Multi sensor data fusion algorithms for target tracking using multiple measurements

Multi-Sensor Data Fusion (MSDF) is very rapidly growing as an independent discipline to be considered with and finds applications in many areas. Surplus and complementary sensor data can be fused using multi-sensor fusion techniques to enhance system competence and consistency. The objective of this...

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Bibliographic Details
Published in:2013 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) pp. 1 - 4
Main Authors: Anitha, R., Renuka, S., Abudhahir, A.
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2013
Subjects:
Data integration
Filtering algorithms
Gain measurement
Kalman filter
Kalman filters
sensor fusion
Signal processing algorithms
Target tracking
Vectors
ISBN:1479915947, 9781479915941
Online Access:Get full text
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Summary:Multi-Sensor Data Fusion (MSDF) is very rapidly growing as an independent discipline to be considered with and finds applications in many areas. Surplus and complementary sensor data can be fused using multi-sensor fusion techniques to enhance system competence and consistency. The objective of this work is to evaluate multi sensor data fusion algorithms for target tracking. Target tracking using observations from several sensors can achieve improved estimation performance than a single sensor. In this work, three data fusion algorithms based on Kalman filter namely State Vector Fusion (SVF), Measurement Fusion (MF) and Gain fusion (GF) are implemented in a tracking system. Using MATLAB, these three methods are compared and performance metrics are computed for the evaluation of algorithms. The results show that State Vector Fusion estimates the states well when compared to Measurement Fusion and Gain Fusion.
ISBN:1479915947
9781479915941
DOI:10.1109/ICCIC.2013.6724283