Exploiting the Momentary Dependence of Radar Observations for Non-Cooperative Target Recognition
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
Original language | English |
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Title of host publication | FUSION 2019 - 22nd International Conference on Information Fusion |
Publisher | IEEE |
ISBN (Electronic) | 9780996452786 |
Publication status | Published - 1 Jul 2019 |
Publication type | A4 Article in a conference publication |
Event | International Conference on Information Fusion - Ottawa, Canada Duration: 2 Jul 2019 → 5 Jul 2019 Conference number: 22nd |
Conference
Conference | International Conference on Information Fusion |
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Abbreviated title | FUSION |
Country | Canada |
City | Ottawa |
Period | 2/07/19 → 5/07/19 |
Abstract
Multiple radar sensors can be used in collaboration to detect targets in an area of surveillance. In this paper, we consider a case, in which a target is detected by a network of radars producing multiple observations of the radar signature of the target during a short time window. Given that this time window is sufficiently narrow, the observations have a dependence between them momentarily related to the change in the orientation of the target. We propose the fusion of these interdependent observations to aid target identification by forming a joint multi-dimensional histogram of the radar cross section (RCS). In addition, we investigate the criteria for windowing the observations to ensure adequate interdependence. We present a case study to demonstrate the ability of the proposed approach to distinguish between different targets using the measured RCS collected by a multi-radar surveillance system. Based on the experiment, we analyze the criteria for the dynamic windowing and discuss the computational requirements of the proposed concept.