The Study of Frequency-Hopping Networks Sorting Based on Randomly Distributed Antenna System

The DOA-based sorting methods of frequency hopping signals mostly utilized arrays with standard geometry, which presented higher requirement for antenna layout and channel conformance, therefore cannot be suitable for flexible application. To solve these problems, a novel method based on Sparse Bayesian Learning algorithm in randomly distributed antenna system was proposed to complete the sparse reconstruction of received signals. According to the reconstruction result the number of frequency hopping signals, carrier frequency and time-delay vector can be calculated based on the matching algorithom and selective vector. Considering of the fact that different hops of one frequency hopping signal possessed the same time-delay vectors, an improved K-means clustering algorithm which can accelerate the calculation speed and enhance the convergence accuracy was utilized to sort the frequency hopping signals from different locations. Numerical examples demonstrate that the proposed method can estimate carrier frequency, cluster the time-delay vectors and sort the frequency hopping signals more accurately and effectively than existed methods based on Particle Filter algorithm.