{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:22:22Z","timestamp":1760059342579,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T00:00:00Z","timestamp":1749168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation Project","award":["51625501","51622501"],"award-info":[{"award-number":["51625501","51622501"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/e27060607","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T11:08:31Z","timestamp":1749208111000},"page":"607","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on Registration Methods for Coupled Errors in Maneuvering Platforms"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1541-5391","authenticated-orcid":false,"given":"Qiang","family":"Li","sequence":"first","affiliation":[{"name":"School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing 100083, China"},{"name":"No. 27 Research Institute of CETC, Zhengzhou 450047, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruidong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Henan University, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yalei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Henan University, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenzhong","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"90999","DOI":"10.1109\/ACCESS.2023.3308152","article-title":"Adaptive decentralized sensor fusion for autonomous vehicle: Estimating the position of surrounding vehicles","volume":"11","author":"Yoon","year":"2023","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3871","DOI":"10.1109\/TIV.2023.3271624","article-title":"Multi-modal sensor fusion and object tracking for autonomous racing","volume":"8","author":"Karle","year":"2023","journal-title":"IEEE Trans. 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