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The objective of this paper is to implement the equivalent filter bank structures for the computation of the Fractional Fourier transform (FrFT). Through this approach we can reduce the computation time of the fractional Fourier transform (FrFT). In this paper first the two methods to compute the fractional Fourier transform of any function is derived. Then the computation method is implemented using the filter bank approach. The fractional Fourier transform has many applications recently like we can compute the DFTs of sequences with prime lengths and sparse sequences, sequences of noninteger periodicities, reducing the noise in noisy images but it is possible when the noise and data are uncorrelated, in sonar signal processing and this transform gives better results compared to the Fourier transform in many areas. Here we discussed a method for reducing process time and increasing matching accuracy in …