A collaborative adaptive algorithm for the filtering of noncircular complex signals

In this paper we propose an adaptive estimation algorithm for in-network processing of complex signals. The proposed algorithm, which will be referred as the incremental augmented complex least mean square (IAC-LMS) algorithm, relies on the incremental collaboration among the nodes, and the LMS adaptive filtering. Spatial data mining is archived by the incremental collaboration; while with LMS learning rules to endow the network with adaptation. We derive the required conditions for mean stability of the proposed algorithm. We use real world noncircular wind data to evaluate the performance of the proposed algorithm. Our simulation results reveal that the IAC-LMS algorithm is able to estimate noncircular (improper) signals.