scholarly journals Analysis of Normalized Orthogonal Gradient Adaptive Algorithm Based on Spline Adaptive Filtering for Smart Communication Technology

2021 ◽  
Author(s):  
Theerayod Wiangtong ◽  
Suchada Sitjongsataporn
Keyword(s):  
Communication Technology ◽  
Adaptive Filtering ◽  
Adaptive Algorithm ◽  
Linear Filter ◽  
Control Points ◽  
Adaptation Algorithm ◽  
Filtering Algorithm ◽  
Gradient Based ◽  
Simulation Results ◽  
Convergence And Stability Analysis

This paper presents a general theoretical framework of spline adaptive filtering based on a normalized version of orthogonal gradient adaptive algorithm. A nonlinear spline adaptive filter normally consists of a linear combination with a memory-less function and a spline function for adaptive approach. We explain how the adaptive linear filter and spline control points are derived in a straightforward iterative gradient-based method. In order to improve the convergence characteristics, the normalized version of orthogonal gradient adaptive algorithm is introduced by the orthogonal projection along with the gradient adaptive algorithm. In addition, a simple form of adaptation algorithm is introduced how to obtain a lower bound on the excess mean square error (MSE) in a theoretical basis. Convergence and stability analysis based on the MSE criterion are proven in terms of the excess MSE. Simulation results reveal that the proposed algorithm achieves more robustness compared with the conventional spline adaptive filtering algorithm.

A Variable Step Size LMS Adaptive Filtering Algorithm and Simulations Based on Computational Verb Theory

2013 ◽  
Vol 756-759 ◽  
pp. 3972-3976 ◽  
Author(s):  
Li Hui Sun ◽  
Bao Yu Zheng
Keyword(s):  
Adaptive Filtering ◽  
Adaptive Algorithm ◽  
Linear Functional ◽  
Functional Relationship ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Filtering Algorithm ◽  
Variable Step ◽  
Simulation Results

Based on traditional LMS algorithm, variable step LMS algorithm and the analysis for improved algorithm, a new variable step adaptive algorithm based on computational verb theory is put forward. A kind of sectorial linear functional relationship is established between step parameters and the error. The simulation results show that the algorithm has the advantage of slow change which is closely to zero. And overcome the defects of some variable step size LMS algorithm in adaptive steady state value is too large.

scholarly journals Robust Adaptive Filtering Algorithm for Self-Interference Cancellation with Impulsive Noise

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 196
Author(s):  
Jun Lu ◽  
Qunfei Zhang ◽  
Wentao Shi ◽  
Lingling Zhang ◽  
Juan Shi
Keyword(s):  
Adaptive Filtering ◽  
Interference Cancellation ◽  
Impulsive Noise ◽  
Weight Vector ◽  
Filtering Algorithm ◽  
Convergence Performance ◽  
Simulation Results ◽  
Robust Adaptive ◽  
Arctangent Cost Function ◽  
Simultaneous Transmission

Self-interference (SI) is usually generated by the simultaneous transmission and reception in the same system, and the variable SI channel and impulsive noise make it difficult to eliminate. Therefore, this paper proposes an adaptive digital SI cancellation algorithm, which is an improved normalized sub-band adaptive filtering (NSAF) algorithm based on the sparsity of the SI channel and the arctangent cost function. The weight vector is hardly updated when the impulsive noise occurs, and the iteration error resulting from impulsive noise is significantly reduced. Another major factor affecting the performance of SI cancellation is the variable SI channel. To solve this problem, the sparsity of the SI channel is estimated with the estimation of the weight vector at each iteration, and it is used to adjust the weight vector. Then, the convergence performance and calculation complexity are analyzed theoretically. Simulation results indicate that the proposed algorithm has better performance than the referenced algorithms.

An Improved Adaptive Algorithm for INS/GPS System

2013 ◽  
Vol 397-400 ◽  
pp. 1606-1610 ◽  
Author(s):  
Li Dong Wang ◽  
Ying Zhao ◽  
Ni Zhang
Keyword(s):  
Adaptive Filtering ◽  
Adaptive Filter ◽  
Adaptive Algorithm ◽  
Initial Conditions ◽  
Forgetting Factor ◽  
Kalman Filter Algorithm ◽  
Simulation Results ◽  
The Stability ◽  
Gps System

In INS/GPS system, the changing of initial conditions and the quality of the data can affect the convergence of the conventional Kalman filter algorithm. Sage-Husa adaptive filter algorithm is adopted in the INS/GPS system in this paper. The effecting of the forgetting factor to the improved Sage-Husa adaptive filter algorithm is studied and the simulation results show that when the forgetting factor taken near 0.97, the adaptive filtering result is best, the stability of the system is guaranteed and the convergent speed of error can be reduced.

An Algorithm of the LMS Adaptive Filtering in Linear Canonical Transform Domain

2013 ◽  
Vol 385-386 ◽  
pp. 1407-1410 ◽  
Author(s):  
Yan Hong Zhang ◽  
Heng Zhao ◽  
Hui Hui Li
Keyword(s):  
Theoretical Analysis ◽  
Adaptive Filtering ◽  
Narrow Band ◽  
Lms Algorithm ◽  
Transform Domain ◽  
Linear Canonical Transform ◽  
Nonstationary Signal ◽  
Filtering Algorithm ◽  
Wideband Signals ◽  
Simulation Results

In allusion to the non-stationary wideband signals, a LMS adaptive filtering algorithm based on linear canonical transform is proposed. In this method, the signal is first transformed to linear canonical transform domain. By using linear canonical transform and selecting appropriate transformation parameters, characteristics of the transformed signal appear to be stationary narrow-band in the corresponding linear canonical transform domain, and then, the transformed signal is filtered adaptively with LMS algorithm in this domain. Theoretical analysis and simulation results show that the algorithm is not only to solve the problem of extracting and filtering of nonstationary signal, and can obtain better filtering performance.

Improved Current Statistic Model and Adaptive Filtering

2005 ◽  
Author(s):  
Hongtao Hu ◽  
Zhongliang Jing
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Statistical Model ◽  
Adaptive Filtering ◽  
Adaptive Algorithm ◽  
Tracking Algorithm ◽  
Unscented Transformation ◽  
Maneuvering Targets ◽  
Statistic Model ◽  
Simulation Results

Current statistical model needs to pre-define the value of maximum accelerations of maneuvering targets. So it may be difficult to meet all maneuvering conditions. In this paper a novel adaptive algorithm for tracking maneuvering targets is proposed. The algorithm is implemented with fuzzy-controlled current statistic model adaptive filtering and unscented transformation. The Monte Carlo simulation results show that this method outperforms the conventional tracking algorithm based on current statistical model.

Simulation Realization of Adaptive Filtering Based on a Normalized LMS Algorithm

2013 ◽  
Vol 373-375 ◽  
pp. 1159-1163
Author(s):  
Ya Feng Li ◽  
Zi Wei Zheng
Keyword(s):  
Adaptive Filtering ◽  
Notch Filter ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Filtering Algorithm ◽  
Adaptive Notch Filter ◽  
Variable Step ◽  
Simulation Results ◽  
Normalized Lms

This paper presents the new algorithm which is an improved normalized variable step size LMS adaptive filtering algorithm. A normalized LMS algorithm with variable step size iterative formula is deduced and at the same time the simulation results prove that the new algorithm has good performance. The LMS adaptive filtering algorithm has been widely used in many applications such as system identification, noise cancellation and the adaptive notch filter ,the paper analyses the application and implement the simulation by matlab. the result shows the proposed algorithm has been applied well.

scholarly journals Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2236
Author(s):  
Sichun Du ◽  
Qing Deng
Keyword(s):  
Particle Filter ◽  
State Space ◽  
Adaptive Filtering ◽  
Divide And Conquer ◽  
Particle Swarm Algorithm ◽  
Tracking Accuracy ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Unscented Particle Filter ◽  
Particle Filter Algorithm

Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

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