SSP-Based UBI Algorithms for Uniform Linear Array

The underdetermined blind identification (UBI) for uniform linear array (ULA) with complex-valued mixing matrix can be processed effectively by the algorithms based on single-source-point (SSP) detection. In this paper, we propose two novel SSP-based methods of UBI in the time–frequency (TF) domain for ULA. One method, called UBI based on linear TF transform (UBI-LT), presents a new SSP detection criterion based on short-time Fourier transform, which modifies IME-RSSP (proposed by Li) by exploiting the phase information of mixture. The other method proposes a new SSP detection criterion based on a cross-term suppression quadratic TF distribution called UBI based on modified quadratic TF distribution (UBI-MQD), which can be seen as an improved version of the SSP-based algorithm proposed by Su. After performing these SSP detection criteria, two methods employ the peak detection and a clustering algorithm to estimate the complex-valued mixing matrix. Two methods have their own advantages and can be chosen by robust systems or high-performance systems. Numerical simulation results show that (1) the proposed methods have better performance than the existing methods with the same means of TF analysis (linear TF transform or quadratic TF distribution), and (2) UBI-LT is more robust than UBI-MQD even on the condition that the source number is large and the signal-to-noise (SNR) is low, while UBI-MQD has higher performance than UBI-LT when the source number is small and the SNR is high.