Analysis of a Fast Quasi-Newton adaptive filtering algorithm

2006 ◽  
pp. 251-262
Author(s):  
Daniel F. Marshall ◽  
W. Kenneth Jenkins
Keyword(s):  
Adaptive Filtering ◽  
Filtering Algorithm ◽  
Quasi Newton

Improved Quasi-Newton Adaptive-Filtering Algorithm

2010 ◽  
Vol 57 (8) ◽  
pp. 2109-2118 ◽  
Author(s):  
Md Zulfiquar Ali Bhotto ◽  
Andreas Antoniou
Keyword(s):  
Adaptive Filtering ◽  
Filtering Algorithm ◽  
Quasi Newton

A fast quasi-Newton adaptive filtering algorithm

1992 ◽  
Vol 40 (7) ◽  
pp. 1652-1662 ◽  
Author(s):  
D.F. Marshall ◽  
W.K. Jenkins
Keyword(s):  
Adaptive Filtering ◽  
Filtering Algorithm ◽  
Quasi Newton

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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