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Updated: Feb 9, 2026

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Improved Spatial Registration and Target Tracking Method for Sensors on Multiple Missiles.

Xiaodong Lu1, Yuting Xie2, Jun Zhou3

  • 1Institute of Precision Guidance and Control, Northwestern Polytechnical University, Xi'an 710072, China. luxiaodong@nwpu.edu.cn.

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|June 5, 2018
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Summary

This study introduces an improved Kalman Filter for precise spatial registration of 3-D sensors on dynamic platforms. The enhanced method improves accuracy and robustness in estimating missile registration errors and maneuvering target states.

Keywords:
3-D sensorsKalman Filter on Earth-Centered Earth-Fixed (ECEF-KF) coordinate algorithmPseudo Linear Kalman Filter (PLKF)Unscented Kalman Filter (UKF)error compensationspatial registrationtarget tracking

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Area of Science:

  • Aerospace Engineering
  • Guidance, Navigation, and Control
  • Signal Processing

Background:

  • Current spatial registration techniques are inadequate for 3-D sensors on high-dynamic platforms.
  • Accurate estimation of sensor measurement biases and attitude biases is critical for cooperative missile guidance and maneuvering target tracking.
  • Existing methods struggle with the complexities of high-dynamic environments.

Purpose of the Study:

  • To develop and validate an enhanced spatial registration method for 3-D sensors on high-dynamic platforms.
  • To accurately estimate registration errors and motion states of maneuvering targets.
  • To improve the robustness and accuracy of guidance systems in dynamic scenarios.

Main Methods:

  • An improved Kalman Filter on Earth-Centered Earth-Fixed (ECEF-KF) coordinate algorithm is proposed for estimating sensor measurement and attitude biases.
  • Pseudo Linear Kalman Filter (PLKF) and Unscented Kalman Filter (UKF) with modified inputs are employed for target tracking.
  • Position-judgement logic monitors filter convergence, and a low-pass filter is used to reduce estimation jitter before compensation.

Main Results:

  • The enhanced ECEF-KF algorithm significantly improves the accuracy and robustness of space alignment.
  • The conditional-compensation-based PLKF method demonstrates optimal performance in target tracking.
  • Simulations confirm the effectiveness of the proposed methods in handling high-dynamic platforms and maneuvering targets.

Conclusions:

  • The developed ECEF-KF enhancement provides a more accurate and robust solution for spatial registration challenges.
  • The conditional-compensation-based PLKF offers superior performance for maneuvering target tracking.
  • This research contributes to advancing guidance and control systems for dynamic platforms.