Detection and classification of unknown MTI and PBNI in slow FH/BFSK systems over Rayleigh fading channels

In this paper, we propose an algorithm using an autoregressive (AR) spectral estimation method to detect and classify unknown interference which is encountered in frequency-hopped spread spectrum (FH-SS) systems over a Rayleigh fading channel. Two most frequently encountered interference models, namely multitone interference (MTI) and partial-band noise interference (PBNI), are considered. The algorithm is processed in the AR coefficient domain by making use of the hopping nature of the FH-SS signal and stationarity of the interference signal. Once the processed AR power spectrum is obtained, an adaptive threshold which does not require a priori knowledge of the signal and interference is used to detect and classify the unknown interference. Computer simulations are conducted to investigate detection performance of the proposed AR technique, and comparisons between the proposed scheme and the fast Fourier transform (FFT)-based scheme are presented. The results show that the proposed AR technique is able to detect both types of unknown interference and classify them accurately. The comparisons reveal that the proposed technique can provide better and more reliable interference estimates than the FFT-based detection technique.