Canceling stationary interference signals exploiting secondary data

In this paper, we propose a novel interference cancellation method that exploits secondary data to estimate stationary interference components present in both the primary and the secondary data sets, thereby allowing for the removal of such interference from the data sets, even when these components share frequencies with the signal of interest. The algorithm estimates the present interference components one frequency at a time, thus enabling for a computationally efficient algorithm, that require only a limited amount of secondary data. Numerical examples using both simulated and measured data show that the proposed methods offers a notable gain in performance as compared to other interference cancellation methods.