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 |
|---|---|
| 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 |
|---|---|
| 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.