Change detection using an iterative algorithm with guarantees
Multiple domains involve systems with abruptly changing states, that result in signals with signatures that are corrupted by noise and sensor dynamics. In many applications, prior information on the magnitudes and timing distributions are unknown, compounding the difficulty of filtering such signals...
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| Veröffentlicht in: | Automatica (Oxford) Jg. 136; S. 110075 |
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| Abstract | Multiple domains involve systems with abruptly changing states, that result in signals with signatures that are corrupted by noise and sensor dynamics. In many applications, prior information on the magnitudes and timing distributions are unknown, compounding the difficulty of filtering such signals. Several non-linear filtering techniques are available with varying efficacies, however only a few offer theoretical guarantees. Recent research has led to a heuristic, iterative, non-linear filtering technique that is found to be effective over a range of applications. In this article, we present this iterative algorithm that learns the distribution of the event magnitude and provides an estimate of when an event has occurred. Here, every iteration involves two stages: the first one providing an estimate of the true signal that uses a prior on the distribution of event magnitudes, followed by the second stage where a distribution of the event magnitudes is computed from an estimate of the true signal. It is shown that with every iterate, the performance of the algorithm only improves and convergence of the posterior probability is established. Our comparative tests show that our algorithm provides significant performance improvements, especially for high bandwidth applications where sensor dynamics cannot be neglected. In addition, we provide a python-based implementation of the algorithm and provide a comprehensive comparison with existing methods in practice. |
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| AbstractList | Multiple domains involve systems with abruptly changing states, that result in signals with signatures that are corrupted by noise and sensor dynamics. In many applications, prior information on the magnitudes and timing distributions are unknown, compounding the difficulty of filtering such signals. Several non-linear filtering techniques are available with varying efficacies, however only a few offer theoretical guarantees. Recent research has led to a heuristic, iterative, non-linear filtering technique that is found to be effective over a range of applications. In this article, we present this iterative algorithm that learns the distribution of the event magnitude and provides an estimate of when an event has occurred. Here, every iteration involves two stages: the first one providing an estimate of the true signal that uses a prior on the distribution of event magnitudes, followed by the second stage where a distribution of the event magnitudes is computed from an estimate of the true signal. It is shown that with every iterate, the performance of the algorithm only improves and convergence of the posterior probability is established. Our comparative tests show that our algorithm provides significant performance improvements, especially for high bandwidth applications where sensor dynamics cannot be neglected. In addition, we provide a python-based implementation of the algorithm and provide a comprehensive comparison with existing methods in practice. |
| ArticleNumber | 110075 |
| Author | Melbourne, James Rajaganapathy, Sivaraman Salapaka, Murti V. |
| Author_xml | – sequence: 1 givenname: Sivaraman surname: Rajaganapathy fullname: Rajaganapathy, Sivaraman email: sivrmn@umn.edu – sequence: 2 givenname: James surname: Melbourne fullname: Melbourne, James email: james.melbourne@cimat.mx – sequence: 3 givenname: Murti V. surname: Salapaka fullname: Salapaka, Murti V. email: murtis@umn.edu |
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| Cites_doi | 10.1198/016214507000000860 10.1016/0167-2789(92)90242-F 10.1080/01621459.2012.737745 10.1529/biophysj.107.110601 10.1002/joc.924 10.1214/14-AOS1245 10.1101/cshperspect.a021931 10.1038/365721a0 10.3233/JAD-170094 10.1111/boc.201700061 10.1109/18.761341 10.1038/nature04928 10.1093/biostatistics/kxh008 10.23919/ACC.2018.8431534 10.1038/onc.2017.406 10.1109/TPAMI.1986.4767851 10.2307/1427090 10.1007/s12195-011-0188-5 10.1016/j.bpj.2010.09.067 10.1109/TIP.2004.838698 10.1038/nature03528 10.1016/0160-9327(93)90069-F 10.1038/nnano.2014.334 10.2307/2333258 10.1126/science.1243472 10.1016/j.sigpro.2019.107299 10.1098/rspa.2010.0671 10.1038/nmeth.3107 10.1038/41118 10.1016/j.bpj.2010.09.066 |
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| Snippet | Multiple domains involve systems with abruptly changing states, that result in signals with signatures that are corrupted by noise and sensor dynamics. In many... |
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| SubjectTerms | Iterative improvements Piece-wise constant signals Step detection algorithm |
| Title | Change detection using an iterative algorithm with guarantees |
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