A realization of classification success in multi sensor data fusion

• Main Authors: Masnan, Maz Jamilah, Zakaria, Ammar, Md Shakaff, Ali Yeon, Mahat, Nor Idayu, Hamid, Hashibah, Subari, Norazian, Mohamad Saleh, Junita
• Other Authors: Sanguansat, Parinya
• Format: Book Section
• Language: English
• Published: InTech 2012
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://repo.uum.edu.my/21573/1/978-953-51-0182-6%201%2024.pdf; http://repo.uum.edu.my/21573/; https://www.intechopen.com/books/principal-component-analysis-engineering-applications/principal-component-analysis-a-realization-of-classification-success-in-multi-sensor-data-fusion

The field of measurement technology in the sensors domain is rapidly changing due to the availability of statistical tools to handle many variables simultaneously.The phenomenon has led to a change in the approach of generating dataset from sensors. Nowadays, multiple sensors, or more specifically multi sensor data fusion (MSDF) are more favourable than a single sensor due to significant advantages over single source data and has better presentation of real cases.MSDF is an evolving technique related to the problem for combining data systematically from one or multiple (and possibly diverse) sensors in order to make inferences about a physical event, activity or situation. Mitchell (2007) defined MSDF as the theory, techniques, and tools which are used for combining sensor data, or data derived from sensory data into a common representational format. The definition also includes multiple measurements produced at different time instants by a single sensor as described by (Smith & Erickson, 1991).