Widely linear UKF constant modulus algorithm for blind adaptive beamforming

Based on a uniform linear array, a new widely linear unscented Kalman filter-based constant modulus algorithm (WL-UKF-CMA) for blind adaptive beamforming is proposed. The new algorithm is designed according to the constant modulus criterion and takes full advantage of the noncircular property of the signal of interest (SOI), significantly increasing the output signal-to interference-plus-noise ratio (SINR), enhancing the convergence speed and decreasing the steady-state misadjustment. Since it requires no known training data, the proposed algorithm saves a large amount of the available spectrum. Theoretical analysis and simulation results are presented to demonstrate its superiority over the conventional linear least mean square-based CMA (L-LMS-CMA), the conventional linear recursive least square-based CMA (L-RLS-CMA), WL-LMS-CMA, WL-RLS-CMA and L-UKF-CMA.

Blind Wideband Beamforming Algorithm Based on the Uncertainty Set

Without prior knowledge,the steering vector estimation error of the desired signal will deteriorate the beamforming performance to some extent.To solve this problem,a space-time blind wideband beamforming algorithm based on the uncertainty set is proposed.First of all,based on the space-time filtering model,the spherical constraint set is designed according to the uncertainty of the estimation error of the space-time steering vector.Then,the method of wideband beamforming under multiconstraints in the frequency domain is derived,and the calculation of parameters of steering vector estimation error and loading factor are given in detail.Finally,a blind broadband beamforming algorithm combined with the CAB algorithm is proposed.The improvement of the output signal-to-noise ratio is quantitatively analyzed by computer simulation to verify the correctness and robustness of the algorithm.

Non-Negative Matrix Factorization Based UKF Algorithm for Constant Modulus Signals in Adaptive Beamforming

Blind adaptive beamforming is getting appreciated for its various applications in contemporary communication systems where sources are statistically dependent or independent that are allowed to formulate new algorithms. Qualitative performance and time complexity are the main issues. In this paper, we propose a technique for constant modulus signals applying basic non-negative matrix factorization (BNMF) in blind adaptive beamforming environment. We compared the existing Unscented Kalman Filter based Constant Modulus Algorithm (UKF-CMA) with proposed NMF-UKF-CMA algorithm. We see there is a better improvement of sensor array gain, signal to interference plus noise ratio (SINR) and mean squared deviation (MSD) as the noise variance and the array size increase with reduced computational complexity with the UKF-CMA.

Wideband Beamforming for Multipath Signals Based on Frequency Invariant Transformation

It is well known that the performance of conventional adaptive beamformers degrades severely due to the presence of coherent or correlated interferences(multipath propagation) and various techniques have been developed to improve the performance of the beamformer.However,most of the work in the past has been focused on the narrowband case.In this paper,the wideband beamforming problem in the presence of multipath signals is addressed,with a novel approach proposed by employing a pre-processing stage based on the frequency invariant beamforming(FIB) technique.In this approach,the received wideband array signals are first processed by an FIB network,and then a traditional narrowband adaptive beamformer or an appropriate instantaneous blind source separation(BSS) algorithm can be applied to the network outputs.It is shown that with the proposed structure,cancellation of the desired signal is reduced,leading to a significantly improved output signal to interference plus noise ratio(SINR).