Detection and classification of buried dielectric anomalies using a separated aperture sensor and a neural network discriminator

Azimi-Sadjadi, M.R.; Poole, D.E.; Sheedvash, S.; Sherbondy, Kelly D.; Stricker, S.A.

doi:10.1109/19.126648

Cited by 26 publications

(34 citation statements)

References 7 publications

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“…4 A main precept in our work is that these constraints dramatically a ect the system-level use of sensor technology.…”

Section: Motivating Observation 1: Geometric Contraints On Sensor Appmentioning

confidence: 99%

“…The most successful approaches will clearly address both these aspects but present t e c hnologies either center on only one aspect or do not identify which aspect is more important g i v en the underlying sensors. 4 Note that we are not concerned with environmental factors moisture, soil granularity, incidence and type of clutter, etc., which can certainly a ect the utility of sensors. Our observations are independent o f t h e e n vironment, and deal only with the geometry of the detection task.…”

Section: Motivating Observation 1: Geometric Contraints On Sensor Appmentioning

confidence: 99%

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<title>Distributed sensing and probing with multiple search agents: toward system-level landmine detection solutions</title>

1995

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The problem of landmine detection has been studied for decades. Mine detection systems have t ypically been developed by rst identifying a sensor technology, then testing on particular manmade testbeds, then deploying the sensor on a vehicle or man-portable device. Despite much e ort, current systems still exhibit gaps between existing and desired capability, e.g., in terms of rate of advance, detection rate, and false alarm rate within demonstration testbeds. In this paper, we propose a new system-level approach to landmine detection. We argue that the landmine detection problem" cannot be attacked in a piecewise fashion: system-level solutions must simultaneously consider functional requirements, sensor technologies, models of sensors, the method of sensor application, and the platforms from which sensors are applied. This perspective allows us to shift our focus from the previous emphasis on novel sensor technology, and to go somewhat beyond traditional doctrines governing stando or man-portable detection. We rst propose a new theory of geometric sensing and probing in the mine detection context. Speci cally, w e propose new formulations of object identi cation by probing" which correspond to various sensing modalities. We demonstrate that multiple agents can achieve probe classes that are not serializable for emulation by a single probe agent. With this in mind, our second main contribution lies in proposing a new paradigm for landmine detection, based on i close-in observation with simple spectra, and ii small, inexpensive, networkable robotic sensing platforms which can act in a cooperative fashion to implement powerful multi-agent probing strategies.

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“…4 A main precept in our work is that these constraints dramatically a ect the system-level use of sensor technology.…”

Section: Motivating Observation 1: Geometric Contraints On Sensor Appmentioning

confidence: 99%

Section: Motivating Observation 1: Geometric Contraints On Sensor Appmentioning

confidence: 99%

<title>Distributed sensing and probing with multiple search agents: toward system-level landmine detection solutions</title>

1995

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“…In [12], data from a single minelane were employed as a training platform for ANN. In contrast with [10] and [11] a single GPR trace was used in order to retain the applicability of the ANN to handheld devices that often do not offer accurate positioning information. B-Scans, after simple post-processing were used as inputs in [13], but again the training set was measured at a single minelane.…”

Section: Introductionmentioning

confidence: 99%

“…In [10], C-Scans measured from a specific minelane were used to train a feed-forward neural network. Principal component analysis (PCA) was also employed in order to reduce the dimensionality of the problem.…”

Section: Introductionmentioning

confidence: 99%

Numerical modelling and neural networks for landmine detection using ground penetrating radar

Giannakis

Giannopoulos

Warren

et al. 2015

2015 8th International Workshop on Advanced Ground Penetrating Radar (IWAGPR)

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Abstract-A numerical modelling case study is presented aiming to investigate aspects of the applicability of artificial neural networks (ANN) to the problem of landmine detection using ground penetrating radar (GPR). An essential requirement of ANN and machine learning in general, is an extensive training set. A good training set should include data from as many scenarios as possible. Therefore, a training set consisting of simulated data from a diverse range of models with varying: topography, soil inhomogeneity, landmines, false alarm targets, height of the antenna, depth of the landmines, has been produced and used. Previous approaches, have employed limited training sets and as a result they often have underestimated the capabilities of ANN. In this preliminary study, a 2D Finite-Difference TimeDomain (FDTD) model has been used as the training platform for ANN. Although a 2D approach is clearly a simplification that cannot directly translate to a practical application, it is a computationally efficient approach to examine the performance of ANN subject to an extensive training set. The results are promising and provide a good basis to further expand this approach in the future by employing even more realistic, but computationally expensive, 3D models and well-characterised, real data sets.

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“…In [5]- [7], Azimi-Sadjadi et al developed neural-network-based approaches for the detection and classification of buried landmines from microwave data. Several data representation schemes such as the principal component method [5], moment invariant [6] and bispectrum [7] were used to reduce the dimensionality of the data and extract the salient features of the targets and nontargets. The test results indicated the effectiveness of the neuralnetwork-based detector/classifier systems.…”

Section: Introductionmentioning

confidence: 99%

Detection of mines and minelike targets using principal component and neural-network methods

Miao

Azimi-Sadjadi

Tan³

et al. 1998

IEEE Trans. Neural Netw.

Self Cite

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Abstract-This paper introduces a new system for real-time detection and classification of arbitrarily scattered surface-laid mines from multispectral imagery data of a minefield. The system consists of six channels which use various neural-network structures for feature extraction, detection, and classification of targets in six different optical bands ranging from near UV to near IR. A single-layer autoassociative network trained using the recursive least square (RLS) learning rule was employed in each channel to perform feature extraction. Based upon the extracted features, two different neural-network architectures were used and their performance was compared against the standard maximum likelihood (ML) classification scheme. The outputs of the detector/classifier network in all the channels were fused together in a final decision-making system. Two different final decision making schemes using the majority voting and weighted combination based on consensual theory were considered. Simulations were performed on real data for six bands and on several images in order to account for the variations in size, shape, and contrast of the targets and also the signalto-clutter ratio. The overall results showed the promise of the proposed system for detection and classification of mines and minelike tagets.

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Detection and classification of buried dielectric anomalies using a separated aperture sensor and a neural network discriminator

Cited by 26 publications

References 7 publications

<title>Distributed sensing and probing with multiple search agents: toward system-level landmine detection solutions</title>

<title>Distributed sensing and probing with multiple search agents: toward system-level landmine detection solutions</title>

Numerical modelling and neural networks for landmine detection using ground penetrating radar

Detection of mines and minelike targets using principal component and neural-network methods

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