A Novel Framework for Target Tracking and Data Fusion in Wireless Sensor Networks Using Kernel Based Learning Algorithm
In recent years, a number of powerful kernel based learning algorithms like Support Vector machines, Kernel Fisher Discriminant Analysis, Kernel Principal Component Analysis have reported their success in various domains like Signal Processing, Image Processing and Pattern Classification. In a nutshell, a kernel-based algorithm is a nonlinear version of a linear algorithm where the data has been nonlinearly transformed to a higher dimensional space, in which we need to compute the inner products via a kernel function as in . The attractiveness of such algorithm stems from their elegant treatment of nonlinear problems and their efficacy in high-dimensional space. In this paper, we illustrate a comprehensive review of kernel methods and its usefulness in signal processing problems like target detection and target tracking.
KeywordsSupport Vector Machine Kernel Function Target Tracking Target Detection
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