Tracking Analyses Of M-Estimate Based LMS And NLMS Algorithms

2021 ◽  
Author(s):  
Yi Yu ◽  
Rodrigo C. de Lamare ◽  
Tao Yang ◽  
Qiangming Cai
Keyword(s):  
M Estimate

Robust information unscented particle filter based on M‐estimate

2019 ◽  
Vol 13 (1) ◽  
pp. 14-20 ◽  
Author(s):  
Xiao‐Hang Wu ◽  
Shen‐Min Song
Keyword(s):  
Particle Filter ◽  
Unscented Particle Filter ◽  
M Estimate

A Comparison of Liquid Scintillation and Geiger–Müller Estimates of Primary Productivity in an in situ Experiment

1973 ◽  
Vol 30 (5) ◽  
pp. 708-711 ◽  
Author(s):  
F. J. Ward ◽  
Masami Nakanishi
Keyword(s):  
Light Intensity ◽  
Surface Area ◽  
Primary Productivity ◽  
Liquid Scintillation ◽  
Lake Biwa ◽  
In Situ Experiment ◽  
M Estimate ◽  
M Estimates ◽  
Loss Rates

For an in situ experiment conducted in Shiozu Bay, Lake Biwa, Japan, primary productivity estimates from liquid scintillation radioactivity counts of wet algae were generally higher than those from Geiger–Müller radioactivity counts of desiccated algae. Values at 0 m were similar, the G–M estimate at 0.5 m was 10% higher, but from 3 to 13 m the liquid scintillation values ranged from 11 to 33% higher than G–M estimates. The 20-m estimates were low and similar. Differences were caused primarily by 14C losses during desiccation prior to G–M counting. Increasing loss rates between 0.5 and 3.0 m may have been caused by decreasing light intensity. On the basis of surface area, the estimate from liquid scintillation data was 27% greater than that obtained from G–M data.

Robust M-estimate adaptive filtering

2001 ◽  
Vol 148 (4) ◽  
pp. 289 ◽  
Author(s):  
Y. Zou ◽  
S.C. Chan ◽  
T.S. Ng
Keyword(s):  
Adaptive Filtering ◽  
M Estimate
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