Using MATLAB? examples wherever possible, Multi-Sensor Data
Fusion with MATLAB explores the three levels of multi-sensor data
fusion (MSDF): kinematic-level fusion, including the theory of DF;
fuzzy logic and decision fusion; and pixel- and feature-level image
fusion. The authors elucidate DF strategies, algorithms, and
performance evaluation mainly for aerospace applications, although
the methods can also be applied to systems in other areas, such as
biomedicine, military defense, and environmental engineering.
After presenting several useful strategies and algorithms for DF
and tracking performance, the book evaluates DF algorithms,
software, and systems. It next covers fuzzy logic, fuzzy sets and
their properties, fuzzy logic operators, fuzzy
propositions/rule-based systems, an inference engine, and
defuzzification methods. It develops a new MATLAB graphical user
interface for evaluating fuzzy implication functions, before using
fuzzy logic to estimate the unknown states of a dynamic system by
processing sensor data. The book then employs principal component
analysis, spatial frequency, and wavelet-based image fusion
algorithms for the fusion of image data from sensors. It also
presents procedures for combing tracks obtained from imaging sensor
and ground-based radar. The final chapters discuss how DF is
applied to mobile intelligent autonomous systems and intelligent
monitoring systems.
Fusing sensors? data can lead to numerous benefits in a system's
performance. Through real-world examples and the evaluation of
algorithmic results, this detailed book provides an understanding
of MSDF concepts and methods from a practical point of view.
Select MATLAB programs are available for download on
www.crcpress.com
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