Covariance Square Root Second-Order Mapping
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918486962339840 |
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| author | LeGrand, Keith A. Hastings, Braden Kulik, Jackson |
| author_facet | LeGrand, Keith A. Hastings, Braden Kulik, Jackson |
| contents | In recursive state estimation, numerical error can play a major role in an algorithm's overall performance and reliability. Roundoff errors due to finite precision arithmetic can violate theoretical guarantees, leading to asymmetric and non-positive-semidefinite covariance matrices. In algorithms employing first-order covariance mappings, such as the extended Kalman filter, these issues have been mitigated by employing square root factorizations of the covariance matrix. However, existing techniques do not directly extend to higher-order moment mappings, which show great value in highly nonlinear settings. This paper presents the first known square root computation of the second-order covariance mapping. The square root computation is not only more accurate, as is shown in two distinct numerical experiments, but generally requires fewer floating point operations compared to the full covariance matrix computation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_05560 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Covariance Square Root Second-Order Mapping LeGrand, Keith A. Hastings, Braden Kulik, Jackson Signal Processing In recursive state estimation, numerical error can play a major role in an algorithm's overall performance and reliability. Roundoff errors due to finite precision arithmetic can violate theoretical guarantees, leading to asymmetric and non-positive-semidefinite covariance matrices. In algorithms employing first-order covariance mappings, such as the extended Kalman filter, these issues have been mitigated by employing square root factorizations of the covariance matrix. However, existing techniques do not directly extend to higher-order moment mappings, which show great value in highly nonlinear settings. This paper presents the first known square root computation of the second-order covariance mapping. The square root computation is not only more accurate, as is shown in two distinct numerical experiments, but generally requires fewer floating point operations compared to the full covariance matrix computation. |
| title | Covariance Square Root Second-Order Mapping |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.05560 |