Adaptive Direct Blind Equalization under Noisy Environment This paper proposes adaptive direct blind equalization under noisy environment. There is direct blind equalization corresponding to noisy environment using Rayleigh Quotient 2 as one of the conventional method of direct blind equalization method. It is known that the conventional method is higher precision even when the observed signals is included the noises. However, the conventional method has the problem that there is many computational complexity because this method have to calculate inverse matrix. In this paper, for solving this problem, we propose adaptive direct blind equalization under noisy environment. The features of proposed method are (i) realization of high performance and stable equalizer estimation with Data Least square(DLS) and (ii) reduction of computational complexity using gradient method. We show the effectiveness of the proposed method using computer simulation.

Modified Blind Equalization Algorithm Based on Cyclostationarity for Contaminated Reference Signal in Airborne PBR In airborne passive bistatic radar (PBR), the reference channel toward the opportunity illuminator is applied to receive the directpath signal as the reference signal. In the actual scenario, the reference signal is contaminated by the multipath signals easily. Unlike the multipath signal in traditional ground PBR system, the multipath signal in the airborne PBR owns not only the time delay but also the Doppler frequency. The contaminated reference signal can cause the spatialtemporal clutter spectrum to expand and the false targets to appear. The performance of target detection is impacted severely. However, the existing blind equalization algorithm is unavailable for the contaminated reference signal in airborne PBR. In this paper, the modified blind equalization algorithm is proposed to suppress the needless multipath signal and restore the pure reference signal. Aiming at the Doppler frequency of multipath signal, the highorder moment information and the cyclostationarity of source signal are exploited to construct the new cost function for the phase constraint, and the complex value back propagation (BP) neural network is exploited to solve the constraint optimization problem for the better convergence. In final, the simulation experiments are conducted to prove the feasibility and superiority of proposed algorithm.