Robust and sparsity-aware adaptive filters: A Review

Kumar, K. Suresh; Pandey, Rajlaxmi; Karthik, Markala; Bhattacharjee, Sankha Subhra; George, Nithin V.

doi:10.1016/j.sigpro.2021.108276

Cited by 65 publications

(5 citation statements)

References 293 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, PI algorithm using fixed step size must compromise between fast convergence rate and low level of steady-state MSE. Scholars have proposed many algorithms to reduce the steady state error with computational complexity [6][7][8][9][10][11][12][13] . Khaled Mayyas [6] proposed a VSS selective partial update LMS algorithm, to reduce computational complexity by updating only a fraction of the adaptive filter coefficients during each iteration.…”

Section: Introductionmentioning

confidence: 99%

“…Yao Jiang et al [8] proposed an adaptive step-size approach, which utilized an improved particle swarm optimization (IPSO) algorithm to determine the key parameters for setting specific step-size adjustment strategies, but the algorithm complexity was large, which is not suitable for RF front-end. Zhang et al Krishna Kumar et al [9] introduced a family of VSS-LMS algorithms that utilize logarithmic, hyperbolic, and sigmoid cost functions, resulting in robust performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

FPGA Implementation of Variable Step Power Inversion Array for BeiDou Receiver

Zhuoya,

Shuyan,

Yalun

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Given the susceptibility of satellite signals to intentional or unintentional interference; it is imperative to incorporate hardware-based navigation anti-jamming algorithms. Currently, spatial domain filtering is widely applied. While numerous variable step size (VSS) algorithms were used to improve the performance, most of these are either computationally complex or depend on many parameters which are hard to tune manually. Some algorithms might improve one index but worsen overall performance, making them unreliable in practical scenarios. To address these challenges, we propose a novel variable step power inversion (PI) that involves just one adaptive parameter. By establishing a new non-linear function model between step size and system output, the parameter can be adapted based on the output. Meanwhile, a variable step PI that can be easily implemented by field programmable gate array (FPGA) are proposed, thereby achieving a balance between accuracy and iteration speed. The proposed algorithm can converge within 10 iterations, effectively suppressing individual interference to below -300dB. In comparison to existing algorithms, the proposed algorithm is characterized by swift convergence, high convergence accuracy, low complexity, and robust anti-jamming capabilities. Finally, FPGA hardware testing are conducted, and the experimental and simulation results are in good agreement, affirming anti-jamming capabilities.INDEX TERMS power inversion array; variable step size (VSS); navigation anti-jamming; FPGA

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of Variable Step Power Inversion Array for BeiDou Receiver

Zhuoya,

Shuyan,

Yalun

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Typical examples include the

-norm with

[ 9 ], the M -estimation theory [ 10 ], and the information theoretic learning (ITL) family [ 11 ]. More details about robust adaptive signal processing schemes can be seen in a review article [ 12 ]. In the family of ITL criteria, thanks to all the even-order moment information of the error signal contained in the minimization of error entropy (MEE) [ 13 ] and the maximum correntropy criterion (MCC) [ 14 , 15 ], they are widely used in robust signal processing and machine learning.…”

Section: Introductionmentioning

confidence: 99%

Newton Recursion Based Random Data-Reusing Generalized Maximum Correntropy Criterion Adaptive Filtering Algorithm

Zhao

Qiao

et al. 2022

Entropy

View full text Add to dashboard Cite

For system identification under impulsive-noise environments, the gradient-based generalized maximum correntropy criterion (GB-GMCC) algorithm can achieve a desirable filtering performance. However, the gradient method only uses the information of the first-order derivative, and the corresponding stagnation point of the method can be a maximum point, a minimum point or a saddle point, and thus the gradient method may not always be a good selection. Furthermore, GB-GMCC merely uses the current input signal to update the weight vector; facing the highly correlated input signal, the convergence rate of GB-GMCC will be dramatically damaged. To overcome these problems, based on the Newton recursion method and the data-reusing method, this paper proposes a robust adaptive filtering algorithm, which is called the Newton recursion-based data-reusing GMCC (NR-DR-GMCC). On the one hand, based on the Newton recursion method, NR-DR-GMCC can use the information of the second-order derivative to update the weight vector. On the other hand, by using the data-reusing method, our proposal uses the information of the latest M input vectors to improve the convergence performance of GB-GMCC. In addition, to further enhance the filtering performance of NR-DR-GMCC, a random strategy can be used to extract more information from the past M input vectors, and thus we obtain an enhanced NR-DR-GMCC algorithm, which is called the Newton recursion-based random data-reusing GMCC (NR-RDR-GMCC) algorithm. Compared with existing algorithms, simulation results under system identification and acoustic echo cancellation are conducted and validate that NR-RDR-GMCC can provide a better filtering performance in terms of filtering accuracy and convergence rate.

show abstract

“…In NLMS, the step size is used to normalize

l_{2}

‐norm of the input signal vector in LMS with substantial fluctuation [12]. Numerous sparse algorithms for adaptive filters, such as proportionate NLMS (PNLMS) algorithms [11] and the zero‐attracting (ZA) algorithm [13, 14], have been proposed to exploit this sparsity.…”

Section: Introductionmentioning

confidence: 99%

An improved sparsity‐aware normalized least‐mean‐square scheme for underwater communication

Kumar

2022

ETRI Journal

View full text Add to dashboard Cite

Underwater communication (UWC) is widely used in coastal surveillance and early warning systems. Precise channel estimation is vital for efficient and reliable UWC. The sparse direct‐adaptive filtering algorithms have become popular in UWC. Herein, we present an improved adaptive convex‐combination method for the identification of sparse structures using a reweighted normalized least‐mean‐square (RNLMS) algorithm. Moreover, to make RNLMS algorithm independent of the reweighted l1‐norm parameter, a modified sparsity‐aware adaptive zero‐attracting RNLMS (AZA‐RNLMS) algorithm is introduced to ensure accurate modeling. In addition, we present a quantitative analysis of this algorithm to evaluate the convergence speed and accuracy. Furthermore, we derive an excess mean‐square‐error expression that proves that the AZA‐RNLMS algorithm performs better for the harsh underwater channel. The measured data from the experimental channel of SPACE08 is used for simulation, and results are presented to verify the performance of the proposed algorithm. The simulation results confirm that the proposed algorithm for underwater channel estimation performs better than the earlier schemes.

show abstract

Robust and sparsity-aware adaptive filters: A Review

Cited by 65 publications

References 293 publications

FPGA Implementation of Variable Step Power Inversion Array for BeiDou Receiver

FPGA Implementation of Variable Step Power Inversion Array for BeiDou Receiver

Newton Recursion Based Random Data-Reusing Generalized Maximum Correntropy Criterion Adaptive Filtering Algorithm

An improved sparsity‐aware normalized least‐mean‐square scheme for underwater communication

Contact Info

Product

Resources

About