Multiple-sensor Fusion Tracking Based on Square-root Cubature Kalman Filtering

2014 ◽  
Vol 9 (7) ◽  
Author(s):  
Yanli Hou
Keyword(s):  
Sensor Fusion ◽  
Kalman Filtering ◽  
Square Root ◽  
Multiple Sensor ◽  
Fusion Tracking

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 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.

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