Landmine detection with a standoff acoustic/laser technique

Thesis (S.M.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2008.Includes bibliographical references (p. 54-56).Landmines and mine-like traps are effective weapons that are difficult to detect and discriminate from a safe distance. The ability to detect landmines in their host environment at a distance and to discriminate them from other objects would be valuable for countering the landmine threat. This paper explores a standoff acoustic/laser technique to discriminate landmines from other forms of man-made objects (clutter) in an urban environment. A novel approach currently under investigation by MIT Lincoln Labs, University of Mississippi, and other groups employs a non-contact acoustic/laser technique to detect landmines from a safe standoff range. This technique uses a sound source to excite vibrations in targets with an acoustic wave. These vibrations are in turn measured remotely with a Laser Doppler Vibrometer (LDV). In this thesis, the vibration responses of landmine variants are measured, analyzed, and compared to those of common urban objects likely to be found on a landmine field or roadside. The Fourier Transform of the vibration of the target as measured by the LDV is used to generate a target vibration spectrum. Target vibration spectra in response to a sound source were experimentally measured for 59 trials, 28 of which were of simulated landmine variants and the remaining trials were of urban clutter objects. Using an algorithm adapted from a methodology for mass spectral analysis, parameters of the target signatures are estimated; then individual target signatures are classified using a Support Vector Machine (SVM) with a training set composed of parameters from the remaining members of the total population. The best results obtained from this methodology had a 71% probability of detection and a 3% false alarm rate corresponding to 20 of 28 of the simulated landmine variants correctly identified and a single clutter object misidentified as a landmine variant.by John Houston Doherty.S.M

A comprehensive review of acoustic methods for locating underground pipelines

Underground pipelines are vital means of transporting fluid resources like water, oil and gas. The process of locating buried pipelines of interest is an essential prerequisite for pipeline maintenance and repair. Acoustic pipe localization methods, as effective trenchless detection techniques, have been implemented in locating underground utilities and shown to be very promising in plastic pipeline localization. This paper presents a comprehensive review of current acoustic methods and recent advances in the localization of buried pipelines. Investigations are conducted from multiple perspectives including the wave propagation mechanism in buried pipe systems, the principles behind each method along with advantages and limitations, representative acoustic locators in commercial markets, the condition of buried pipes, as well as selection of preferred methods for locating pipelines based on the applicability of existing localization techniques. In addition, the key features of each method are summarized and suggestions for future work are proposed. Acoustic methods for locating underground pipelines have proven to be useful and effective supplements to existing localization techniques. It has been highlighted that the ability of acoustic methods to locate non-metallic objects should be of particular practical value. While this paper focuses on a specific application associated with pipeline localization, many acoustic methods are feasible across a wide range of underground infrastructures

A novel application of a microaccelerometer for target classification

This paper presents a novel method of target classification by means of a microaccelerometer. Its principle is that the seismic signals from moving vehicle targets are detected by a microaccelerometer, and targets are automatically recognized by the advanced signal processing method. The detection system based on the microaccelerometer is small in size, light in weight, has low power consumption and low cost, and can work under severe circumstances for many different applications, such as battlefield surveillance, traffic monitoring, etc. In order to extract features of seismic signals stimulated by different vehicle targets and to recognize targets, seismic properties of typical vehicle targets are researched in this paper. A technique of artificial neural networks (ANNs) is applied to the recognition of seismic signals for vehicle targets. An improved back propagation (BP) algorithm and ANN architecture have been presented to improve learning speed and avoid local minimum points in error curve. The improved BP algorithm has been used for classification and recognition of seismic signals of vehicle targets in the outdoor environment. Through experiments, it can be proven that target seismic properties acquired are correct, ANN is effective to solve the problem of classification and recognition of moving vehicle targets, and the microaccelerometer can be used in vehicle target recognition. <br /

Optimal maneuvering of seismic sensors for localization of subsurface targets

We consider the problem of detecting and locating buried land mines and subsurface objects by using a maneuvering array that receives scattered seismic surface waves. We demonstrate an adaptive system that moves an array of receivers according to an optimal positioning algorithm based on the theory of optimal experiments. The goal is to minimize the number of distinct measurements (array movements) needed to localize mines. The adaptive localization algorithm has been tested using experimental data collected in a laboratory facility at Georgia Tech. The performance of algorithm is exhibited for cases with one or two targets and in the presence of common types of clutter like rocks found in the soil. It has also been tested for the case where the propagation properties of the medium vary spatially. In almost all test cases the mines were located exactly using three or four array movements. It is envisioned that future systems could incorporate this new method into a portable mobile mine-location system

The SIMCA algorithm for processing ground penetrating radar data and its practical applications

The main objective of this thesis is to present a new image processing technique to improve the detectability of buried objects such as landmines using Ground Penetrating Radar (GPR). The main challenge of GPR based landmine detection is to have an accurate image analysis method that is capable of reducing false alarms. However an accurate image relies on having sufficient spatial resolution in the received signal. An Antipersonnel mine (APM) can have a diameter as little as 2cm, whereas many soils have very high attenuation at frequencies above 450 MHz. In order to solve the detection problem, a system level analysis of the issues involved with the recognition of landmines using image reconstruction is required. The thesis illustrates the development of a novel technique called the SIMCA (“SIMulated Correlation Algorithm”) based on area or volume correlation between the trace that would be returned by an ideal point reflector in the soil conditions at the site (obtained using the realistic simulation of Maxwell’s equations) and the actual trace. During an initialization phase, SIMCA carries out radar simulation using the system parameters of the radar and the soil properties. Then SIMCA takes the raw data as the radar is scanned over the ground and uses a clutter removal technique to remove various unwanted signals of clutter such as cross talk, initial ground reflection and antenna ringing. The trace which would be returned by a target under these conditions is then used to form a correlation kernel using a GPR simulator. The 2D GPR scan (B scan), formed by abutting successive time-amplitude plots taken from different spatial positions as column vectors,is then correlated with the kernel using the Pearson correlation coefficient resulting in a correlated image which is brightest at points most similar to the canonical target. This image is then raised to an odd power >2 to enhance the target/background separation. The first part of the thesis presents a 2-dimensional technique using the B scans which have been produced as a result of correlating the clutter removed radargram (’B scan’) with the kernel produced from the simulation. In order to validate the SIMCA 2D algorithm, qualitative evidence was used where comparison was made between the B scans produced by the SIMCA algorithm with B scans from some other techniques which are the best alternative systems reported in the open literature. It was found from this that the SIMCA algorithm clearly produces clearer B scans in comparison to the other techniques. Next quantitative evidence was used to validate the SIMCA algorithm and demonstrate that it produced clear images. Two methods are used to obtain this quantitative evidence. In the first method an expert GPR user and 4 other general users are used to predict the location of landmines from the correlated B scans and validate the SIMCA 2D algorithm. Here human users are asked to indicate the location of targets from a printed sheet of paper which shows the correlated B scans produced by the SIMCA algorithm after some training, bearing in mind that it is a blind test. For the second quantitative evidence method, the AMIRA software is used to obtain values of the burial depth and position of the target in the x direction and hence validate the SIMCA 2D algorithm. Then the absolute error values for the burial depth along with the absolute error values for the position in the x direction obtained from the SIMCA algorithm and the Scheers et al’s algorithm when compared to the corresponding ground truth values were calculated. Two-dimensional techniques that use B scans do not give accurate information on the shape and dimensions of the buried target, in comparison to 3D techniques that use 3D data (’C scans’). As a result the next part of the thesis presents a 3-dimensional technique. The equivalent 3D kernel is formed by rotating the 2D kernel produced by the simulation along the polar co-ordinates, whilst the 3D data is the clutter removed C scan. Then volume correlation is performed between the intersecting parts of the kernel and the data. This data is used to create iso-surfaces of the slices raised to an odd power > 2. To validate the algorithm an objective validation process which compares the actual target volume to that produced by the re-construction process is used. The SIMCA 3D technique and the Scheers et al’s (the best alternative system reported in the open literature) technique are used to image a variety of landmines using GPR scans. The types of mines included plastic, wooden and glass ones. In all cases clear images were obtained with SIMCA. In contrast Scheers’ algorithm, the present state-of-the-art, failed to provide clear images of non metallic landmines. For this thesis, the above algorithms have been tested for landmine data and for locating foundations in demolished buildings and to validate and demonstrate that the SIMCA algorithms are better than existing technologies such as the Scheers et al’s method and the REFLEXW commercial software

Comparing The Variability Of Natural Sand To The Variability Of Sand Containing A Simulant Land Mine

Understanding the relationship between excitation sources, buried target (i.e., buried hazard, land mine, acoustic article) response, and soil properties is fundamental to improve laser-ground-vibration sensing methods. This project investigates the natural soil’s behavior under acoustic stimuli and compares soil behavior with a buried target through geostatistical methods. Vibrational velocity of sand is measured with an LDV in a confined box filled with and without a buried target. Geostatistical calculations were performed on standardized data (e.g., background velocity and with-target velocity) sets to observe spatial variability. The standardized background velocity is mean 0 and variance of 1, while the addition of the target increases the variance to 27X the background. The background variability resembled uncorrelated white noise. The with-target variogram reveals structural features indicative of the target size and location in the measurement grid. Sensitivity studies evaluate the impact of fewer data and uncorrelated, correlated, and trending noise in the off-target soils. In a subdomain of the measurement grid, the structure of the target is preserved in the variogram and correlate with the size of the grid and surrounding encounters with off-target points. Systematically removing velocity points preserved the target presence with slight changes in the variogram structure according to new separation distances. When uncorrelated noise replaced off-target observations, the target is interpretable from the variogram up to a variance of over 400. Alternatively, when a random field with fixed correlation lengths is applied, the target is obscured at higher variances. Trended data added to off-target observations attempts to simulate field parameters. At increasing variances, strong trends in the background obscure the target. Geostatistical characteristics revealed through data sensitivity studies provides a robust indicator of target presence up to applications of high variability. Small-scale variation in sand provides features indicative of target presence. This study suggests that understanding the spatial structure of the acoustic response of natural soils is critical to the development of land mine detection technologies using an LDV. Future studies should focus on collecting experimental data from field sites

Determining boreal clearcut object properties and characteristics for identification purposes

After clearcutting, machines traffic the clearcut conducting different silvicultural activities. Many objects on a forest clearcut (slash residues, stones, stumps and roots) may disturb e.g. site preparation and planting. This paper describes properties and characteristics of these objects. A flowchart was developed that describes a possible computer-aided system that identifies the objects, and ultimately, makes a machine avoid or target them. A system for obstacle identification creates conditions for further technical development and (semi)automation of e.g. site preparation, mechanized planting, and stump removal