Landmine Detection Research Articles

Landmine Detector Robotic Vehicle

Nowadays, in countries such as Afghanistan and Iraq, land mines are posing a major danger to the lives of civilians. These mines are explosive devices that are designed to disable or destroy enemy targets and are typically hidden beneath the surface of the ground. Most land mines are placed just below the ground and are triggered by pressure or trip-wires. They typically contain a number of metallic components, which can be used to detect them. The mines that are set up during times of war may remain undetected for extended periods. The term "Metal Detector" implies that it can be done from a distance, allowing for a safer approach to locating landmines. Using a mine detection robot, soldiers can traverse mine-ridden areas without entering them, providing a secure and efficient way of landmine detection. Other methods, such as radar bullets, biological methods, and mechanical methods, are much risky and put soldiers' lives at risk. The Landmine Detection Robot, which utilizes both IoT and GPS technology, is a much safer option.

Design of Unmanned Landmine Detection Vehicle Using Arduino

The concept presented in this article revolves around the idea of an economically designed UGV (Unmanned Ground Vehicle) for the demining process. Landmine discovery is a critical area of exploration due to the high number of losses and injuries caused by landmines each time. The developed technology generally requires human assistance nearby for landmine detection. Every so often the military bear loss of their equipment specialists. Also, false detection leads to the wastage of resources invested in the technology. Our prime aim is to develop a sturdy design for UGV which require minimum assembly time i.e., “Ready-to-Assemble” concept. This paper outlines the design of a UGV equipped with IR sensor, and metal detector and controlled using Arduino microcontroller. The Arduino microcontrollers are programmed to control the UGV’s movements and the sensor data processing. The UGV is built on a six-wheel drive controlled by two motor drivers, which is equipped with IR sensor to detect the heat signature of buried landmines and a metal detector to detect metallic landmines. The GPS location of the landmine is sent directly to a mobile device using a GSM Module. The UGV is controlled using Bluetooth remotely. The design of the vehicle was performed on Solidworks, CAD software. The software was used to design the chassis, suspension system, and other components such as the metal detector and IR sensor mounts. Proteus software was used to stimulate the electronic circuitry. The UGV has potential applications in detecting and removing landmines safely, minimizing the risk of human casualties. Future work could involve improving the system's accuracy and efficiency to make it a more effective tool for landmine detection and removal.

Educational high school physics demonstration apparatus for acoustic landmine simulant resonance detection

This landmine “simulant” resonance detection demo was created in conjunction with a high school physics mentorship, using a variety of easily-accessibleequipment in order to demonstrate an important application of fundamental concepts of sound and vibration. A plastic tray (4 in. deep) contained a 3-in. diameter drum-like circular landmine simulant (1.5 in. high) with a thin acrylic top plate buried (1 in. deep) in a 3 in. layer of dry sifted masonry sand. A small accelerometer was place on the surface over the simulant. Airborne sound, a loud speaker chirp, with frequency slowly increasing over time, was created using a Mathematica® generated wav file played by the Raspberry Pi® computer. Sound excited the sand vibration that coupled to the top plate. The accelerometer generated a frequency response voltage, related to the sweep time. The Raspberry Pi® also played a role in implementing the A to D conversion, so that the frequency response near resonance was displayed as a voltage in a readable format. With this demo apparatus high school students will be excited by the fundamentals of wave propagation and concepts about resonance with regards to its real-life applications. [Specially inspired by James M. Sabatier’s research on acoustic landmine detection.]

Land mines detection, mapping and clearance using Quadcopter in Yemen: A perspective study

People who live or work in areas where there have been armed conflicts may face the threat of landmines and explosive hazards, such as unexploded or abandoned ordnance, abandoned military vehicles, and equipment. Therefore, this is a tremendous obstacle to development and construction in such areas, which requires awareness of the threat and appropriate preventive action. This paper aims to facilitate the detection and elimination of planted landmines in Yemen. Two stages are proposed to be implemented in this study for the demining techniques: detection and clearance phases. The first phase uses a Quadcopter with detecting sensors to locate and map the mines' spot, while the second stage uses a Quadcopter to unload a mass on that determined location from a certain height. By employing these two stages, it is expected to quickly and accurately detect the locations of landmines. That will reduce risk, protect human lives, and provide a more efficient and safe way to sweep minefield workers using sample, small, and easy control equipment.

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.