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Paper Abstract and Keywords
Presentation 2025-12-08 09:25
Optimizing Multilateration Receiver Placement in a 3D Airport Environment Model via Genetic Algorithm with Local Search
Shuntaro Arao, Hiroki Komiyama, Ryoya Tsuruga, Takeshi Amishima (Meiji Univ.), Yasuyuki Kakubari, Junichi Naganawa (ENRI) SANE2025-81
Abstract (in Japanese) (See Japanese page) 
(in English) In airport-surface multilateration (MLAT), aircraft position is estimated from time-difference-of-arrival (TDOA) measurements at multiple receivers; however, building-induced obstruction is a major source of positioning error, so receiver placement must explicitly account for this effect. This paper proposes a method that selects receiver placements by combining a genetic algorithm (GA) with local search, while evaluating Distance Root Mean Square (DRMS) via line-of-sight tests on a 3-D obstacle model of Sendai Airport and the Cramér–Rao Lower Bound (CRLB). Numerical simulations show that the proposed method achieves 100% positionable coverage in all runs and uniformly reduces both the spatial 95th-percentile metric $P_{0.95}$ and the 95% conditional mean $mu_{0.95}$ relative to the current deployment. Furthermore, $P_{0.95}$ remains below $4.90~mathrm{m}$ in every run—a conservative threshold obtained from the ED-117A requirement $R_{95}=12~mathrm{m}$ via $chi^2_{2,0.95}$—demonstrating the effectiveness of the optimization in realistic, occlusion-rich airport environments. Additionally, statistical analysis confirmed a consistent preference for candidate stations with high LOS coverage and favorable geometry.
Keyword (in Japanese) (See Japanese page) 
(in English) TDOA / Multilateration / Genetic Algorithm / Local Search / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 280, SANE2025-81, pp. 1-6, Dec. 2025.
Paper # SANE2025-81 
Date of Issue 2025-12-01 (SANE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee SANE  
Conference Date 2025-12-08 - 2025-12-08 
Place (in Japanese) (See Japanese page) 
Place (in English) Meiji University Surugadai Campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Radar image processing, Signal processing, EM measurement, Machine learning and general 
Paper Information
Registration To SANE 
Conference Code 2025-12-SANE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Optimizing Multilateration Receiver Placement in a 3D Airport Environment Model via Genetic Algorithm with Local Search 
Sub Title (in English)  
Keyword(1) TDOA  
Keyword(2) Multilateration  
Keyword(3) Genetic Algorithm  
Keyword(4) Local Search  
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1st Author's Name Shuntaro Arao  
1st Author's Affiliation Meiji University (Meiji Univ.)
2nd Author's Name Hiroki Komiyama  
2nd Author's Affiliation Meiji University (Meiji Univ.)
3rd Author's Name Ryoya Tsuruga  
3rd Author's Affiliation Meiji University (Meiji Univ.)
4th Author's Name Takeshi Amishima  
4th Author's Affiliation Meiji University (Meiji Univ.)
5th Author's Name Yasuyuki Kakubari  
5th Author's Affiliation Electronic Navigation Research Institute (ENRI)
6th Author's Name Junichi Naganawa  
6th Author's Affiliation Electronic Navigation Research Institute (ENRI)
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Speaker Author-1 
Date Time 2025-12-08 09:25:00 
Presentation Time 20 minutes 
Registration for SANE 
Paper # SANE2025-81 
Volume (vol) vol.125 
Number (no) no.280 
Page pp.1-6 
#Pages 6 
Date of Issue 2025-12-01 (SANE) 

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