Cognitive foundations for model-based sensor fusion

2003 ◽  
Author(s):  
Leonid I. Perlovsky ◽  
Bertus Weijers ◽  
Chris W. Mutz
Keyword(s):  
Sensor Fusion ◽  
Model Based ◽  
Cognitive Foundations

scholarly journals A Geometric Model based 2D LiDAR/Radar Sensor Fusion for Tracking Surrounding Vehicles

2019 ◽  
Vol 52 (8) ◽  
pp. 130-135 ◽  
Author(s):  
Hojoon Lee ◽  
Heungseok Chae ◽  
Kyongsu Yi
Keyword(s):  
Sensor Fusion ◽  
Geometric Model ◽  
Radar Sensor ◽  
Model Based

Model-based sensor fusion for aviation

1997 ◽  
Author(s):  
Misha Pavel ◽  
Ravi K. Sharma
Keyword(s):  
Sensor Fusion ◽  
Model Based

Model-based sensor fusion

1992 ◽  
Author(s):  
Leonid I. Perlovsky
Keyword(s):  
Sensor Fusion ◽  
Model Based

Nonlinear Averaging of Multi-Sensor Data

2003 ◽  
Author(s):  
Sugathevan Suranthiran ◽  
Suhada Jayasuriya
Keyword(s):  
Prior Knowledge ◽  
Sensor Fusion ◽  
Kalman Filtering ◽  
Sensor Data ◽  
Simulation Data ◽  
Model Based ◽  
Multiple Sensor ◽  
Multiple Measurements ◽  
Reliable Solution ◽  
Estimation Models

Considered in this paper is a framework for combining multiple sensor data to obtain a single inference. The task of fusing multi-sensor data is very challenging when no information about the sensor or estimation models is available. Kalman Filtering and other model-based techniques cannot be used to obtain a reliable inference. Linear Averaging of data is probably the simplest technique available, however, there is no guarantee that the fused measurement is, in fact, the best estimation. The problem will be worsened if one or more sensor measurements are faulty. In this paper, we analyze this problem and propose an effective multi-sensor fusion methodology. It is shown that a reliable solution can be obtained by nonlinearly averaging the multiple measurements. The proposed technique is well suited to identify outliers in the sensor measurements as well as to detect faulty sensor measurements. The developed algorithm is versatile in the sense that prior knowledge or information about sensors can be easily incorporated to improve the accuracy further. Illustrative examples and simulation data are presented to validate the proposed scheme.

scholarly journals Model-based sensor fusion and filtering for localization of a semi-autonomous robotic vehicle*

2021 ◽  
Author(s):  
Catalin Stefan Teodorescu ◽  
Irving Caplan ◽  
Harry Eberle ◽  
Tom Carlson
Keyword(s):  
Sensor Fusion ◽  
Model Based ◽  
Robotic Vehicle
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