Informatica

The crosstalk error is widely used to evaluate the performance of blind source separation. However, it needs to know the global separation matrix in advance, and it is not robust. In order to solve these problems, a new adaptive algorithm for calculating crosstalk error is presented, which calculates the crosstalk error by a cost function of least squares criterion, and the robustness of the crosstalk error is improved by introducing the position information of the maximum value in the global separation matrix. Finally, the method is compared with the conventional RLS algorithms in terms of performance, robustness and convergence rate. Furthermore, its validity is verified by simulation experiments and real world signals experiments.

In this paper, one method for training the Support Vector Regression (SVR) machine in the complex data field is presented, which takes into account all the information of both the real and imaginary parts simultaneously. Comparing to the existing methods, it not only considers the geometric information of the complex-valued data, but also can be trained with the same amount of computation as the original SVR in the real data field. The accuracy of the proposed method is analysed by the simulation experiments. This also can be applied to the field of anti-interference for satellite navigation successfully, which shows its effectiveness in practical application.