Application of Polarimetric Features and Support Vector Machines for Classification of Improvised Explosive Devices

Gutiérrez, Sergio Armando; Vega, Félix; González, Fabio A.; Baer, Christoph; Sachs, J.

doi:10.1109/lawp.2019.2934691

Cited by 12 publications

(3 citation statements)

References 28 publications

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“…The backscattering response of a metal cylinder in free space was measured by using two RFSpace TSA600 antennas and a VNA. Likewise, the GPR measurements in a sandbox for buried objects are described in [22], where the EM responses for a metal plate and a metal can were obtained using Vivaldi antennas with operation frequencies from 0.8 GHz to 5 GHz.…”

Section: Accuracy and Feasibility For Hardware Implementationmentioning

confidence: 99%

“…The setup is shown in Figure 15 alongside the reconstruction-wise behavior of each method in Figure 16. Now, as a critical evaluation requires the backscattered response of buried objects, a sandbox experiment was performed using an ultrawide band (UWB) radar from 0.8 GHz to 5 GHz and Vivaldi antennas as well [22]. As before, the time and frequency responses for a metal can and a metal plate were obtained and processed with complex natural resonances extraction methods (CNREMs), and the reconstruction of the signal is shown in Figure 17.…”

Section: Experimentally Registered Signaturesmentioning

confidence: 99%

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Hardware Suitability of Complex Natural Resonances Extraction Algorithms in Backscattered Radar Signals

Gallego,

Roman

2023

Algorithms

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Complex natural resonances (CNRs) extraction methods such as matrix pencil method (MPM), Cauchy, vector-fitting Cauchy method (VCM), or Prony’s method decompose a signal in terms of frequency components and damping factors based on Baum’s singularity expansion method (SEM) either in the time or frequency domain. The validation of these CNRs is accomplished through a reconstruction of the signal based on these complex poles and residues and a comparison with the input signal. Here, we perform quantitative performance metrics in order to have an evaluation of each method’s hardware suitability factor before selecting a hardware platform using benchmark signals, simulations of backscattering scenarios, and experiments.

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Section: Accuracy and Feasibility For Hardware Implementationmentioning

confidence: 99%

Section: Experimentally Registered Signaturesmentioning

confidence: 99%

Hardware Suitability of Complex Natural Resonances Extraction Algorithms in Backscattered Radar Signals

Gallego,

Roman

2023

Algorithms

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show abstract

“…Therefore, the procedure of collecting true measurements is not effective, as only a small amount of possible IEDs can be investigated. For instance, in [20], a total of 30 different IEDs were manufactured to obtain realistic training data. Afterward, an SVM trained with the measured training data reached an accuracy of up to 87.02%.…”

Section: Introductionmentioning

confidence: 99%

A Radar Echo Simulator for the Synthesis of Randomized Training Data Sets in the Context of AI-Based Applications

Schorlemer,

Altholz,

Barowski

et al. 2024

Sensors

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Supervised machine learning algorithms usually require huge labeled data sets to produce sufficiently good results. For many applications, these data sets are still not available today, and the reasons for this can be manifold. As a solution, the missing training data can be generated by fast simulators. This procedure is well studied and allows filling possible gaps in the training data, which can further improve the results of a machine learning model. For this reason, this article deals with the development of a two-dimensional electromagnetic field simulator for modeling the response of a radar sensor in an imaging system based on the synthetic aperture radar principle. The creation of completely random scenes is essential to achieve data sets with large variance. Therefore, special emphasis is placed on the development of methods that allow creating random objects, which can then be assembled into an entire scene. In the context of this contribution, we focus on humanitarian demining with regard to improvised explosive devices using a ground-penetrating radar system. This is an area where the use of trained classifiers is of great importance, but in practice, there are little to no labeled datasets for the training process. The simulation results show good agreement with the measurement results obtained in a previous contribution, demonstrating the possibility of enhancing sparse training data sets with synthetic data.

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Detection of explosives in dustbins using deep transfer learning based multiclass classifiers

Gyasi-Agyei

2024

Appl Intell

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Application of Polarimetric Features and Support Vector Machines for Classification of Improvised Explosive Devices

Cited by 12 publications

References 28 publications

Hardware Suitability of Complex Natural Resonances Extraction Algorithms in Backscattered Radar Signals

Hardware Suitability of Complex Natural Resonances Extraction Algorithms in Backscattered Radar Signals

A Radar Echo Simulator for the Synthesis of Randomized Training Data Sets in the Context of AI-Based Applications

Detection of explosives in dustbins using deep transfer learning based multiclass classifiers

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