Realistic simulations of surveillance sensors in an algorithm-level sensor fusion test-bed

1995 ◽  
Author(s):  
Eloi Bosse ◽  
Nicolas Duclos-Hindie ◽  
Jean Roy ◽  
Denis Dion, Jr.
Keyword(s):  
Sensor Fusion ◽  
Test Bed ◽  
Level Sensor ◽  
Algorithm Level

Feature-level sensor fusion

1999 ◽  
Author(s):  
Tamar Peli ◽  
Mon Young ◽  
Robert Knox ◽  
Kenneth K. Ellis ◽  
Frederick Bennett
Keyword(s):  
Sensor Fusion ◽  
Level Sensor

Feature Level Sensor Fusion for Passive RF and EO Information Integration

2020 ◽  
Author(s):  
Asad Vakil ◽  
Jenny Liu ◽  
Peter Zulch ◽  
Erik Blasch ◽  
Robert Ewing ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Information Integration ◽  
Level Sensor

Automated Sensor Selection and Fusion for Monitoring and Diagnostics of Plunge Grinding

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Niranjan Subrahmanya ◽  
Yung C. Shin
Keyword(s):  
Feature Selection ◽  
Test Bed ◽  
Wheel Wear ◽  
Widespread Application ◽  
Plunge Grinding ◽  
Level Sensor ◽  
Single Method ◽  
Model Training ◽  
Monitoring And Diagnostics ◽  
Leave One Out

This paper deals with the development of an online monitoring system based on feature-level sensor fusion and its application to OD plunge grinding. Different sensors are used to measure acoustic emission, spindle power, and workpiece vibration signals, which are used to monitor three of the most common faults in grinding—workpiece burn, chatter, and wheel wear. Although a number of methods have been reported in recent literature for monitoring these faults, they have not found widespread application in industry as no single method or feature has been shown to be successful for all setups and for all wheel-workpiece combinations. This paper proposes a systematic approach, which allows the development and deployment of process-monitoring systems via automated sensor and feature selection combined with parameter-free model training, both of which are especially crucial for implementation in industry. The proposed algorithm makes use of “sparsity-promoting” penalty terms to encourage sensor and feature selection while the “hyperparameters” of the algorithm are tuned using an approximation of the leave-one-out error. Experimental results obtained for monitoring burn, chatter, and wheel wear from a plunge grinding test bed show the effectiveness of the proposed method.

Multi-Level Sensor Fusion Algorithm Approach for BMD Interceptor Applications

1998 ◽  
Author(s):  
Doug Allen ◽  
Bart Smith ◽  
Norman Morris ◽  
Charles Bjork ◽  
John Rushing
Keyword(s):  
Sensor Fusion ◽  
Fusion Algorithm ◽  
Level Sensor ◽  
Multi Level

scholarly journals A Fault-Tolerant Multiple Sensor Fusion Approach Applied to UAV Attitude Estimation

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Yu Gu ◽  
Jason N. Gross ◽  
Matthew B. Rhudy ◽  
Kyle Lassak
Keyword(s):  
Sensor Fusion ◽  
Fault Tolerant ◽  
Attitude Estimation ◽  
Single Frequency ◽  
Test Bed ◽  
Multiple Sources ◽  
Fusion Algorithm ◽  
Cost Rate ◽  
Fault Detection And Identification ◽  
Multiple Sensor

A novel sensor fusion design framework is presented with the objective of improving the overall multisensor measurement system performance and achieving graceful degradation following individual sensor failures. The Unscented Information Filter (UIF) is used to provide a useful tool for combining information from multiple sources. A two-step off-line and on-line calibration procedure refines sensor error models and improves the measurement performance. A Fault Detection and Identification (FDI) scheme crosschecks sensor measurements and simultaneously monitors sensor biases. Low-quality or faulty sensor readings are then rejected from the final sensor fusion process. The attitude estimation problem is used as a case study for the multiple sensor fusion algorithm design, with information provided by a set of low-cost rate gyroscopes, accelerometers, magnetometers, and a single-frequency GPS receiver’s position and velocity solution. Flight data collected with an Unmanned Aerial Vehicle (UAV) research test bed verifies the sensor fusion, adaptation, and fault-tolerance capabilities of the designed sensor fusion algorithm.

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