Genetic Algorithms based Design of a Radar Tracker for Stressful Environments

A.W. Abid, F. Azam, and S. Khan (Pakistan)


Genetic Algorithms, Genetic Optimization, fuzzy logic, fuzzy Membership Functions, AlphaBeta Tracking


In this paper, we present genetically optimized fuzzy alpha beta tracker for radar tracking in rather stressful environ ments. During the course of flight, an aircraft can accel erate and may make sharp turns. Limitations of the sen sor may also add noise to the actual already-stressful envi ronment, which further reduce the accuracy of alpha-beta and fuzzy alpha-beta tracker. Moreover, fuzzy alpha-beta tracker requires expert domain knowledge for modeling of fuzzy gain filters. The proposed tracker eliminates the re quirements of expert domain knowledge, as the Genetic Al gorithms heuristically search for the different membership functions in the given Universe of Discourse. The proposed tracker was tested on simulated noisy data, and is found better in terms of MSE of actual and tracked positions.

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