scholarly journals Kernel adaptive filtering algorithm based on Softpuls function under non-Gaussian impulse interference

2020 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
◽  
Keyword(s):  
Adaptive Filtering ◽  
Filtering Algorithm ◽  
Non Gaussian ◽  
Impulse Interference

scholarly journals Frequency domain maximum correntropy criterion spline adaptive filtering

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wenyan Guo ◽  
Yongfeng Zhi ◽  
Kai Feng
Keyword(s):  
Frequency Domain ◽  
Adaptive Filtering ◽  
Adaptive Filter ◽  
Adaptive Filters ◽  
Operation Time ◽  
Filtering Algorithm ◽  
Impulsive Noises ◽  
Type Frequency ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian

AbstractA 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.

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.

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.

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