Quaternion estimator algorithm: Revision history

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curprev 03:2403:24, 22 July 2024 OAbot talk contribs m 12,017 bytes +32 Open access bot: hdl updated in citation with #oabot. undo

curprev 21:4921:49, 11 August 2023 97.113.187.176 talk 11,985 bytes +10 Adding the word "conjugate" to clarify for people who are not as familiar with the * notation in the domain of conjugates. undo

curprev 04:3604:36, 2 July 2022 SunDawn talk contribs 11,975 bytes +57 Importing Wikidata short description: "Algorithm to solve Wahba's problem" undo Tag: Shortdesc helper

curprev 03:1903:19, 5 March 2022 Citation bot talk contribs 11,918 bytes +338 Alter: journal. Add: url, s2cid, bibcode, doi. | Use this bot. Report bugs. | Suggested by Headbomb | Linked from Wikipedia:WikiProject_Academic_Journals/Journals_cited_by_Wikipedia/Sandbox | #UCB_webform_linked 300/387 undo

curprev 21:3421:34, 28 February 2022 Tino talk contribs 11,580 bytes 0 Rename vector Q to v for consistency (to have all vectors denoted with lowercase letters, and all matrices denoted with uppercase) undo
curprev 21:2421:24, 28 February 2022 Tino talk contribs 11,580 bytes −8 fix typo: lambda is the (scalar valued) maximum value of the loss, not the (matrix valued) argument of the maximum undo

curprev 21:0421:04, 27 February 2022 Tino talk contribs 11,588 bytes +1,837 fix bilinear form -> quadratic form, brief comparison with other methods, more applications undo
curprev 17:0817:08, 27 February 2022 John B123 talk contribs 9,751 bytes +35 Added tags to the page using Page Curation (one source) undo Tag: PageTriage

curprev 17:5317:53, 26 February 2022 Tino talk contribs 9,716 bytes −1 title undo
curprev 17:5317:53, 26 February 2022 Tino talk contribs m 9,717 bytes 0 Tino moved page Quaternion estimation algorithm to Quaternion estimator algorithm: accurate name undo
curprev 17:5117:51, 26 February 2022 Tino talk contribs 9,717 bytes +9,717 ←Created page with 'The '''quaternion estimation algorithm''' ('''QUEST''') is an algorithm designed to solve Wahba's problem, that consists of finding a rotation matrix between two coordinate systems from two sets of observations sampled in each system respectively. The key idea behind the algorithm is to find an expression of the loss function for the Wahba's problem as a bilinear form, using the Cayley–Hamilton theorem and the Newton's method|Newton–Ra...'