Joint angular and spectral estimation technique using nonlinear Kalman filters for cognitive radio

The joint estimation of direction of arrivals (DOA) and carrier frequencies of band-limited source signals is considered in this paper. A novel technique based on nonlinear Kalman filters is proposed for this joint angular and spectral estimation problem for cognitive radio (CR). Since sampling a wideband spectrum at Nyquist rate increases the analog-to-digital converter (ADC) requirements, we propose executing Kalman filter algorithm over a spatial state space model. Thus, one time sample is required and hardware complexity is reduced. Two types of nonlinear Kalman filters, extended Kalman filter (EKF) and unscented Kalman filter (UKF), are proposed. We consider their sub-optimal performance and show how to control their convergence. However, the proposed algorithms can detect a number of source signals limited to the number of elements in employing arrays.