A principal component analysis and partical swarm optimization based method for separation of electromagnetic signals

In a complex electromagnetic environment, and with little knowledge about the source electromagnetic signals, it is a big challenge to analyze an electronic system's electromagnetic compatibility (EMC) or arrange the electromagnetic signal source properly. To solve this problem, useful signals and noise signals should be separated first from the mixed signals that can be measured. In this paper, we proposed a separation method which mainly takes two steps. The first step is to determine the number of source signals by combining the Principle component analysis (PCA) and the maximum likelihood estimation (MLE) methods. The second step is to separate the mixed observation signals. We achieve this by using the particle swarm algorithm. A simulation experiment is given to demonstrate the validity and efficiency of the proposed separation algorithm.