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<p>This paper develops heterogeneous distributed versions of the extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF) within a distributed feedback information filtering framework. These algorithms are applied for three-dimensional ballistic target trajectory prediction and multi-sensor data fusion utilizing radar, infrared, and laser sensors. We establish a discrete-time nonlinear dynamic model for a ballistic target and analyze the unique characteristics of measurements from the various sensors. Based on the distributed feedback information fusion structure, we introduce three novel algorithms: the heterogeneous distributed extended information filter, the heterogeneous distributed unscented information filter, and the heterogeneous distributed cubature information filter. Simulation results confirm that these algorithms significantly improve filtering accuracy compared to traditional single-sensor methods, highlighting their practical utility in multi-sensor data fusion applications.</p>

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Multi-sensor Heterogeneous Data Distributed Fusion for Ballistic Target Real-time Tracking in Three-dimensional Space

  • 期刊论文

  • 发表日期：

    2025年5月14日

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  • Fa Chen,
  • Lu Chen,
  • Jian-an Fang

摘要

This paper develops heterogeneous distributed versions of the extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF) within a distributed feedback information filtering framework. These algorithms are applied for three-dimensional ballistic target trajectory prediction and multi-sensor data fusion utilizing radar, infrared, and laser sensors. We establish a discrete-time nonlinear dynamic model for a ballistic target and analyze the unique characteristics of measurements from the various sensors. Based on the distributed feedback information fusion structure, we introduce three novel algorithms: the heterogeneous distributed extended information filter, the heterogeneous distributed unscented information filter, and the heterogeneous distributed cubature information filter. Simulation results confirm that these algorithms significantly improve filtering accuracy compared to traditional single-sensor methods, highlighting their practical utility in multi-sensor data fusion applications.