Realistic simulation of GPR for landmine and IED detection including antenna models, soil dispersion and heterogeneity

Stadler, Sam; Schennen, Stephan; Hiller, Thomas; Igel, Jan

doi:10.1002/nsg.12282

Cited by 2 publications

(2 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of robots [10] and handheld sensors [11] is particularly important here. In the context of humanitarian demining, a landmine leads to specific spatial reflection signatures in the GPR raw data, as shown in [12][13][14]. A system that applies a similar measurement method is ground-penetrating synthetic aperture radar (GPSAR), which uses a more advanced signal processing procedure following a synthetic aperture radar (SAR) approach.…”

Section: Introductionmentioning

confidence: 99%

“…Here, it was shown that the trained least squares support vector machine (LS-SVM) was capable of transferring the learned model to other measurement environments by only using a rather limited amount of new training data. In [13], a total of five different landmines were modeled. The IEDs were modeled, for instance, as plastic canisters filled with the explosive material.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

Synthetic Imaging Radar Data Generation in Various Clutter Environments Using Novel UWB Log-Periodic Antenna

Trivedi,

Phartiyal,

Kumar

et al. 2024

Sensors

View full text Add to dashboard Cite

In short-range microwave imaging, the collection of data in real environments for the purpose of developing techniques for target detection is very cumbersome. Simultaneously, to develop effective and efficient AI/ML-based techniques for target detection, a sufficiently large dataset is required. Therefore, to complement labor-intensive and tedious experimental data collected in a real cluttered environment, synthetic data generation via cost-efficient electromagnetic wave propagation simulations is explored in this article. To obtain realistic synthetic data, a 3-D model of an antenna, instead of a point source, is used to include the coupling effects between the antenna and the environment. A novel printed scalable ultra-wide band (UWB) log-periodic antenna with a tapered feed line is designed and incorporated in simulation models. The proposed antenna has a highly directional radiation pattern with considerable high gain (more than 6 dBi) on the entire bandwidth. Synthetic data are generated for two different applications, namely through-the-wall imaging (TWI) and through-the-foliage imaging (TFI). After the generation of synthetic data, clutter removal techniques are also explored, and results are analyzed in different scenarios. Post-analysis shows evidence that the proposed UWB log-periodic antenna-based synthetic imagery is suitable for use as an alternative dataset for TWI and TFI application development, especially in training machine learning models.

show abstract

Realistic simulation of GPR for landmine and IED detection including antenna models, soil dispersion and heterogeneity

Cited by 2 publications

References 63 publications

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

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

Synthetic Imaging Radar Data Generation in Various Clutter Environments Using Novel UWB Log-Periodic Antenna

Contact Info

Product

Resources

About