| Paper ID | SAM-9.1 |
| Paper Title |
TARGET DETECTION FROM DISTRIBUTED PASSIVE SENSORS: SEMI-LABELED DATA QUANTIZATION |
| Authors |
Zachariah Sutton, Peter Willett, University of Connecticut, United States; Stefano Marano, University of Salerno, Italy |
| Session | SAM-9: Detection and Classification |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Sensor Array and Multichannel Signal Processing: [RAS-DTCL] Target detection, classification, localization |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Consider a test at a particular point in space for the existence of a point target using intensity measurements from passive sensors distributed uniformly around the test location. The distance from the test location of a particular sensor is relevant to the decision making, and is considered "labeling" on the sensor's intensity data. This work considers the case where both the intensity data and the label (distance) values are coarsely quantized to decrease communication cost. It will be shown that, for a given per-measurement communication budget, there exists an ideal quantization rule. The results provide a method for choosing between possible apportionments of the communication budget between data (intensity) and labeling (distance). |
