{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T18:57:08Z","timestamp":1776452228885,"version":"3.51.2"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T00:00:00Z","timestamp":1615334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Attitude estimation is the process of computing the orientation angles of an object with respect to a fixed frame of reference. Gyroscope, accelerometer, and magnetometer are some of the fundamental sensors used in attitude estimation. The orientation angles computed from these sensors are combined using the sensor fusion methodologies to obtain accurate estimates. The complementary filter is one of the widely adopted techniques whose performance is highly dependent on the appropriate selection of its gain parameters. This paper presents a novel cascaded architecture of the complementary filter that employs a nonlinear and linear version of the complementary filter within one framework. The nonlinear version is used to correct the gyroscope bias, while the linear version estimates the attitude angle. The significant advantage of the proposed architecture is its independence of the filter parameters, thereby avoiding tuning the filter\u2019s gain parameters. The proposed architecture does not require any mathematical modeling of the system and is computationally inexpensive. The proposed methodology is applied to the real-world datasets, and the estimation results were found to be promising compared to the other state-of-the-art algorithms.<\/jats:p>","DOI":"10.3390\/s21061937","type":"journal-article","created":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T20:51:42Z","timestamp":1615409502000},"page":"1937","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Cascaded Complementary Filter Architecture for Sensor Fusion in Attitude Estimation"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1836-4438","authenticated-orcid":false,"given":"Parag","family":"Narkhede","sequence":"first","affiliation":[{"name":"Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9661-8224","authenticated-orcid":false,"given":"Shashi","family":"Poddar","sequence":"additional","affiliation":[{"name":"CSIR-Central Scientific Instruments Organisation, Chandigarh 160030, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1745-5231","authenticated-orcid":false,"given":"Rahee","family":"Walambe","sequence":"additional","affiliation":[{"name":"Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune 412115, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2578-5580","authenticated-orcid":false,"given":"George","family":"Ghinea","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Engineering, Brunel University, London UB8 3PH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2653-3780","authenticated-orcid":false,"given":"Ketan","family":"Kotecha","sequence":"additional","affiliation":[{"name":"Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune 412115, India"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/JSEN.2019.2940612","article-title":"Upper Body Pose Estimation Using Wearable Inertial Sensors and Multiplicative Kalman Filter","volume":"20","author":"Baldi","year":"2019","journal-title":"IEEE Sens. 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