Atmospheric attenuation reduction through multisensor fusion

1998 ◽  
Author(s):  
Lynne L. Grewe ◽  
Richard R. Brooks
Keyword(s):  
Multisensor Fusion ◽  
Atmospheric Attenuation

Multisensor Fusion Processing for Enhanced Radar Imaging

2000 ◽  
Author(s):  
Laura D. Vann ◽  
Kevin M. Cuomo ◽  
Jean E. Piou ◽  
Joseph T. Mayhan
Keyword(s):  
Radar Imaging ◽  
Multisensor Fusion

Multimodal Sensor Fusion for Indoor Occupancy Determination

2015 ◽  
Vol 764-765 ◽  
pp. 1319-1323
Author(s):  
Rong Shue Hsiao ◽  
Ding Bing Lin ◽  
Hsin Piao Lin ◽  
Jin Wang Zhou
Keyword(s):  
Bayesian Networks ◽  
Sensor Fusion ◽  
Dynamic Bayesian Networks ◽  
Sensor Nodes ◽  
Detection Accuracy ◽  
Multisensor Fusion ◽  
Fusion Algorithm ◽  
Improve Energy Efficiency ◽  
Occupancy Detection ◽  
Automation Systems

Pyroelectric infrared (PIR) sensors can detect the presence of human without the need to carry any device, which are widely used for human presence detection in home/office automation systems in order to improve energy efficiency. However, PIR detection is based on the movement of occupants. For occupancy detection, PIR sensors have inherent limitation when occupants remain relatively still. Multisensor fusion technology takes advantage of redundant, complementary, or more timely information from different modal sensors, which is considered an effective approach for solving the uncertainty and unreliability problems of sensing. In this paper, we proposed a simple multimodal sensor fusion algorithm, which is very suitable to be manipulated by the sensor nodes of wireless sensor networks. The inference algorithm was evaluated for the sensor detection accuracy and compared to the multisensor fusion using dynamic Bayesian networks. The experimental results showed that a detection accuracy of 97% in room occupancy can be achieved. The accuracy of occupancy detection is very close to that of the dynamic Bayesian networks.

Sign in / Sign up

Export Citation Format

Share Document