Tensor factorisation for narrowband single channel source decomposition

In many subspace signal decomposition methods such as principal component analysis (PCA) or its extension, singular spectrum analysis (SSA), particularly meant for processing of single channel signals, there is need for a robust determination and validation of the number of sources. Here, we attempt to find a relation between the number of sources within single channel mixtures and the rank of a symmetric tensor constructed from such mixtures by adjusting the embedding dimension. This leads to a new approach for decomposition of single channel mixtures using tensor factorisation. Consequently, the effect of model order is analysed for simulated narrowband data. The inherent frequency diversity of the time series has also been effectively exploited in selection of the desired subspace. The proposed method has been applied to both simulated and real data. The results have been discussed and compared with those of a number of benchmark algorithms.