Paper Info
Title
State space least p-power filter.
Abstract
As a new addition to the recursive least squares (RLS) family filters, the state space recursive least squares (SSRLS) filter can achieve desirable performance by conquering some limitations of the standard RLS filter. However, when the system is contaminated by some non-Gaussian noises, the performance of SSRLS will get worse. The main reason for this is that the SSRLS is developed under the well-known minimum mean square error (MMSE) criterion, which is not very suitable for non-Gaussian situations. To address this issue, in this paper, we propose a new state space based linear filter, called the state space least p-power (SSLP) filter, which is derived under the least mean p-power error (LMP) criterion instead of the MMSE. With a proper p value, the SSLP can outperform the SSRLS substantially especially in non-Gaussian noises. Two illustrative examples are presented to show the satisfactory results of the new algorithm. A new state space based filter is derived under the least mean p-power error (LMP) criterion.A novel fixed-point iterative algorithm is applied to update the solution.With a proper parameter, the proposed algorithm can outperform the original state space recursive least squares (SSRLS).Two illustrative examples are presented to show the satisfactory results of the new algorithm.
Year
DOI
Venue
2017
10.1016/j.dsp.2016.12.009
Digital Signal Processing
Keywords
Field
DocType
State space recursive least squares (SSRLS),Least mean p-power (LMP),State space least p-power (SSLP)
Mathematical optimization,Linear filter,Power filter,p-value,Minimum mean square error,State space,Recursive least squares filter,Mathematics
Journal
Volume
Issue
ISSN
63
C
1051-2004
Citations 
PageRank 
References 
2
0.38
17
Authors
5
Name
Order
Citations
PageRank
Xi Liu112220.80
Badong Chen291965.71
Jiuwen Cao3202.95
Bin Xu479343.26
Haiquan Zhao588664.79