The eigencomponent association method for adaptive interference suppression

The performance of passive localization algorithms can become severely degraded when the target of interest is in the presence of interferers. In this paper, the eigencomponent association (ECA) method of adaptive interference suppression is presented for signals received on horizontal arrays. ECA uses an eigendecomposition to decompose the cross-spectral density matrix (CSDM) of the data and then beamforms each of the eigenvectors. Using an estimate of the target’s bearing, the target-to-interference power in each eigenvector at each CSDM update is computed to determine which are dominated by interference. Eigenvectors identified to contain low target-to-interference power are subtracted from the CSDM to suppress the interference. Using this approach, ECA is able to rapidly adapt to the hierarchical swapping of target and interference-related eigenvectors due to relative signal power fluctuations and target dynamics. Simulated data examples consisting of a target and two interferers are presented to demonstrate the effectiveness of ECA. These examples show ECA enabling accurate localization estimates in the presence of interferers, which without using the technique was not possible.