Fast De-hopping and Frequency Hopping Pattern (FHP) Estimation for DS/FHSS Using Neural Networks
A Fast de-hopping and FHP estimation model for Direct Sequence/Frequency Hopping Spread Spectrum (DS/FHSS) system is proposed. The Neural Networks (NNs) were used to mimic the Parallel Matched Filtering (PMF). The signal samples and its Fast Fourier Transform (FFT) were used for Back propagation Neural Network (BNN) training. The FH patterns designated as concatenated prime codes  were used for the Radial Basis Function (RBF) training. Computer simulations show that the proposed method can effectively identify the frequency and estimate its pattern. Small hardware resources compared with PMF hardware.
KeywordsAcquisition Direct Sequence Frequency Hopping Pattern Neural Network Matched Filter Fourier transform
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