A Robust LPNN Technique for Target Localization Under Hybrid TOA/AOA Measurements

Muideen Adegoke; Andrew Chi Sing Leung; John Sum

doi:10.1007/978-3-030-04179-3_27

A Robust LPNN Technique for Target Localization Under Hybrid TOA/AOA Measurements

Muideen Adegoke, Andrew Chi Sing Leung, John Sum

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 12 - Chapter in an edited book (Author) › peer-review

Abstract

This paper presents an approach based on the Lagrange programming neural network (LPNN) framework for target localization under the outlier situation. The problem is formulated as a minimization problem of the mixture of l₂ and l₂ norms of the measurement errors with time of arrival (TOA) and angle of arrival (AOA) measurements. In our approach, we introduce an approximation function for the l₁ norm to avoid non-differentiable points of the l₁ norm. Moreover, we study the network stability of the proposed approach. We carry out simulations with various settings to validate the performance of the presented approach. In addition, the proposed method is compared with the existing linear and nonlinear target localization techniques. Based on the mean square error (MSE) values obtained under different settings, the proposed approach is superior to the other comparison algorithms.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	25th International Conference, ICONIP 2018, Proceedings
Editors	Long Cheng, Andrew Chi Sing Leung, Seiichi Ozawa
Publisher	Springer, Cham
Pages	308-320
Volume	2
ISBN (Electronic)	9783030041793
ISBN (Print)	9783030041786
DOIs	https://doi.org/10.1007/978-3-030-04179-3_27
Publication status	Published - Dec 2018
Event	25th International Conference on Neural Information Processing (ICONIP 2018) - Sokha Siem Reap Resort & Convention Center, Siem Reap, Cambodia Duration: 13 Dec 2018 → 16 Dec 2018 https://conference.cs.cityu.edu.hk/iconip/

Publication series

Name	Lecture Notes in Computer Science (including subseries Theoretical Computer Science and General Issues)
Publisher	Springer, Cham
Volume	11302
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Neural Information Processing (ICONIP 2018)
Abbreviated title	ICONIP 2018
Place	Cambodia
City	Siem Reap
Period	13/12/18 → 16/12/18
Internet address	https://conference.cs.cityu.edu.hk/iconip/

Research Keywords

Angle-of-arrival
LPNN
Outliers
Target localization
Time-of-arrival

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10.1007/978-3-030-04179-3_27

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Adegoke, M., Leung, A. C. S., & Sum, J. (2018). A Robust LPNN Technique for Target Localization Under Hybrid TOA/AOA Measurements. In L. Cheng, A. C. S. Leung, & S. Ozawa (Eds.), Neural Information Processing: 25th International Conference, ICONIP 2018, Proceedings (Vol. 2, pp. 308-320). (Lecture Notes in Computer Science (including subseries Theoretical Computer Science and General Issues); Vol. 11302). Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_27

Adegoke, Muideen ; Leung, Andrew Chi Sing ; Sum, John. / A Robust LPNN Technique for Target Localization Under Hybrid TOA/AOA Measurements. Neural Information Processing: 25th International Conference, ICONIP 2018, Proceedings. editor / Long Cheng ; Andrew Chi Sing Leung ; Seiichi Ozawa. Vol. 2 Springer, Cham, 2018. pp. 308-320 (Lecture Notes in Computer Science (including subseries Theoretical Computer Science and General Issues)).

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title = "A Robust LPNN Technique for Target Localization Under Hybrid TOA/AOA Measurements",

abstract = "This paper presents an approach based on the Lagrange programming neural network (LPNN) framework for target localization under the outlier situation. The problem is formulated as a minimization problem of the mixture of l2 and l2 norms of the measurement errors with time of arrival (TOA) and angle of arrival (AOA) measurements. In our approach, we introduce an approximation function for the l1 norm to avoid non-differentiable points of the l1 norm. Moreover, we study the network stability of the proposed approach. We carry out simulations with various settings to validate the performance of the presented approach. In addition, the proposed method is compared with the existing linear and nonlinear target localization techniques. Based on the mean square error (MSE) values obtained under different settings, the proposed approach is superior to the other comparison algorithms.",

keywords = "Angle-of-arrival, LPNN, Outliers, Target localization, Time-of-arrival",

author = "Muideen Adegoke and Leung, {Andrew Chi Sing} and John Sum",

year = "2018",

month = dec,

doi = "10.1007/978-3-030-04179-3_27",

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booktitle = "Neural Information Processing",

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Adegoke, M , Leung, ACS & Sum, J 2018, A Robust LPNN Technique for Target Localization Under Hybrid TOA/AOA Measurements. in L Cheng, ACS Leung & S Ozawa (eds), Neural Information Processing: 25th International Conference, ICONIP 2018, Proceedings. vol. 2, Lecture Notes in Computer Science (including subseries Theoretical Computer Science and General Issues), vol. 11302, Springer, Cham, pp. 308-320, 25th International Conference on Neural Information Processing (ICONIP 2018), Siem Reap, Cambodia, 13/12/18. https://doi.org/10.1007/978-3-030-04179-3_27

A Robust LPNN Technique for Target Localization Under Hybrid TOA/AOA Measurements. / Adegoke, Muideen ; Leung, Andrew Chi Sing; Sum, John.
Neural Information Processing: 25th International Conference, ICONIP 2018, Proceedings. ed. / Long Cheng; Andrew Chi Sing Leung; Seiichi Ozawa. Vol. 2 Springer, Cham, 2018. p. 308-320 (Lecture Notes in Computer Science (including subseries Theoretical Computer Science and General Issues); Vol. 11302).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 12 - Chapter in an edited book (Author) › peer-review

TY - CHAP

T1 - A Robust LPNN Technique for Target Localization Under Hybrid TOA/AOA Measurements

AU - Adegoke, Muideen

AU - Leung, Andrew Chi Sing

AU - Sum, John

PY - 2018/12

Y1 - 2018/12

N2 - This paper presents an approach based on the Lagrange programming neural network (LPNN) framework for target localization under the outlier situation. The problem is formulated as a minimization problem of the mixture of l2 and l2 norms of the measurement errors with time of arrival (TOA) and angle of arrival (AOA) measurements. In our approach, we introduce an approximation function for the l1 norm to avoid non-differentiable points of the l1 norm. Moreover, we study the network stability of the proposed approach. We carry out simulations with various settings to validate the performance of the presented approach. In addition, the proposed method is compared with the existing linear and nonlinear target localization techniques. Based on the mean square error (MSE) values obtained under different settings, the proposed approach is superior to the other comparison algorithms.

AB - This paper presents an approach based on the Lagrange programming neural network (LPNN) framework for target localization under the outlier situation. The problem is formulated as a minimization problem of the mixture of l2 and l2 norms of the measurement errors with time of arrival (TOA) and angle of arrival (AOA) measurements. In our approach, we introduce an approximation function for the l1 norm to avoid non-differentiable points of the l1 norm. Moreover, we study the network stability of the proposed approach. We carry out simulations with various settings to validate the performance of the presented approach. In addition, the proposed method is compared with the existing linear and nonlinear target localization techniques. Based on the mean square error (MSE) values obtained under different settings, the proposed approach is superior to the other comparison algorithms.

KW - Angle-of-arrival

KW - LPNN

KW - Outliers

KW - Target localization

KW - Time-of-arrival

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U2 - 10.1007/978-3-030-04179-3_27

DO - 10.1007/978-3-030-04179-3_27

M3 - RGC 12 - Chapter in an edited book (Author)

SN - 9783030041786

VL - 2

T3 - Lecture Notes in Computer Science (including subseries Theoretical Computer Science and General Issues)

SP - 308

EP - 320

BT - Neural Information Processing

A2 - Cheng, Long

A2 - Leung, Andrew Chi Sing

A2 - Ozawa, Seiichi

PB - Springer, Cham

T2 - 25th International Conference on Neural Information Processing (ICONIP 2018)

Y2 - 13 December 2018 through 16 December 2018

ER -

Adegoke M , Leung ACS, Sum J. A Robust LPNN Technique for Target Localization Under Hybrid TOA/AOA Measurements. In Cheng L, Leung ACS, Ozawa S, editors, Neural Information Processing: 25th International Conference, ICONIP 2018, Proceedings. Vol. 2. Springer, Cham. 2018. p. 308-320. (Lecture Notes in Computer Science (including subseries Theoretical Computer Science and General Issues)). Epub 2018 Nov 18. doi: 10.1007/978-3-030-04179-3_27

A Robust LPNN Technique for Target Localization Under Hybrid TOA/AOA Measurements

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