Abstract
The location awareness of a 'passive' target has drawn much attention in recent years. Several papers have investigated passive localization in different scenarios. In this paper, we build a factor graph framework and show that the factor graph can be modified easily for different passive localization problems. Then the beliefs of unknown variables are obtained via message passing algorithms. To reduce the huge complexity of nonparametric message passing, we propose a damped expectation propagation method which restricts the messages on factor graph to Gaussian distributions. Simulations results verify the feasibility of proposed algorithm.
Original language | English |
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Title of host publication | 2016 IEEE/CIC International Conference on Communications in China, ICCC 2016 |
Publisher | IEEE |
ISBN (Electronic) | 9781509021437 |
ISBN (Print) | 9781509021444 |
DOIs | |
Publication status | Published - 21 Oct 2016 |
Externally published | Yes |
Event | 2016 IEEE/CIC International Conference on Communications in China, ICCC 2016 - Chengdu, China Duration: 27 Jul 2016 → 29 Jul 2016 |
Conference
Conference | 2016 IEEE/CIC International Conference on Communications in China, ICCC 2016 |
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Country/Territory | China |
City | Chengdu |
Period | 27/07/16 → 29/07/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- expectation propagation
- factor graph
- message damping
- Passive localization