RLS channel estimation with adaptive forgetting factor in space-time coded MIMO-OFDM systems

Considering that channel estimation plays a crucial role in coherent detection, this paper addresses a method of Recursive-least-squares (RLS) channel estimation with adaptive forgetting factor in wireless space-time coded multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Because there are three different forgetting factor scenarios including adaptive, two-step and conventional ones applied to RLS channel estimation, this paper describes the principle of RLS channel estimation and analyzes the impact of different forgetting factor scenarios on the performances of RLS channel estimation. Simulation results proved that the RLS algorithm with adaptive forgetting factor (RLS-A) outperformed that with two-step forgetting factor (RLS-T) or with conventional forgetting factor (RLS-C) in both estimation accuracy and robustness over the multiple-input multiple-output (MIMO) channel, i.e., a wide-sense stationary uncorrelated scattering (WSSUS) and frequency-selective slowly fading channel. Hence, we can employ the RLS-A method by adjusting forgetting factor adaptively to track and estimate channel state parameters successfully in space-time coded MIMO-OFDM systems.

Autonomous navigation method of satellite constellation based on adaptive forgetting factors

To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation,an autonomous navigation method of satellite constellation based on the Unscented Kalman Filter with Adaptive Forgetting Factors(UKF-AFF)is proposed.The process noise covariance matrix is estimated online with the strategy that combines covariance matching and adaptive adjustment of forgetting factors.The adaptive adjustment coefficient based on squared Mahalanobis distance of state residual is employed to achieve online regulation of forgetting factors,equipping this method with more adaptability.The intersatellite direction vector obtained from photographic observations is introduced to determine the constellation satellite orbit together with the distance measurement to avoid rank deficiency issues.Considering that the number of available measurements varies online with intersatellite visibility in practical applications such as time-varying constellation configurations,the smooth covariance matrix of state correction determined by innovation and gain is adopted and constructed recursively.Stability analysis of the proposed method is also conducted.The effectiveness of the proposed method is verified by the Monte Carlo simulation and comparison experiments.The estimation accuracy of constellation position and velocity of UKF-AFF is improved by 30%and 44%respectively compared to those of the extended Kalman filter,and the method proposed is also better than other several adaptive filtering methods in the presence of significant model uncertainty.

An efficient equivariant adaptive separation via independence algorithm for acoustical source separation and identification

To balance the convergence rate and steadystate error of blind source separation(BSS) algorithms, an efficient equivariant adaptive separation via independence(Efficient EASI) algorithm is proposed based on separating indicator, which was derived from the convergence condition of EASI, and can be used to evaluate the separation degree of separated signals. Furthermore, a nonlinear monotone increasing function between suitable step sizes and separating indicator is constructed to adaptively adjust step sizes, and forgetting factor is employed to weaken effects of data at the initial stage. Numerical case studies and experimental studies on a test bed with shell structures are provided to validate the efficiency improvement of the proposed method. This study can benefit for vibration & acoustic monitoring and control, and machinery condition monitoring and fault diagnosis.