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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 this paper, a stochastic model is presented for the nonparametric variable step-size normalized least-mean-square (NP-VSS-NLMS) algorithm. This algorithm has demonstrated potential in practical applications and hence a deeper understanding of its behavior becomes crucial. In this context, model expressions are obtained for characterizing the algorithm behavior in the transient phase as well as in the steady state, considering a system identification problem and Gaussian input data. Such expressions reveal interesting algorithm characteristics that are useful for establishing design guidelines and for the advancement of more refined algorithms. Simulation results for various operating scenarios ratified both the model's accuracy and the algorithm's superior performance relative to other recent and relevant algorithms from the literature. [ABSTRACT FROM AUTHOR]

A novel multi-combined-step-size normalized subband adaptive filtering algorithm is proposed. Different from the traditional combined-step-size method, by employing a variable mixing parameter on each subband, our designed algorithm is able to combine a large step size with a small one more effectively for each subband. The subband mixing parameters are derived from the variance of the noise-free a priori subband error signals. In this design, a noniterative shrinkage strategy is also utilized to estimate the noise-free a priori subband error signals. Moreover, the mean-square and steady-state performances of the proposed algorithm are studied. Simulation results illustrate that the proposed algorithm outperforms other algorithms mentioned in this paper in terms of tracking capability, convergence speed and steady-state error. [ABSTRACT FROM AUTHOR]

To enhance the performance of the diffusion robust adaptive filtering algorithm, this paper presents a novel loss function framework named the "Tanh cost function framework." By utilizing this framework, we are able to incorporate standard cost functions, devise novel cost functions, and introduce the associated diffusion algorithm. Moreover, the convergence of the algorithm is thoroughly analyzed. Furthermore, the proposed diffusion Tanh algorithm family is simulated in a system identification model to assess its performance, demonstrating superior performance compared to the standard diffusive algorithm. Notably, the DTHLMS within the diffusion tanh algorithm family exhibits superior performance over the mainstream robust diffusion algorithms. [ABSTRACT FROM AUTHOR]

To enhance the convergence performance of the affine projection sign subband adaptive filter (AP-SSAF) algorithm under highly correlated inputs, the novel AP-SSAF (NAP-SSAF) algorithm was proposed through fully utilizing the decorrelation characteristic of SAF, which obtains quicker convergence behavior than the AP-SSAF algorithm. However, under unknown system identification, the estimation accuracy of the NAP-SSAF algorithm may unavoidably decrease when the input of the adaptive filter is contaminated by noise. To alleviate this issue, in this paper, we propose the bias-compensated NAP-SSAF (BC-NAP-SSAF) algorithm to alleviate the estimated bias through employing unbiasedness criterion. Benefiting from the unbiased criterion, the proposed BC-NAP-SSAF algorithm achieves an asymptotically unbiased estimation. Moreover, the convergence bounds of the proposed BC-NAP-SSAF algorithm in the mean and mean-square senses are provided to ensure stability, which are obtained by resorting to Price's theorem along with some reasonable assumptions. The effectiveness of the derived theoretical convergence bounds is also corroborated. Finally, numerical simulations under system identification and acoustic echo cancellation applications demonstrate that the proposed BC-NAP-SSAF algorithm outperforms other algorithms in terms of estimation accuracy and tracking capability for noisy input under impulsive noise environment. [ABSTRACT FROM AUTHOR]

For maneuvering target tracking, a novel adaptive variable structure interactive multiple model filtering and smoothing (AVSIMMFS) algorithm is proposed in this paper. Firstly, an accurate model of the variable structure interactive multiple model algorithm is established. Secondly, by constructing a new model subset based on the original model subsets, the matching accuracy between the model subset and the actual maneuvering mode of the target is improved. Then, the AVSIMMFS algorithm is obtained by smoothing the filtered data of the new model subset. Because of the combination of forward filtering and backward smoothing, the target tracking accuracy is further improved. Finally, in order to verify the effectiveness of the algorithm, the simulation is carried out on two cases. The simulation results show that the tracking performance of AVSIMMFS algorithm is better than other methods and has lower calculation cost. [ABSTRACT FROM AUTHOR]

Noise and interference are the two most common and basic problems in wireless communication systems. The noise in wireless communication channels has the characteristics of randomness and impulsivity, so the performance of adaptive filtering algorithms based on geometric algebra (GA) and second-order statistics is greatly reduced in the wireless communication systems. In order to improve the performance of adaptive filtering algorithms in wireless communication systems, this paper proposes two novel GA-based adaptive filtering algorithms, which are deduced from the robust algorithms based on the minimum error entropy (MEE) criterion and the joint criterion (MSEMEE) of the MEE and the mean square error (MSE) with the help of GA theory. The noise interference in wireless communication is modeled by α -stable distribution which is in good agreement with the actual data in this paper. Simulation results show that for the mean square deviation (MSD) learning curve, the GA-based MEE (GA-MEE) algorithm has faster convergence rate and better steady-state accuracy compared to the GA-based maximum correntropy criterion algorithm (GA-MCC) under the same generalized signal-to-noise ratio (GSNR). The GA-MEE algorithm reduces the convergence rate, but improves the steady-state accuracy by 10–15 dB compared to the adaptive filtering algorithms based on GA and second-order statistics. For GA-based MSEMEE (GA-MSEMEE) algorithm, when GA-MSEMEE and the adaptive filtering algorithms based on GA and second-order statistics keep the same convergence rate, its steady-state accuracy is improved by 10–15 dB, and when GA-MSEMEE and GA-MEE maintain approximately steady-state accuracy, its convergence rate is improved by nearly 100 iterations. In addition, when the algorithms are applied to noise cancellation, the average recovery error of the two proposed algorithms is 7 points lower than that of other GA-based adaptive filtering algorithms. The results validate the effectiveness and superiority of the GA-MEE and GA-MSEMEE algorithms in the α -stable noise environment, providing new methods to deal with multi-channel interference in wireless networks. [ABSTRACT FROM AUTHOR]

To make the zero attractor sign subband adaptive filter (ZA-SSAF) algorithm more suitable for sparse systems, where the impulse response is sparse and disturbed with impulse interference, this paper proposes an improved sign subband adaptive filtering algorithm that takes advantage of the splendid robustness of the arctangent function against impulse interference. Based on the ZA-SSAF algorithm, this algorithm introduces a proportionate coefficient matrix composed of a nonlinear function (the arctangent function) to assign different step sizes for the tap coefficients that need to be updated. The step size of the algorithm is updated in proportion to the magnitude of the weight coefficient in the adaptive process according to the relationship of the arctangent function, which greatly shortens the calculation convergence time and improves the overall convergence performance. The simulation results show that the proposed algorithm takes into account the trade-off between a faster convergence rate and a lower steady-state error and is superior to the traditional sign subband algorithm and zero attractor sign subband adaptive filtering algorithm in terms of the convergence rate and robustness against impulse noise. [ABSTRACT FROM AUTHOR]

The recursive-least-squares (RLS) algorithm is one of the most representative adaptive filtering algorithms. ℓ 1 -norm full-recursive RLS has also been successfully applied to various sparsity-related areas. However, computing the autocorrelation matrix inverse in the ℓ 1 -norm full-recursive RLS generates numerical instability that results in divergence. In addition, the regularization coefficient calculation for ℓ 1 -norm often requires actual channel information or relies on empirical methods. The iterative Wiener filter (IWF) has a similar performance to the RLS algorithm and does not require the inverse of the autocorrelation matrix. Therefore, IWF can be used as a numerically stable RLS. This paper proposes ℓ 1 -norm IWF for sparse channel estimation using the IWF and ℓ 1 -norm. The algorithm proposed in this paper includes a realistic regularization coefficient calculation that does not require actual channel information. The simulation shows that the sparse channel estimation performance of the proposed algorithm is similar to the conventional ℓ 1 -norm full-recursive RLS using real channel information as well as being superior in terms of numerical stability. [ABSTRACT FROM AUTHOR]

In reality, the range of sensor response is limited in many sensor systems due to the saturation characteristics of the sensor. That is, the value exceeding the sensor response range is not observed. Using traditional adaptive algorithms to identify the system of this type may lead to the performance degradation. To address this problem, the censored regression algorithms have been proposed. However, when the mixed sub-Gaussian and super-Gaussian/impulsive noises occur, these algorithms may fail to work. To overcome these drawbacks, a family of robust M-shaped (FRMS) functions for censored regression (CR-FRMS) is proposed in this paper. When the system to be identified exhibits a certain degree of sparsity, the CR-FRMS algorithm cannot fully utilize the characteristics of the sparse system. Therefore, in this paper, proportionate FRMS (PFRMS) algorithm based on l 0 -norm constraint for censored regression ( l 0 -CRPFRMS) is also proposed accordingly. The simulations using Gaussian white noise as the input signal and the non-Gaussian mixed noise as the background noise show that the proposed algorithm performs better than other algorithms. [ABSTRACT FROM AUTHOR]

Estimation in a cooperative and distributed manner in wireless sensor networks (WSNs) has considered much attention in recent years. When this distributed estimation is performed adaptively, the concept of adaptive networks will develop. In such networks, proper selection of the unknown parameter length is an issue in itself. A deficient filter length results in an additional steady-state error while selecting a large length will impose a more computational load on the nodes, which is critical in sensor networks due to the lack of energy resources. This motivates the use of variable tap-length adaptive filters in the context of the adaptive networks. This has been achieved in adaptive networks using the distributed fractional tap-length (FT) algorithm. This algorithm requires proper selection of the length adaptation parameters, such as error width and length adaptation step-size. This paper proposes an automatic method for selecting these parameters. In the proposed method, these parameters are adapted based on the estimated gradient vector. The proposed method is fully distributed and presented in a diffusion strategy. Simulation results show that the proposed algorithm has both the advantage of fast length convergence and an unbiased steady-state tap-length. [ABSTRACT FROM AUTHOR]

A filtering algorithm based on frequency domain spline type, frequency domain spline adaptive filters (FDSAF), effectively reducing the computational complexity of the filter. However, the FDSAF algorithm is unable to suppress non-Gaussian impulsive noises. To suppression non-Gaussian impulsive noises along with having comparable operation time, a maximum correntropy criterion (MCC) based frequency domain spline adaptive filter called frequency domain maximum correntropy criterion spline adaptive filter (FDSAF-MCC) is developed in this paper. Further, the bound on learning rate for convergence of the proposed algorithm is also studied. And through experimental simulations verify the effectiveness of the proposed algorithm in suppressing non-Gaussian impulsive noises. Compared with the existing frequency domain spline adaptive filter, the proposed algorithm has better performance. [ABSTRACT FROM AUTHOR]

This paper aims to extend the proportionate adaptation concept to the design of a class of diffusion normalized subband adaptive filter (DNSAF) algorithms. This leads to four extensions of the algorithm associated with different step-size variations, namely diffusion proportionate normalized subband adaptive filter (DPNSAF), diffusion μ -law PNSAF (DMPNSAF), diffusion improved PNSAF (DIPNSAF) and diffusion improved IPNSAF (DIIPNSAF). Subsequently, steady-state performance, stability conditions and computational complexity of the proposed algorithms are investigated. For each extension the performance has been evaluated using both real and simulated data, where the outcomes demonstrate the accuracy of the theoretical expressions and effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

To better perform distributed estimation, this paper, by combining the Fair cost function and adapt-then-combine scheme at all distributed network nodes, a novel diffusion adaptive estimation algorithm is proposed from an M-estimator perspective, which is called the diffusion Fair (DFair) adaptive filtering algorithm. The stability of the mean estimation error and the computational complexity of the DFair are theoretically analyzed. Compared with the robust diffusion LMS (RDLMS), diffusion Normalized Least Mean M-estimate (DNLMM), diffusion generalized correntropy logarithmic difference (DGCLD), and diffusion probabilistic least mean square (DPLMS) algorithms, the simulation experiment results show that the DFair algorithm is more robust to input signals and impulsive interference. In conclusion, Theoretical analysis and simulation results show that the DFair algorithm performs better when estimating an unknown linear system in the changeable impulsive interference environments. [ABSTRACT FROM AUTHOR]

The widely linear model has attracted much attention due to its good features for non-circular adaptive signal processing in recent years. In this paper, a sparsity-induced augmented complex-valued NLMS algorithm is proposed to promote the performance of the adaptive filter for estimating sparse systems, which is established by incorporating the l 0 -norm regularization into the squared error normalized by the input vector. To address the problem of trade-off between fast convergence rate and low steady-state misalignment, we minimize the variance of the a posteriori error to derive an optimal step-size and then some practical problems are considered. Simulation results are provided to verify the superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Due to its low computational burden, the affine-projection-like (APL) adaptive filtering algorithm has been extensively studied for colored signal input. Recently, a robust APL algorithm was designed by adopting the M-estimate cost function in impulsive noise environment; however, its convergence rate is very slow for sparse system identification. This paper proposed a proportionate APL M-estimate (PAPLM) algorithm, which is derived by using the proportionate matrix to heighten the convergence rate. To maintain good steady-state performance of the PAPLM algorithm, a time-varying parameter PAPLM (TV-PAPLM) algorithm is proposed, which uses a modified exponential function to adjust the time-varying parameter according to the ratio of the mean square score function to the system noise variance. Moreover, the steady-state excess mean-square error performance of PAPLM algorithm is analyzed and obtained in detail. Simulation results reveal that the proposed PAPLM and TV-PAPLM algorithms achieve fast convergence rate and good steady-state performance in sparse system identification. [ABSTRACT FROM AUTHOR]

This paper proposes a normalized subband spline adaptive filter (SAF-NSAF) algorithm to solve the problem that linear subband adaptive filtering cannot identify nonlinear systems. The weight update of the proposed algorithm is conducted using the principle of minimum disturbance. Since a delayless structure is used in the proposed algorithm, a delay is not introduced into the update process. The effectiveness of the proposed algorithm is verified by simulations. Also, the mean and mean square stability of the proposed algorithm are evaluated using the principle of conservation of energy. The simulation results demonstrate that the performance of the proposed algorithm outperforms other cited nonlinear algorithms. [ABSTRACT FROM AUTHOR]

In the recent past, a new set of digital signal processing algorithms are developed called short word length (SWL) DSP systems to mitigate the multiplier complexity that is an inherent part in most DSP functions. In SWL algorithms, the critical element is sigma-delta modulation (SDM). In this paper, we present the design, implementation, and hardware synthesis of the FIR filter and adaptive algorithms with the conventional and proposed multiplier schemes. Two proposed short word length adaptive algorithms namely Wiener and Steepest-Descent are compared with their counterpart LMS algorithm using conventional and proposed multiplier schemes. The hardware synthesize of these algorithms is done using Xilinx Spartan-6 and Vertex-7 FPGA and comparison is done based on area-performance-power. The overall results show that the sigma-delta modulation based adaptive DSP algorithm outperforms and is an efficient approach for mobile communication. [ABSTRACT FROM AUTHOR]

In this paper, the improved wavelet transform domain least mean squares (IWTDLMS) adaptive algorithm is established. The IWTDLMS algorithm has a faster convergence speed than the conventional WTDLMS for colored input signals. Since the performances of WTDLMS and IWTDLMS are degraded in impulsive noise interference, the IWTDLMS sign algorithm (IWTDLMS-SA) is proposed. In comparison with IWTDLMS, the IWTDLMS-SA has lower computational complexity. In order to improve the performance of IWTDLMS-SA, the variable step-size IWTDLMS-SA (VSS-IWTDLMS-SA) is introduced. The VSS-IWTDLMS-SA is derived by minimizing the ℓ 1 -norm of the a posteriori error vector. To increase the tracking ability of the VSS-IWTDLMS-SA, the modified VSS-IWTDLMS-SA (MVSS-IWTDLMS-SA)is presented. The simulation results demonstrate that the proposed algorithms have a faster convergence rate and lower misadjustment than the conventional WTDLMS. The robustness feature of the IWTDLMS-SA, VSS-IWTDLMS-SA, and MVSS-IWTDLMS-SA against impulsive noises is also verified through several experiments in a system identification setup. [ABSTRACT FROM AUTHOR]

The feature least-mean-square (F-LMS) algorithm has already been introduced to exploit hidden sparsity in lowpass and highpass systems. In this paper, by proposing the extended F-LMS (EF-LMS) algorithm, we boosted the F-LMS algorithm to exploit hidden sparsity in more general systems, those which are neither lowpass nor highpass. To this end, by means of the so-called feature matrix, we reveal the hidden sparsity in coefficients and utilize the l 1 -norm to exploit the exposed sparsity. As a result, the EF-LMS algorithm will improve the convergence rate and the steady-state mean-squared error (MSE) as compared to the traditional least-mean-square algorithm. Moreover, in this work, we analyze the convergence behavior of the coefficient vector and the steady-state MSE performance of the EF-LMS algorithm. Through synthetic and real-world experiments, it has been seen that the EF-LMS algorithm can improve the convergence rate and the steady-state MSE whenever the hidden sparsity is revealed. [ABSTRACT FROM AUTHOR]

Partial-update (PU) algorithms offer reduced computational complexity to adaptive second-order Volterra filters (SOV) in nonlinear systems while retaining acceptable performance. In this paper, a new selective partial-update technique for the normalized LMS (NLMS) SOV algorithm is proposed, which requires lesser number of comparison operations per iteration than existing methods while providing close performance to the standard M-Max NLMS-SOV algorithm. Convergence properties of the proposed algorithm are enhanced by making the algorithm step-size time varying based on the natural logarithm function. Simulation experiments compare the proposed algorithm with existing PU and variable step-size NLMS-SOV algorithms, which illustrate the advantageous properties of the new algorithm. The proposed algorithm achieves both lower steady-state misalignment and faster convergence speed when compared with the fixed step-size full-update NLMS-SOV algorithm. Simulations also show that comparison operations overhead of the proposed algorithm is reduced significantly compared to that of the standard M-Max NLMS-SOV algorithm. [ABSTRACT FROM AUTHOR]

Subband adaptive filtering algorithms can increase the convergence rate of system identification tasks when the input signal is colored. Recently, a new normalized subband adaptive filtering algorithm with sparse subfilters (NSAF-SF) has been proposed, whose main advantage is the lower computational complexity when compared to state-of-the-art subband approaches, while maintaining similar convergence performance. In this paper, the first- and second-order stochastic analyses of the NSAF-SF algorithm are presented in order to provide predictions about its transient and steady-state performances. A relationship between the adaptive subband coefficients and the ideal fullband transfer function is derived, and the algorithm is proven to produce an asymptotically unbiased solution. In addition, a closed-form expression is obtained for the steady-state mean square deviation (MSD) of the subfilter coefficients. Although the proposed analyses use conventional assumptions of statistical independence, they do not assume a specific stochastic characteristic for the input signal (e.g., Gaussianity or whiteness). Transient and steady-state theoretical predictions of the MSD are confirmed by simulations. [ABSTRACT FROM AUTHOR]

In this paper, we would suggest a sparse adaptive filtering technique which is robust against Gaussian and non-Gaussian noises. To this goal, a linear combination of the least mean square and the least mean fourth loss functions has been considered as the fidelity term. Moreover, in order to promote the sparsity property of the underlying vector, we have added different sparsity-inducing penalty terms. To optimize the resultant cost function, the quasi-Newton scheme has been adopted which accelerates the convergence of the algorithm. The convergence of the proposed method has been proved analytically. Furthermore, the efficiency of the suggested scheme has been evaluated through extensive simulation scenarios which confirm the superiority of the proposed algorithm over the other state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

This paper deals with the Tobit Kalman filtering (TKF) process when the measurements are correlated and censored. The case of interval censoring, i.e., the case of measurements which belong to some interval with given censoring limits, is considered. Two improvements of the standard TKF process are proposed, in order to estimate the hidden state vectors. Firstly, the exact covariance matrix of the censored measurements is calculated by taking into account the censoring limits. Secondly, the probability of a latent (normally distributed) measurement to belong in or out of the uncensored region is calculated by taking into account the Kalman filter residual. The designed algorithm is tested using both synthetic and real data sets. The real data set includes human skeleton joints' coordinates captured by the Microsoft Kinect II sensor. In order to cope with certain real-life situations that cause problems in human skeleton tracking, such as (self)-occlusions, closely interacting persons, etc., adaptive censoring limits are used in the proposed TKF process. Experiments show that the proposed method outperforms other filtering processes in minimizing the overall root-mean-square error for synthetic and real data sets. [ABSTRACT FROM AUTHOR]

This paper develops an efficient adaptive filtering algorithm for active impulsive noise control (AINC) systems. For AINC systems, the filtered-x least mean square (FxLMS) algorithm fails to converge due to the impulsive nature of the noise source. In previous work, the step-size of the FxLMS algorithm was normalized using the power estimate of the error as well as the reference signals, resulting in the improved normalized step-size FxLMS (INSS-FxLMS) algorithm. The INSS-FxLMS algorithm exhibits a robust performance for AINC systems; however, it uses a preselected fixed step-size. Therefore, the INSS-FxLMS algorithm results in a compromise between convergence speed and noise reduction. The proposed algorithm employs a convex-combined step-size (CCSS) within the framework of the INSS-FxLMS algorithm. While normalization takes care of the impulsive nature of noise, the CCSS solves the above-mentioned trade-off issue. Essentially, the CCSS selects a large (small) value of the step-size in the transient (steady) state of the AINC system. It is demonstrated by extensive computer simulations that the proposed algorithm outperforms the existing counterparts for a variety of case studies in AINC systems. [ABSTRACT FROM AUTHOR]

This paper proposes a prefiltered observation-based adaptive filter algorithm that is robust against impulsive noise. Previous impulsive noise rejection algorithms were based on output error stochastic, so there was a trade-off relationship between impulsive noise detection and tracking performances. The proposed rejection algorithm is derived by using the statistics of the observed signal and the inequality such as the Schwarz and Young inequality in the absence of impulsive noise. From this, the proposed algorithm updates the weight vector only when the observed signal is not corrupted by impulsive noise. The proposed algorithm achieves the good tracking performance because it distinguishes between the system change and interruption of impulsive noise. In addition, the proposed algorithm has same performance without impulsive noise, compared with the normalized least-mean-square-type algorithm. Further, the proposed rejection algorithm could expand to various adaptive filtering structures, which suffer the performance degradation with impulsive noise, because it is easy to implement. Hence, the proposed algorithm is combined with the NLMS algorithm for dispersive systems and the proportionate NLMS algorithm for sparse systems. Simulation results show that the proposed algorithm achieves fast convergence rate, good tracking performance, and robustness under the impulsive noise environment. [ABSTRACT FROM AUTHOR]

It is known that the conventional adaptive filtering algorithms can have good performance for non-sparse systems identification, but unsatisfactory performance for sparse systems identification. The normalized least mean absolute third (NLMAT) algorithm which is based on the high-order error power criterion has a strong anti-jamming capability against the impulsive noise, but reduced estimation performance in case of sparse systems. In this paper, several sparse NLMAT algorithms are proposed by inducing sparse-penalty functions into the standard NLMAT algorithm in order to exploit the system sparsity. Simulation results are given to validate that the proposed sparse algorithms can achieve a substantial performance improvement for a sparse system and robustness to impulsive noise environments. [ABSTRACT FROM AUTHOR]

This paper presents a novel affine projection sign subband adaptive filter (NAPSSAF) which could achieve better performance than the conventional APSSAF. The proposed NAPSSAF is obtained by solving the optimal problems regarding the l1-norm of the subband a posteriori error vectors rather than overall a posteriori error vector, which fully uses the subband adaptive filter's inherent decorrelating property. However, since the NAPSSAF algorithm uses the fixed step size, it has an inherent trade-off between low steady-state error and fast convergence rate. Thus, we also propose a combined step size NAPSSAF (CSS-NAPSSAF) to further improve the performance of the NAPSSAF. Finally, simulations are carried out to exhibit the advantages of the NAPSSAF and CSS-NAPSSAF algorithms. The results of simulations demonstrate that the NAPSSAF is superior to the existing algorithms. Besides, the results of simulations also exhibit the improved performance of the CSS-NAPSSAF compared to NAPSSAF. [ABSTRACT FROM AUTHOR]

In this paper, two arctangent-based normalized subband adaptive filter (Arc-NSAF) algorithms are proposed, which are derived from two different forms of arctangent cost function and their difference is the order of the arctangent computation and the summation operation of the normalized subband error signal. Benefiting from the excellent property of the arctangent function for suppressing impulsive interferences, the proposed Arc-NSAF algorithms not only possess good robustness against impulsive interferences, but also obtain faster convergence rate for the colored input signals. To further improve the convergence performance of the Arc-NSAFs when identifying sparse system, the proportionate versions of the Arc-NSAF algorithms are also proposed. Simulation results have demonstrated the superiority of the proposed algorithms in impulsive interferences environments as compared to their respective competitors. [ABSTRACT FROM AUTHOR]

Variable step size norm-constrained adaptive filtering algorithms are proposed in the paper. A variable step size is derived by minimizing the variance of the noise-free a posterior error. Thus, the update equation can obtain a reasonable step size at each iteration. Due to the introduction of variable step size, the proposed algorithms based on the constrained conditions of $$L_{1}$$ and $$L_{0}$$ norm have a significant advantage that the convergence rate is faster than some well-known algorithms in the sparse system. The simulation results illustrate the good performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

A novel normalized subband adaptive filter algorithm with combined step size is proposed for acoustic echo cancellation, which is derived by utilizing a variable mixing parameter to combine a large step size and a small one, thus providing fast convergence rate and small steady-state error. The mixing parameter is indirectly updated by utilizing the stochastic gradient method which minimizes the sum of squared subband errors. Simulation results demonstrate the superiority of the proposed algorithm in terms of the convergence rate and steady-state error as compared to other algorithms mentioned in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a new proportionate normalized subband adaptive filter (PNSAF) algorithm, called the IAF-PNSAF, which assigns an individual activation factor for each filter coefficient instead of a common one for all filter coefficients as in the standard PNSAF algorithm. This algorithm achieves an improved performance in terms of the convergence rate and tracking capability for identifying a highly sparse system. Moreover, to overcome the trade-off of the fixed-step-size IAF-PNSAF algorithm between the fast convergence rate and small steady-state misalignment, its adaptive combination version is proposed. Simulation results for identifying the impulse response with high sparseness have demonstrated that the proposed algorithms outperform their counterparts. [ABSTRACT FROM AUTHOR]

To overcome the performance degradation of adaptive filtering algorithms in the presence of impulsive noise, a novel normalized sign algorithm (NSA) based on a convex combination strategy, called NSA-NSA, is proposed in this paper. The proposed algorithm is capable of solving the conflicting requirement of fast convergence rate and low steady-state error for an individual NSA filter. To further improve the robustness to impulsive noises, a mixing parameter updating formula based on a sign cost function is derived. Moreover, a tracking weight transfer scheme of coefficients from a fast NSA filter to a slow NSA filter is proposed to speed up the convergence rate. The convergence behavior and performance of the new algorithm are verified by theoretical analysis and simulation studies. [ABSTRACT FROM AUTHOR]

In this paper an $$\ell _1$$ -regularized recursive total least squares (RTLS) algorithm is considered for the sparse system identification. Although recursive least squares (RLS) has been successfully applied in sparse system identification, the estimation performance in RLS based algorithms becomes worse, when both input and output are contaminated by noise (the error-in-variables problem). We proposed an algorithm to handle the error-in-variables problem. The proposed $$\ell _1$$ -RTLS algorithm is an RLS like iteration using the $$\ell _1$$ regularization. The proposed algorithm not only gives excellent performance but also reduces the required complexity through the effective inversion matrix handling. Simulations demonstrate the superiority of the proposed $$\ell _1$$ -regularized RTLS for the sparse system identification setting. [ABSTRACT FROM AUTHOR]

In this paper, a new model for adaptive artifact cancelation in impedance cardiography (ICG) signals is presented. It is a hybrid model based on wavelet decomposition and an adaptive filter. A novel feature of this model is the implementation of reference-free adaptive artifact cancellers (AAC). For this implementation, the reference signal is constructed using a wavelet transformation. During critical conditions the filter weights may be negative and cause an imbalance in the convergence. To overcome this problem, we introduce non-negative adaptive algorithms in the proposed artifact canceller. To accelerate the performance of the AAC, we propose exponential non-negative and normalized non-negative algorithms to update the filter coefficients. The computational complexity of the filtering section in a remote health care system is important to avoid inter-symbol interference of the incoming samples. This can be achieved by combining sign-based algorithms with the adaptive filtering section. Finally, several AACs are developed using variants of the non-negative algorithms and performance measures are computed and compared. All of the proposed AACs are tested on actual ICG signals. Among the AACs evaluated, sign regressor normalized non-negative LMS (SRNLMS) based adaptive artifact canceller achieves highest signal to noise ratio (SNR). The SNR achieved by this algorithm in baseline wander artifact elimination is 8.5312 dBs, in electrode muscle artifact elimination is 7.5908 dBs and in impedance measurement artifact elimination is 8.4231 dBs. [ABSTRACT FROM AUTHOR]

The recently proposed distributed incremental least mean-square (DILMS) adaptive networks assume that the length of the adaptive filter at each node is equal to that of the unknown parameter; in other words, a sufficient length adaptive filter is assumed for each node. However, in many practical situations, the length of the employed adaptive filter at each node is less than that of the unknown parameter. In other words, at each node, a deficient length adaptive filter is employed. Since the analysis results for the sufficient length DILMS algorithm are not necessarily applicable to the deficient length case, so in this paper, we extend existing analysis to study the performance of the DILMS algorithm in this realistic case. More precisely, we derive a closed-form expression for the mean-square deviation (MSD) to explain the steady-state performance at each individual node. Simulation results support the theoretical analysis. [ABSTRACT FROM AUTHOR]