Received Signal Strength-Based Emitter Geolocation Using an Iterative Maximum Likelihood Approach

2013 ◽  
Author(s):  
Sichun Wang ◽  
Brad R. Jackson ◽  
Sreeraman Rajan ◽  
Francois Patenaude
Keyword(s):  
Maximum Likelihood ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Maximum Likelihood Approach ◽  
Strength Based ◽  
Likelihood Approach ◽  
Iterative Maximum Likelihood

scholarly journals Flexible Birnbaum–Saunders Distribution

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1305 ◽  
Author(s):  
Guillermo Martínez-Flórez ◽  
Inmaculada Barranco-Chamorro ◽  
Heleno Bolfarine ◽  
Héctor W. Gómez
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Environmental Sciences ◽  
Maximum Likelihood Approach ◽  
Special Cases ◽  
Competing Models ◽  
Likelihood Approach ◽  
Iterative Maximum Likelihood ◽  
Extra Parameter ◽  
Better Than

In this paper, we propose a bimodal extension of the Birnbaum–Saunders model by including an extra parameter. This new model is termed flexible Birnbaum–Saunders (FBS) and includes the ordinary Birnbaum–Saunders (BS) and the skew Birnbaum–Saunders (SBS) model as special cases. Its properties are studied. Parameter estimation is considered via an iterative maximum likelihood approach. Two real applications, of interest in environmental sciences, are included, which reveal that our proposal can perform better than other competing models.

2.4 GHz RF Received Signal Strength Based Node Separation in WSN Monitoring Infrastructure for Millet and Rice Vegetation

2021 ◽  
pp. 1-1
Author(s):  
Pankaj Pal ◽  
Rashmi Priya Sharma ◽  
Sachin Tripathi ◽  
Chiranjeev Kumar ◽  
Dharavath Ramesh
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Strength Based

Emotion detection in infants² cries based on a maximum likelihood approach

2006 ◽  
Author(s):  
S. Matsunaga ◽  
S. Sakaguchi ◽  
M. Yamashita ◽  
S. Miyahara ◽  
S. Nishitani ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Emotion Detection ◽  
Maximum Likelihood Approach ◽  
Likelihood Approach
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