Ian J. Chant

Signal processing techniques for landmine detection using impulse ground penetrating radar

Landmines are affecting the lives and livelihoods of millions of people around the world. A number of detection techniques, developed for use with impulse ground penetrating radar, are described, with emphasis on a Kalman filter based approach. Comparison of results from real data show that the Kalman filter algorithm provides the best detection performance, although its computational burden is also the highest.

Landmine detection using single sensor metal detectors

Historically, metal detectors have been essential tools for demining. However they have been unable to keep pace with developments that made landmines more difficult to find. Here, techniques for the detection of buried objects using a metal detector are presented, evaluated and compared. The findings highlight a number of deficiencies, as well as a number of strengths, in the proposed detectors. Of particular interest are the parameters found using Pronys method, as well as the difference operator, reverse arrangements test and the median filter. Suggestions are made for the improvement of a number of detectors.

Modeling GPR echoes from land mines using linear combinations of exponentially damped sinusoids

Ultra-wideband returns from surface-scattered and buried land mines give distinctive echoes that depend on the target type and soil environment. These echoes remain relatively stable for land mines with non-metallic casings but vary with the soil environment for land mines with metallic casings. This fact indicates that the signature resulting from the non-metallic-cased land mine is due to internal structure, whereas the signature of the metallic-cased target is due to interaction of the illuminating pulse with the metal case and the surrounding medium. The shape of these echoes can be described by a series of damped sinusoids. Certain parameters of the functions are believed to be stable for the same target in a variety of environments. Thus the echo can theoretically be used to identify targets with non-metallic casings by determining the position of the fixed poles that describe the exponential dampled sinusoids associated with a particular target type. This paper examines extraction of these poles, by the Prony method, for different types of non-metallic land mines and land-mine-like targets.

Microwave enhancement of thermal land mine signatures

Buried landmine detection using IR sensor has been investigated by a number of researchers around the world. The technique is promising, particularly for unpaved roads in hot dry countries with little roadside shading. The thermal source for this detection mechanism is the solar heat flux and this imposes constraints on the time of day and weather conditions when this technique is effective. Here we investigate the use of a microwave energy source to drive the thermal flux with the aim of extending the usefulness of IR detection to full day operation in all forms of overcast weather. The microwave radiation penetrates the soil providing a depth to the heating process. We present a 1D model of microwave absorption and heat dissipation by moisture-laden soils which contain landmine-like buried objects. The microwave source for our numerical experiments is a 2.45 GHz, 5 kW. If required, a possible power increase to as high as 50 kW is envisaged. The physical mechanisms employed are suitable for a vehicle mounted detection system. We consider thermal properties of plastic bodied landmines shallowly buried in soil at the peak power point in the ground footprint of the heat source and review some of the factors which govern the thermal difference which will result. In particular we review (a) the factors which determine the radiant power penetrating the ground, and (b) the absorption and conduction processes which subsequently occur within the vicinity of the landmine.

Suggested soil characterization techniques and surrogate targets for ultrawideband radar mine detection experiments

The Technical Cooperation Program subgroup K Action Group 23 is engaged in collaborative ultra-wideband (UWB) radar land mien detection experiments using instruments located in Australia, Canada, the UK and the US. In order to compare results, it is essential to develop standard characterization techniques to provide an improved basis for comparison of data and radar techniques. This paper discusses some suggested standard soil characterization techniques and standardized targets for UWB GPR mine detection experiments. For land mine detection using ground penetrating radar, it is the electrical characterization of soil over the relevant frequency range that is important. Some results of analysis of soil from test sites are given and discussed. Standardized targets are needed to allow reliable comparison of independently conducted experiments. Surrogate targets for this purpose need to be safely handled and deployed, sufficiently realistic to allow estimation of mine detection performance, and easily transported across national boundaries. It is desirable to have simple targets to allow comparison of numerical models with experiment. The design of a limited target set is discussed and sample signature measurements are presented in this and companion papers.

Feature extraction and classification of minelike targets from GPR data using Gaussian mixture models

This paper presents a method for target-specific feature extraction from ground penetrating radar (GPR) signatures and examines the applicability of such features to classification of minelike targets. The signatures of a set of targets measured in soils with different dielectric permitivities and at various burial depths are used in the experiments. Each target, buried in one soil and at one particular depth, is represented by a set of complex poles computed from the ensemble of neighbouring target-specific signatures. The empirical probability density of the corresponding set of pole frequencies is modelled as a mixture of univariate Gaussian functions. Robust partial modelling algorithm is applied to determine the number of Gaussians in the mixture and to estimate their parameters. The procedure that uses the resulting target-specific Gaussian mixtures to compute the class-conditional probabilities of a target is applied for target classification.

Detection of landmines using ground-penetrating radar

The detection of anti-personnel landmines is very difficult due to the minimal content of metal in their structure, and thus, the inability to detect them with a metal detector. A promising alternative to metal detector technology is given by GPR systems. These have the potential to detect low- and non-metallic landmines and can be used to classify the targets. We propose two methods based on the bootstrap to detect changes in backscattering echoes. The first approach is parametric in which each signal is modelled by an AM-FM signal. The difference in the phase of the signal suggest possible existence of a target. The second approach is nonparametric where the existence of a target is indicated by the change in the average signal power. We apply the methods to real GPR data.

A multi-sensor land mine detection system: hardware and architectural outline of the Australian RRAMNS CTD system

The Rapid Route Area and Mine Neutralisation System (RRAMNS) Capability Technology Demonstrator (CTD) is a countermine detection project undertaken by DSTO and supported by the Australian Defence Force (ADF). The limited time and budget for this CTD resulted in some difficult strategic decisions with regard to hardware selection and system architecture. Although the delivered system has certain limitations arising from its experimental status, many lessons have been learned which illustrate a pragmatic path for future development. RRAMNS a similar sensor suite to other systems, in that three complementary sensors are included. These are Ground Probing Radar, Metal Detector Array, and multi-band electro-optic sensors. However, RRAMNS uses a unique imaging system and a network based real-time control and sensor fusion architecture. The relatively simple integration of each of these components could be the basis for a robust and cost-effective operational system. The RRAMNS imaging system consists of three cameras which cover the visible spectrum, the mid-wave and long-wave infrared region. This subsystem can be used separately as a scouting sensor. This paper describes the system at its mid-2004 status, when full integration of all detection components was achieved.

Kalman detection of landmines in metal detector array data

Tens of millions of mines are currently buried in a number of countries around the world. They cause injuries to civilians and economic damage to war-torn countries by restricting the civilian access to huge agricultural lands. Rapid Route and Area Mine Neutralisation System (RRAMNS) is a Capability Technology Demonstrator (CTD) conducted by Defence Science and Technology Organisation (DSTO) in Australia. The detection system consists of three sensors: a metal detector array, an array of ground penetrating radar (GPR), and forward looking infrared and visual imaging systems. The Kalman filter-based detection technique has previously been shown to be a powerful tool for detection of landmines from metal detector data. In this paper scalar Kalman filter-based detection algorithm has been extended to the multi-dimensional case. The new version of the detection technique has been successfully implemented in RRAMNS real-time mine detection system.

Modified Kalman target detection algorithm applied to metal detection

We discuss an improved Kalman filter-based algorithm for automatic detection of targets from metal detector data. This innovations process utilizes the difference between measurements and single-stage predicted values. In our previous work a Kalman filter based algorithm was used to detect targets assuming that the metal detector output signal is a constant in the background. In this work we extend the capability of this method to detect targets by assuming the distribution of the metal detector output data is Gaussian. The analysis has been extended by computing state estimation errors, covariance matrices and treating metal detector background data as a discrete-time Gauss-Markov random sequence. The proposed detection algorithms have been applied to Minelab F1A4-MIM metal detector data.