Coherent Signal Enumeration based on Deep Learning and the FTMR Algorithm
Trong-Dai Hoang, Kyungchun Lee · 2022
This work explores the potential of a deep learning-aided detector for narrowband signal enumeration in a coherent environment. Specifically, we introduce the logarithmic eigenvalue-based classification network (LogECNet) to detect the signal number. In the proposed scheme, the full-row Toeplitz matrices reconstruction (FTMR) algorithm is employed to avoid the rank loss of the signal covariance matrix (SCM) in highly correlated signal environments. The simulation results show that the FTMR method not only achieves the complexity reduction with respect to the prior forward/backward spatial smoothing (FBSS) algorithm, but also improves the signal number detection performance when combined with LogECNet.