A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception

Abstract

The separation of time–frequency mixing signals composed of radar, communication, and jamming is the first step in integrated reception processing, which requires higher accuracy for complex blind source separation (CVBSS). However, traditional CVBSS methods have limitations such as low separation accuracy, a slow convergence speed, and poor robustness in low signal-to-noise ratio (SNR) and high jamming-to-signal ratio (JSR) scenarios. To address the above issues, this paper firstly establishes a time delay mixing mathematical model. A robust whitening algorithm is proposed by using the time delay correlation matrix of the observed signal, which is insensitive to noise. Secondly, the joint diagonalized F-parametrization is used as the objective function, and the separation matrix is constructed based on the multiple complex-valued Givens matrices. The complex-valued Givens matrix not only ensures orthogonality in the separation matrix but also effectively reduces the number of parameters to be calculated. This approach guarantees accuracy and simplifies the complexity of the separation process. Finally, the nonlinear chaotic grey wolf optimizer is utilized to search for the optimal rotation angle. The simulation results demonstrate that this algorithm offers higher separation accuracy and requires fewer iterations compared to the traditional algorithm. Additionally, it enhances the accuracy of direction of arrival (DOA) estimation, reduces the communication bit error rate, and enables the joint estimation of the target distance and velocity even in the presence of powerful jamming and a low SNR.

Antijamming Schemes for the Generalized MIMO Y Channel

“…Our further investigations will be directed at overcoming the drawback of the assumption of ideal jamming channel knowledge, e.g., by the application of a blind signal separation technique [45] well-suited for the scenario considered.…”

Exploiting Time–Frequency Sparsity for Dual-Sensor Blind Source Separation

“…For the first issue, various MME methods [18,21,27,[31][32][33][34][35][36][37][38] consider the TF points where only single source occurs or possesses dominant energy. These single-source TF points (SSPs) provide a directional clustering property, which can be associated with each column of the mixing matrix.…”