Adaptive filtering strategy for maneuvering target tracking with two angle-only sensors
- 1School of Computer Science and Technology, Harbin University of Science and Technology
Abstract
Angle-only tracking (AOT) has emerged as a compelling paradigm in state estimation and has been widely applied in various scenarios, such as passive surveillance and target localization. However, single-sensor AOT suffers from several limitations, including weak observability, strong nonlinearity, and significant performance degradation due to target maneuvering. This study proposes an adaptive extended Kalman filter (AEKF) with two angle-only sensors. The proposed normalized innovation squared (NIS)-driven adaptive strategy is designed to monitor and adjust the process model in real time. A theoretical error bound analysis is provided to guarantee that state estimation is performed within a certain accuracy bound. Using a simulation scenario involving two unmanned aerial vehicles (UAVs), it is shown that the proposed AEKF achieves higher tracking accuracy than existing methods, namely the strong tracking extended Kalman filter (STEKF) and the unbiased minimum-variance extended Kalman filter (UMVEKF).
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BibTeX
@article{liang2026adaptive,
title = {{Adaptive filtering strategy for maneuvering target tracking with two angle-only sensors}},
author = {Hengyu Liang and Jinjie Huang and Heshan Lei and Jiachen Zhang},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-63361-x},
url = {https://doi.org/10.1038/s41598-026-63361-x},
note = {CC BY},
}
RIS
TY - JOUR
TI - Adaptive filtering strategy for maneuvering target tracking with two angle-only sensors
AU - Hengyu Liang
AU - Jinjie Huang
AU - Heshan Lei
AU - Jiachen Zhang
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-63361-x
UR - https://doi.org/10.1038/s41598-026-63361-x
AB - Angle-only tracking (AOT) has emerged as a compelling paradigm in state estimation and has been widely applied in various scenarios, such as passive surveillance and target localization. However, single-sensor AOT suffers from several limitations, including weak observability, strong nonlinearity, and significant performance degradation due to target maneuvering. This study proposes an adaptive extended Kalman filter (AEKF) with two angle-only sensors. The proposed normalized innovation squared (NIS)-driven adaptive strategy is designed to monitor and adjust the process model in real time. A theoretical error bound analysis is provided to guarantee that state estimation is performed within a certain accuracy bound. Using a simulation scenario involving two unmanned aerial vehicles (UAVs), it is shown that the proposed AEKF achieves higher tracking accuracy than existing methods, namely the strong tracking extended Kalman filter (STEKF) and the unbiased minimum-variance extended Kalman filter (UMVEKF).
SN - 2045-2322
ER -