and Their Soldier Companions: The More-than-human Biopolitics of Leishmaniasis in Conflict-torn Colombia.

Cutaneous leishmaniasis is a vector‐borne disease that produces growing skin ulcers. In Colombia, the transmitting phlebotomine sandfly is native to the same jungles that have been the primary theater of war. Although combatants are the most affected by leishmaniasis, military landmine detection dogs are also significantly impacted. This article draws on ethnographic field research with human and canine members of the Colombian military. While their leishmaniasis ulcers constitute a shared expression of violence that makes evident the closeness of the human–dog bond, differences in their state‐provided health care reveal the production of shifting species hierarchies. I argue that war scrambles both human–dog affective relationships and biopolitically configured interspecies hierarchies in ways that produce suffering, not just for humans and dogs separately, but also for the bonds they forge together. Building peace through health care demands repairing the ways in which armed violence has rendered the bonds between humans and nonhumans pathological.

Comparative optimization of Be/Zr(BH4)4 and Be/Be(BH4)2 as 252Cf source shielding assemblies: Effect on landmine detection by neutron backscattering technique

Monte Carlo simulations were used to model a portable Neutron backscattering (NBT) sensor suitable for detecting plastic anti-personnel mines (APMs) buried in dry and moist soils. The model consists of a 100 MBq 252Cf source encapsulated in a neutron reflector/shield assembly and centered between two 3He detectors. Multi-parameter optimization was performed to investigate the efficiency of Be/Zr(BH4)4 and Be/Be(BH4)2 assemblies in terms of increasing the signal-to-background (S/B) ratio and reducing the total dose equivalent rate. The MCNP results showed that 2 cm Be/3 cm Zr(BH4)4 and 2 cm Be/3 cm Be(BH4)2 are the optimal configurations. However, due to portability requirements and abundance of Be, the 252Cf-2 cm Be/3 cm Be(BH4)2 NBT model was selected to scan the center of APM buried 3 cm deep in dry and moist soils. The selected NBT model has positively identified the APM with a S/B ratio of 886 for dry soils of 1 wt% hydrogen content and with S/B ratios of 615, 398, 86, and 12 for the moist soils containing 4, 6, 10, and 14 wt% hydrogen, respectively. The total dose equivalent rate reached 0.0031 mSv/h, suggesting a work load of 8 h/day for 806 days within the permissible annual dose limit of 20 mSv.