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28 July 1997 Linear combination of multiple adaptive 2-class classifiers: an efficient, effective approach to airborne target ID
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Linearly combining multiple, adaptive 2-class classifiers provides the capability to integrate many individual classifiers each of which possesses arbitrarily complex decision boundaries into simple, functioning classification system. Adaptive two classifiers and the linear combination approach are described. In benchmarking studies this approach has been shown to require O(2) magnitude less computational resources when compared to a classical statistical approach. We will present comparative resource requirements for performance equivalent algorithms. The adaptive algorithm used in the above benchmarking study is widely known for its pattern recognition capabilities. This approach can be used on range profile data to perform 1D target ID. In this applications, two characteristics of this approach can be seen to be of particular benefit in an airborne environment. These characteristics are: robust performance in the face of varying SNR and robust performance in the face of rough aspect angle information. We will discuss the theory behind the characteristics of this approach that provide such benefits. Finally, with respect to the cost required to maintain a fielded system, a linear combination approach to adaptive ID algorithms provides special benefits. Chief among the advantages is the ability to rapidly update a fielded system with new information. Using this approach the changes required to add new targets to a given system are equivalent to downloading a new data base. The nature of this algorithm allows for low risk, low cost upgrades. Furthermore, very rapid turn around time will be possible in the case where only a small percentage of new targets are to be added to the capabilities of a fielded system.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Joan L. Brierton "Linear combination of multiple adaptive 2-class classifiers: an efficient, effective approach to airborne target ID", Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997);

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