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To address the shortcomings of the widely-linear complex-valued distributed adaptive filtering algorithm that cannot combine convergence speed and steady-state performance when using a fixed step-size strategy. In this paper, by making the partial derivative of the square of the sum of a priori error and a posteriori error with respect to step-size equal to zero, we propose a novel variable step-size (VSS) strategy, which is formulated as a function of a priori error and a posteriori error. In addition, a corresponding adaptive filtering algorithm is proposed by using the VSS strategy. Then in the process of parameter update, the moving average method is used to avoid large perturbations in iteration of the algorithm. Secondly, the theoretically analysis of the transient and steady-state performance of the proposed algorithm is provided. Finally, in the numerical simulation experiments, the effect of each parameter on the algorithm is tested, the result of the comparison with the fixed-step algorithm illustrates that the proposed algorithm has more superiority, and the high degree of matching between theory and experiment also verifies the feasibility and accuracy of the theoretical analysis method. [ABSTRACT FROM AUTHOR]

Constrained least mean square (CLMS) algorithm is the most popular constrained adaptive filtering algorithm due to its simple structure and easy implementation. However, its convergence slows down when the input signal is colored. To address this issue, this paper firstly introduces the normalized subband adaptive filter (NSAF) into the constrained filtering problem and derives a constrained NSAF (CNSAF) algorithm using the Lagrange multiplier method. Benefiting from the good decorrelation capability of the NSAF, the proposed CNSAF algorithm significantly improves the convergence performance of the CLMS algorithm under colored inputs. Then, the mean and mean-square stability of the CNSAF algorithm is analyzed, and the theoretical models to characterize the transient and steady-state mean square deviation (MSD) behaviors of the CNSAF algorithm are derived utilizing the Kronecker product property and vectorization method. Further to extend the CNSAF algorithm to the problem of sparse system identification, a sparse version of the CNSAF algorithm (S-CNSAF) is derived. Finally, the validity of the derived theoretical MSD prediction models and the superiority of the proposed algorithms are confirmed by extensive computer simulations on system identification with colored inputs. [ABSTRACT FROM AUTHOR]

In the past decade, the augmented complex-valued least mean square adaptive filtering algorithm based on the minimum mean square error criterion and widely-linear model has attracted much attentions due to its simplicity and applicability to non-circular signals. However, the system noise with impulse characteristics will dramatically affect the convergence of the augmented complex-valued least mean square algorithm. Recently, a robust widely-linear complex-valued least mean M-estimate algorithm has been proposed, which significantly improves the robustness of the augmented complex-valued least mean square algorithm to impulsive noise. But it should be noted that the widely-linear complex-valued least mean M-estimate algorithm with fixed step size faces the trade-off between convergence speed and steady-state accuracy. To overcome this drawback, a variable step size widely-linear complex-valued least mean M-estimate algorithm is presented in this paper, in which the optimal step size at each iteration is obtained by maximizing the difference between the mean square deviations of the WL-CLMM algorithm at adjacent moments. In order to reveal the statistical convergence behavior of the proposed variable-step-size widely-linear complex-valued least mean M-estimate algorithm to better guide the parameter selection in practical applications. Theoretical transient and steady-state mean-square deviation convergence behaviors of the variable-step-size widely-linear complex-valued least mean M-estimate algorithm are analyzed and the corresponding mean-square deviation expressions are also derived. Computer simulations on system identification and stereophonic acoustic echo cancellation in impulsive noise environments confirm the validity of the analysis results and the performance improvement from the variable step size strategy. [ABSTRACT FROM AUTHOR]

This paper proposes a Siamese motion-aware Spatio-temporal network (SiamMAST) for video action recognition. The SiamMAST is designed based on the fusion of four features via processing video frames: spatial features, temporal features, spatial dynamic features, and temporal dynamic features of a moving target. The SiamMAST comprises AlexNets as the backbone, LSTMs, and the spatial motion-awareness and temporal motion-awareness sub-modules. RGB images are fed into the network, where AlexNets extract spatial features. Further, they are fed into LSTMs to generate temporal features. Additionally, spatial motion-awareness and temporal motion-awareness sub-modules are proposed to capture spatial and temporal dynamic features. Finally, all features are fused and fed into the classification layer. The final recognition result is produced by averaging the test label probabilities across a fixed number of RGB frames and selecting the label of the highest probability. The whole network is trained offline using an end-to-end approach with large-scale image datasets using the standard SGD algorithm with back-propagation. The proposed network is evaluated on two challenging datasets UCF101 (93.53%) and HMDB51 (69.36%). The experiments have demonstrated the effectiveness and efficiency of our proposed SiamMAST. [ABSTRACT FROM AUTHOR]

The complex domain adaptive filtering algorithms has shown excellent performance in the field of signal processing. Among them, the well-known complex least mean square (CLMS) algorithm has been widely used in practical projects. However, the CLMS algorithm only considers the case where the output signal is disturbed by noise, ignores the input signal may also be interfered by noise. Moreover, the signal is likely to be disturbed by impulsive noise in practice. Based on the above problems, the complex total least mean M-estimation (CTLMM) adaptive algorithm based on the M-estimation function is put forward in this paper. The algorithm can well handle the case where both input and output signals are subject to complex domain noise interference, and it also shows robustness with impulsive noise. This paper also analyzes the local stability of the CTLMM algorithm and discuss the theoretical steady-state error of the algorithm. Finally, simulation results verify the advantages of CTLMM algorithm and the rightness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

In order to solve the problem of deteriorating performance of the conventional subband adaptive filtering algorithm when processing the EIV model with impulsive noise, this paper proposes the robust Total Least Mean M-Estimate normalized subband filter (TLMM-NSAF) adaptive algorithm based on the M-estimation function. In addition, we conduct a detailed theoretical performance analysis of the TLMM-NSAF algorithm, which allows us to determine the stable step size range and theoretical steady-state mean squared deviation of the algorithm. To further improve the algorithm's performance, we propose a new variable step size method. Finally, we compared the algorithm with other competition algorithms in applications of system identification and acoustic echo cancellation. Simulation results have demonstrated the superiority of our proposed algorithm, as well as the consistency between the theoretical values and the simulated values. [ABSTRACT FROM AUTHOR]