Sensor Selection via Maximizing Hybrid Bayesian Fisher Information and Mutual Information in Unreliable Sensor Networks
The sensor selection problem is addressed for unreliable sensor networks. The Bayesian Fisher information (BFI) matrix, mutual information (MI) and their relationship are investigated under Gaussian mixture noise conditions. To overcome the flaw that the sensor selection methods based on either BFI...
Main Authors: | Qingli Yan, Jianfeng Chen |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-02-01
|
Series: | Electronics |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-9292/9/2/283 |
Similar Items
-
Source Localization in Acoustic Sensor Networks via Constrained Least-Squares Optimization Using AOA and GROA Measurements
by: Ji-An Luo, et al.
Published: (2018-03-01) -
Indoor Localization Based on Infrared Angle of Arrival Sensor Network
by: Damir Arbula, et al.
Published: (2020-11-01) -
An SOCP Estimator for Hybrid RSS and AOA Target Localization in Sensor Networks
by: Marcelo Salgueiro Costa, et al.
Published: (2021-03-01) -
Construction of Optimal Trees for Maximizing Aggregation Information in Deadline- and Energy-Constrained Unreliable Wireless Sensor Networks
by: Yunquan Gao, et al.
Published: (2018-01-01) -
Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks
by: Anh Tuyen Le, et al.
Published: (2020-07-01)