Research on Registration Methods for Coupled Errors in Maneuvering Platforms

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Vydáno v:Entropy vol. 27, no. 6 (2025), p. 607-622
Hlavní autor: Li, Qiang
Další autoři: Liu, Ruidong, Liu Yalei, Wei Zhenzhong
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MDPI AG
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100 1 |a Li, Qiang  |u School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing 100083, China; zhenzhongwei@buaa.edu.cn 
245 1 |a Research on Registration Methods for Coupled Errors in Maneuvering Platforms 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a The performance limitations of single-sensor systems in target tracking have led to the widespread adoption of multi-sensor fusion, which improves accuracy through information complementarity and redundancy. However, on mobile platforms, dynamic changes in sensor attitude and position introduce coupled measurement and attitude errors, making accurate sensor registration particularly challenging. Most existing methods either treat these errors independently or rely on simplified assumptions, which limit their effectiveness in dynamic environments. To address this, we propose a novel joint error estimation and registration method based on a pseudo-Kalman filter (PKF). The PKF constructs pseudo-measurements by subtracting outputs from multiple sensors, projecting them into a bias space that is independent of the target’s state. A decoupling mechanism is introduced to distinguish between measurement and attitude error components, enabling accurate joint estimation in real time. In the shipborne environment, simulation experiments on pitch, yaw, and roll motions were conducted using two sensors. This method was compared with least squares (LS), maximum likelihood (ML), and the standard method based on PKF. The results show that the method based on PKF has a lower root mean square error (RMSE), a faster convergence speed, and better estimation accuracy and robustness. The proposed approach provides a practical and scalable solution for sensor registration in dynamic environments, particularly in maritime or aerial applications where coupled errors are prevalent. 
653 |a Position measurement 
653 |a Rolling motion 
653 |a Accuracy 
653 |a Position sensing 
653 |a Sensors 
653 |a Attitude (inclination) 
653 |a Decoupling 
653 |a Root-mean-square errors 
653 |a Pitch (inclination) 
653 |a Noise 
653 |a Error analysis 
653 |a Registration 
653 |a Batch processing 
653 |a Algorithms 
653 |a Tracking 
653 |a Kalman filters 
653 |a Multisensor fusion 
653 |a Redundancy 
700 1 |a Liu, Ruidong  |u School of Artificial Intelligence, Henan University, Zhengzhou 450046, China; loner_d123@163.com (R.L.); liuyl3362@163.com (Y.L.) 
700 1 |a Liu Yalei  |u School of Artificial Intelligence, Henan University, Zhengzhou 450046, China; loner_d123@163.com (R.L.); liuyl3362@163.com (Y.L.) 
700 1 |a Wei Zhenzhong  |u School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing 100083, China; zhenzhongwei@buaa.edu.cn 
773 0 |t Entropy  |g vol. 27, no. 6 (2025), p. 607-622 
786 0 |d ProQuest  |t Engineering Database 
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