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<p>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).</p>

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Adaptive filtering strategy for maneuvering target tracking with two angle-only sensors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月23日

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  • Hengyu Liang,
  • Jinjie Huang,
  • Heshan Lei,
  • Jiachen Zhang

摘要

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).