A least mean square approach to buried object detection in ground penetrating radar

Eyyup Temlioglu; Isin Erer; Deniz Kumlu

doi:10.1109/IGARSS.2017.8128084

A least mean square approach to buried object detection in ground penetrating radar

Eyyup Temlioglu, Isin Erer, Deniz Kumlu

Department of Electronics and Communication Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

Abstract

Ground Penetrating Radar (GPR) is one of the most popular subsurface sensing devices and has a wide range of applications, e.g., buried object detection. In this study, Least Mean Square (LMS) approach is used to solve buried object detection problem. Point of interest located in each depth location of 2D GPR signal is estimated from previous samples by using separate 1D LMS algorithms and prediction errors defined as the difference between the measured and estimated values are aggregated. If calculated error exceeded a predefined threshold, it is decided that a buried object exists at that location. The proposed approach is tested with a realistic data set simulated by using a new version of gprMax electromagnetic modeling software. The data set consists of several different soil types, objects, different burial depths and surface types. Resulting Receiver Operating Characteristic (ROC) curves demonstrate the performance of the proposed method.

Original language	English
Title of host publication	2017 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publication	International Cooperation for Global Awareness, IGARSS 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4833-4836
Number of pages	4
ISBN (Electronic)	9781509049516
DOIs	https://doi.org/10.1109/IGARSS.2017.8128084
Publication status	Published - 1 Dec 2017
Event	37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 - Fort Worth, United States Duration: 23 Jul 2017 → 28 Jul 2017

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2017-July

Conference

Conference	37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Country/Territory	United States
City	Fort Worth
Period	23/07/17 → 28/07/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Anomaly detection
Buried object detection
GprMax
Ground penetrating radar (GPR)
Least mean square

Access to Document

10.1109/IGARSS.2017.8128084

Cite this

Temlioglu, E., Erer, I., & Kumlu, D. (2017). A least mean square approach to buried object detection in ground penetrating radar. In 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings (pp. 4833-4836). Article 8128084 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2017-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2017.8128084

Temlioglu, Eyyup ; Erer, Isin ; Kumlu, Deniz. / A least mean square approach to buried object detection in ground penetrating radar. 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 4833-4836 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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abstract = "Ground Penetrating Radar (GPR) is one of the most popular subsurface sensing devices and has a wide range of applications, e.g., buried object detection. In this study, Least Mean Square (LMS) approach is used to solve buried object detection problem. Point of interest located in each depth location of 2D GPR signal is estimated from previous samples by using separate 1D LMS algorithms and prediction errors defined as the difference between the measured and estimated values are aggregated. If calculated error exceeded a predefined threshold, it is decided that a buried object exists at that location. The proposed approach is tested with a realistic data set simulated by using a new version of gprMax electromagnetic modeling software. The data set consists of several different soil types, objects, different burial depths and surface types. Resulting Receiver Operating Characteristic (ROC) curves demonstrate the performance of the proposed method.",

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Temlioglu, E, Erer, I & Kumlu, D 2017, A least mean square approach to buried object detection in ground penetrating radar. in 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings., 8128084, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2017-July, Institute of Electrical and Electronics Engineers Inc., pp. 4833-4836, 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017, Fort Worth, United States, 23/07/17. https://doi.org/10.1109/IGARSS.2017.8128084

A least mean square approach to buried object detection in ground penetrating radar. / Temlioglu, Eyyup; Erer, Isin; Kumlu, Deniz.
2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 4833-4836 8128084 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2017-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Erer, Isin

AU - Kumlu, Deniz

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N2 - Ground Penetrating Radar (GPR) is one of the most popular subsurface sensing devices and has a wide range of applications, e.g., buried object detection. In this study, Least Mean Square (LMS) approach is used to solve buried object detection problem. Point of interest located in each depth location of 2D GPR signal is estimated from previous samples by using separate 1D LMS algorithms and prediction errors defined as the difference between the measured and estimated values are aggregated. If calculated error exceeded a predefined threshold, it is decided that a buried object exists at that location. The proposed approach is tested with a realistic data set simulated by using a new version of gprMax electromagnetic modeling software. The data set consists of several different soil types, objects, different burial depths and surface types. Resulting Receiver Operating Characteristic (ROC) curves demonstrate the performance of the proposed method.

AB - Ground Penetrating Radar (GPR) is one of the most popular subsurface sensing devices and has a wide range of applications, e.g., buried object detection. In this study, Least Mean Square (LMS) approach is used to solve buried object detection problem. Point of interest located in each depth location of 2D GPR signal is estimated from previous samples by using separate 1D LMS algorithms and prediction errors defined as the difference between the measured and estimated values are aggregated. If calculated error exceeded a predefined threshold, it is decided that a buried object exists at that location. The proposed approach is tested with a realistic data set simulated by using a new version of gprMax electromagnetic modeling software. The data set consists of several different soil types, objects, different burial depths and surface types. Resulting Receiver Operating Characteristic (ROC) curves demonstrate the performance of the proposed method.

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Temlioglu E, Erer I, Kumlu D. A least mean square approach to buried object detection in ground penetrating radar. In 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 4833-4836. 8128084. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2017.8128084

A least mean square approach to buried object detection in ground penetrating radar

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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