Abstract:
Frequency-hopping spread spectrum (FHSS) spreads the signal over a wide bandwidth where the carrier frequencies
change rapidly according to a pseudorandom number making signal classification difficult. Classification becomes more complex
with the presence of additive white Gaussian noise (AWGN) and interference due to background signals. In this paper, an artificial
neural network (ANN) based classification system is proposed for FHSS signals in the presence of AWGN and background signal.
The probability of correct classification (PCC) of the FHSS signals is performed by the linear discriminant (LD) and ANN. Based on
the signal-to-noise ratio (SNR) range at 0.9 PCC, the performance of the LD and ANN respectively is 5.1 dB and 2.5 dB in the
presence of AWGN only whereas their performance is 14 dB and 2.3 dB when the background signal is present. Resultantly, the
ANN-based system outperformed the LD method by 2.6 to 11.7 dB of SNR.