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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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Sensors and Materials
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 38, Number 2(2) (2026)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 819-839
S&M4350 Research paper https://doi.org/10.18494/SAM5875 Published: February 12, 2026 Convolutional Bidirectional Long Short-Term Memory-based Dynamic Noise Adaptive Distributed Collaborative Maneuvering Target Tracking [PDF] Xie Kun, Cai ChengLin, Lv KaiHui, and Pan Jundao (Received August 8, 2025; Accepted October 28, 2025) Keywords: wireless networks, convolutional bidirectional long short-term memory neural network, mobile target tracking, distributed collaboration
A distributed cooperative target tracking algorithm based on a convolutional bidirectional long short-term memory (ConvBiLSTM) neural network is proposed to address the nonnegligible and changing observation noise caused by previous passive maneuvering target tracking. The algorithm improves the time difference of arrival/frequency difference of arrival (TDOA/FDOA) measurement accuracy by utilizing the ConvBiLSTM neural network to correct the sensor observations to adapt to the dynamically changing observation environment, as well as combining with the weighted two-step least squares method to reduce the initial estimation error. The experimental results show that the algorithm can be used to estimate the target position and velocity more accurately in maneuvering target tracking environments with large observation errors, and at the same time, improves the stability of target tracking.
Corresponding author: Cai ChengLin  This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Xie Kun, Cai ChengLin, Lv KaiHui, and Pan Jundao, Convolutional Bidirectional Long Short-Term Memory-based Dynamic Noise Adaptive Distributed Collaborative Maneuvering Target Tracking, Sens. Mater., Vol. 38, No. 2, 2026, p. 819-839. |
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