Sensor Fusion Algorithm for Airborne Autonomous Vehicle Collision Avoidance Applications

2018 ◽  
Author(s):  
Julien Albert Doe
Keyword(s):  
Collision Avoidance ◽  
Sensor Fusion ◽  
Autonomous Vehicle ◽  
Fusion Algorithm ◽  
Vehicle Collision

scholarly journals RACE: Reinforced Cooperative Autonomous Vehicle Collision Avoidance

2020 ◽  
Vol 69 (9) ◽  
pp. 9279-9291 ◽  
Author(s):  
Yali Yuan ◽  
Robert Tasik ◽  
Sripriya Srikant Adhatarao ◽  
Yachao Yuan ◽  
Zheli Liu ◽  
...  
Keyword(s):  
Collision Avoidance ◽  
Autonomous Vehicle ◽  
Vehicle Collision

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.

scholarly journals Evaluation of Matrix Square Root Operations for UKF within a UAV GPS/INS Sensor Fusion Application

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Matthew Rhudy ◽  
Yu Gu ◽  
Jason Gross ◽  
Marcello R. Napolitano
Keyword(s):  
Sensor Fusion ◽  
Unscented Kalman Filter ◽  
Attitude Estimation ◽  
Square Root ◽  
Fusion Algorithm ◽  
Matrix Square Root ◽  
Global Positioning ◽  
The Matrix ◽  
Aerial Vehicle ◽  
Diagonalization Method

Using an Unscented Kalman Filter (UKF) as the nonlinear estimator within a Global Positioning System/Inertial Navigation System (GPS/INS) sensor fusion algorithm for attitude estimation, various methods of calculating the matrix square root were discussed and compared. Specifically, the diagonalization method, Schur method, Cholesky method, and five different iterative methods were compared. Additionally, a different method of handling the matrix square root requirement, the square-root UKF (SR-UKF), was evaluated. The different matrix square root calculations were compared based on computational requirements and the sensor fusion attitude estimation performance, which was evaluated using flight data from an Unmanned Aerial Vehicle (UAV). The roll and pitch angle estimates were compared with independently measured values from a high quality mechanical vertical gyroscope. This manuscript represents the first comprehensive analysis of the matrix square root calculations in the context of UKF. From this analysis, it was determined that the best overall matrix square root calculation for UKF applications in terms of performance and execution time is the Cholesky method.

Sign in / Sign up

Export Citation Format

Share Document