An Adaptive Fir Filtering Based on Balanced Realization

2013 ◽  
Vol 753-755 ◽  
pp. 2566-2572
Author(s):  
Zheng Hong Deng ◽  
Jian Guo Dang ◽  
Ting Ting Li
Keyword(s):  
Transfer Function ◽  
State Space ◽  
Adaptive Filter ◽  
Finite Impulse Response ◽  
Balanced Realization ◽  
Filtering Algorithm ◽  
Filter Structure ◽  
Fir Filtering ◽  
Attractive Candidate ◽  
Adaptive Fir

Balanced realization is an attractive candidate to design state-space adaptive filter structure due to its least parameter sensitivity. In this paper, based on the balanced realization, an adaptive finite impulse response (FIR) filtering algorithm is proposed to minimize the output-error using the coefficients of the transfer function as the adaptive filter parameters. This algorithm is an internally balanced realization form and guarantees that the designed adaptive FIR filtering always minimizes the ratio of maximum-to-minimum eigenvalues of the Grammian matrices.

Estimation of the clock TIE state space model using a thinning unbiased FIR filtering algorithm

2008 ◽  
Author(s):  
Yuriy S. Shmaliy ◽  
Oscar Ibarra-Manzano ◽  
Luis Arceo-Miquel ◽  
Luis Moralez-Mendoza ◽  
Oleksandr Yu. Shmaliy
Keyword(s):  
State Space ◽  
State Space Model ◽  
Filtering Algorithm ◽  
Space Model ◽  
Fir Filtering

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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