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| 001 | 9.853771 |
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| 003 | CaOODSP |
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| 005 | 20221107154916 |
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| 007 | cr ||||||||||| |
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| 008 | 180316s2016 oncd fo f000 0 eng d |
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| 040 | |aCaOODSP|beng |
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| 043 | |an-cn--- |
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| 086 | 1 |aD69-54/2016E-PDF |
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| 245 | 00|aMultisensor-multitarget bearing-only sensor registration |h[electronic resource] / |cEhsan Taghavi ... [et. al.]. |
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| 260 | |a[Ottawa : |bDefence Research and Development Canada, Ottawa Research Centre, |c2016] |
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| 300 | |a22 p. : |bcharts |
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| 500 | |aCaption title. |
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| 504 | |aIncludes bibliographic references. |
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| 520 | 3 |aBearing–only estimation is one of the fundamental and challenging problems in target tracking. As in the case of radar tracking, the presence of offset or position biases can exacerbate the challenges in bearing–only estimation. Modeling various sensor biases is not a trivial task and not much has been done in the literature specifically for bearing–only tracking. This paper addresses the modeling of offset biases in bearing–only sensors and the ensuing multitarget tracking with bias compensation. Bias estimation is handled at the fusion node to which individual sensors report their local tracks in the form of associated measurement reports (AMR) or angle-only tracks. The modeling is based on a multisensor approach that can effectively handle a time–varying number of targets in the surveillance region. The proposed algorithm leads to a maximum likelihood bias estimator. The corresponding Cramer–Rao Lower Bound to quantify the theoretical accuracy that can be achieved by the proposed method or any other algorithm is also derived. Finally, simulation results on different distributed tracking scenarios are presented to demonstrate the capabilities of the proposed approach. In order to show that the proposed method can work even with false alarms and missed detections, simulation results on a centralized tracking scenario where the local sensors send all their measurements (not AMRs or local tracks) are also presented. |
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| 693 | 4|aBias estimation |
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| 693 | 4|aTarget motion analysis |
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| 693 | 4|aTriangulation |
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| 693 | 4|aFiltering |
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| 700 | 1 |aTaghavi, Ehsan. |
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| 710 | 1 |aCanada. |bDefence R&D Canada. |bOttawa Research Centre. |
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| 856 | 40|qPDF|s590 KB|uhttps://publications.gc.ca/collections/collection_2018/rddc-drdc/D69-54-2016-eng.pdf |
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