Handheld, vehicle mounted and air-borne Ground Penetrating Radar (GPR) systems have been identified as potential technology solutions for detection of current and evolving buried threat objects. However, the success rate of the GPR systems are limited by operational conditions and the robustness of automatic target recognition (ATR) algorithms embedded with the systems. With the ever-increasing complexity of target configuration and their deployment scenarios it is becoming a challenge to develop ATR algorithms robust enough to detect and identify GPR signatures of a wide variety of threat objects. The aim of this research is to design a potential solution for detection of threat objects using GPR data and reducing the number of false alarms. In this paper, a Machine Learning (ML) based ATR algorithm applicable to GPR data is developed to detect complex patterns and trends relevant to a multitude of threat objects. The proposed ATR algorithm has been validated using a data set acquired by a vehicle mounted GPR array. The data set utilized in this investigation involved GPR data of threat objects (both conventional and improvised) commonly found in realistic operational scenarios. Lane based summaries of the algorithm performance are presented in terms of the probability of detection threat objects and false alarm rate. Preliminary results of the proposed ML techniques have shown promise of achieving a high detection rate and a low false alarm rate in multiple GPR data sets collected in challenging geographical locations.
In ground penetrating radars, background clutter, which comprises the signals backscattered from the rough, uneven ground surface and the background noise, impairs the visualization of buried objects and subsurface inspections. In this paper, a clutter mitigation method is proposed for target detection. The removal of background clutter is formulated as a constrained optimization problem to obtain a low-rank matrix and a sparse matrix. The low-rank matrix captures the ground surface reflections and the background noise, whereas the sparse matrix contains the target reflections. An optimization method based on split-Bregman algorithm is developed to estimate these two matrices from the input GPR data. Evaluated on real radar data, the proposed method achieves promising results in removing the background clutter and enhancing the target signature.
Ground Penetrating Radar (GPR) is considered as one of the promising technologies to address the challenges of detecting buried threat objects. However, the success rate of the GPR systems are limited by operational conditions and the robustness of automatic target recognition (ATR) algorithms embedded with the systems. In this paper an alternate ATR algorithm applicable to GPR is developed by combining image pre-processing and machine learning techniques. The aim of this research was to design a potential solution for detection of threat alarms using GPR data and reducing the number of false alarms through classification into one of the predefined categories of target types. The proposed ATR algorithm has been validated using a data set acquired by a vehicle-mounted GPR array. The data set utilized in this investigation involved greyscale GPR images of threat objects (both conventional and improvised) commonly found in realistic operational scenarios. Target based summaries of the algorithm performance are presented in terms of the probability of detection, false alarm rate, and confidence of allocating detections to a predefined target class.
Vehicle Mounted Metal Detector (VMMD) systems are widely used for detection of threat objects in humanitarian demining and military route clearance scenarios. Due to the diverse nature of such operational conditions, operational use of VMMD without a proper understanding of its capability boundaries may lead to heavy causalities. Multi-criteria fitness evaluations are crucial for determining capability boundaries of any sensor-based demining equipment. Evaluation of sensor based military equipment is a multi-disciplinary topic combining the efforts of researchers, operators, managers and commanders having different professional backgrounds and knowledge profiles. Information acquired through field tests usually involves uncertainty, vagueness and imprecision due to variations in test and evaluation conditions during a single test or series of tests. This report presents a fuzzy logic based methodology for experimental data analysis and performance evaluation of VMMD. This data evaluation methodology has been developed to evaluate sensor performance by consolidating expert knowledge with experimental data. A case study is presented by implementing the proposed data analysis framework in a VMMD evaluation scenario. The results of this analysis confirm accuracy, practicability and reliability of the fuzzy logic based sensor performance evaluation framework.
KEYWORDS: Sensors, Land mines, Imaging systems, Computing systems, Cameras, Roads, Metals, Control systems, General packet radio service, Image processing
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.
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.
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.
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