Subspace-Based Algorithms for Blind ML Frequency and Transition Time Estimation in Frequency Hopping Systems

Frequency hopping spread spectrum (FHSS) is a technology for combating narrow band interference. Two parameters required for estimation in FHSS are transition time and hopping frequency. In this paper, blind subspace-based schemes with a maximum likelihood (ML) criterion for estimating frequency and transition time without using reference signals are proposed. The selection of the related parameters is discussed. Subspace-based algorithms are applied with the help of the proposed block selection scheme. The performance is improved with a block selection algorithm to overcome the unbalanced processing block problems in various algorithms. The proposed method significantly reduces computational complexity compared with a greedy search ML-based algorithm. The performance is shown to outperform an existing iterative ML-based algorithm with a comparable complexity.