{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T10:12:49Z","timestamp":1768731169362,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T00:00:00Z","timestamp":1628035200000},"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>In problems of parameter estimation from sensor data, the Fisher information provides a measure of the performance of the sensor; effectively, in an infinitesimal sense, how much information about the parameters can be obtained from the measurements. From the geometric viewpoint, it is a Riemannian metric on the manifold of parameters of the observed system. In this paper, we consider the case of parameterized sensors and answer the question, \u201cHow best to reconfigure a sensor (vary the parameters of the sensor) to optimize the information collected?\u201d A change in the sensor parameters results in a corresponding change to the metric. We show that the change in information due to reconfiguration exactly corresponds to the natural metric on the infinite-dimensional space of Riemannian metrics on the parameter manifold, restricted to finite-dimensional sub-manifold determined by the sensor parameters. The distance measure on this configuration manifold is shown to provide optimal, dynamic sensor reconfiguration based on an information criterion. Geodesics on the configuration manifold are shown to optimize the information gain but only if the change is made at a certain rate. An example of configuring two bearings-only sensors to optimally locate a target is developed in detail to illustrate the mathematical machinery, with Fast Marching methods employed to efficiently calculate the geodesics and illustrate the practicality of using this approach.<\/jats:p>","DOI":"10.3390\/s21165265","type":"journal-article","created":{"date-parts":[[2021,8,4]],"date-time":"2021-08-04T08:47:52Z","timestamp":1628066872000},"page":"5265","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The Information Geometry of Sensor Configuration"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7521-3874","authenticated-orcid":false,"given":"Simon","family":"Williams","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC 3000, Australia"}]},{"given":"Arthur George","family":"Suvorov","sequence":"additional","affiliation":[{"name":"Department of Theoretical Astrophysics, Eberhard Karls University of T\u00fcbingen, D-72076 T\u00fcbingen, Germany"},{"name":"Manly Astrophysics, 15\/41-42 East Esplanade, Manly, NSW 2095, Australia"}]},{"given":"Zengfu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Bill","family":"Moran","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, VIC 3000, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1109\/18.259642","article-title":"Information theory and radar waveform design","volume":"39","author":"Bell","year":"1993","journal-title":"IEEE Trans. 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