A new approach for target localization using Maximum Likelihood Estimation in MIMO radar

2011 ◽  
Author(s):  
Mojtaba Dianat ◽  
Mohammad Reza Taban ◽  
Ali Akbar Tadaion
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Mimo Radar ◽  
Target Localization ◽  
New Approach

scholarly journals Plotting positions via maximum-likelihood for a non-standard situation

1997 ◽  
Vol 1 (2) ◽  
pp. 357-366 ◽  
Author(s):  
D. A. Jones
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Frequency Analysis ◽  
Likelihood Estimation ◽  
Similar Data ◽  
Statistical Distributions ◽  
Annual Maximum ◽  
New Approach ◽  
Special Cases ◽  
Standard Situation

Abstract. A new approach is developed for the specification of the plotting positions used in the frequency analysis of extreme flows, rainfalls or similar data. The approach is based on the concept of maximum likelihood estimation and it is applied here to provide plotting positions for a range of problems which concern non-standard versions of annual-maximum data. This range covers the inclusion of incomplete years of data and also the treatment of cases involving regional maxima, where the number of sites considered varies from year to year. These problems, together with a not-to-be-recommended approach to using historical information, can be treated as special cases of a non-standard situation in which observations arise from different statistical distributions which vary in a simple, known, way.

A New Approach to Estimating Populations by the Removal Method

1983 ◽  
Vol 40 (12) ◽  
pp. 2153-2169 ◽  
Author(s):  
Jon Schnute
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
New Brunswick ◽  
Population Size ◽  
Confidence Intervals ◽  
Likelihood Estimation ◽  
Data Interpretation ◽  
Population Estimate ◽  
New Approach ◽  
Removal Method

This paper presents a new approach to the use of removal data in estimating the size of a population of fish or other animals. The theory admits a variety of assumptions on how catchability varies among fishings including the assumption of constant catchability, which underlies most previous work. The methods here hinge on maximum likelihood estimation, and they can be used both to decide objectively if the data justify rejecting constant catchability and to determine confidence intervals for the parameters. The work includes a new method of assigning confidence to the population estimate and points out problems with methods currently available in the literature, even in the case of constant catchability. The theory is applied both to data in historical literature and to more recent data from streams in New Brunswick, Canada. These examples demonstrate that the assumption of constant catchability can frequently lead to serious errors in data interpretation. In some cases, the conclusion that the population size is well known may be blatantly false, and reasonable estimates may be impossible without further data.

Maximum likelihood estimation of DOD and DOA for bistatic MIMO radar

2013 ◽  
Vol 93 (5) ◽  
pp. 1349-1357 ◽  
Author(s):  
Bo Tang ◽  
Jun Tang ◽  
Yu Zhang ◽  
Zhidong Zheng
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Mimo Radar ◽  
Bistatic Mimo Radar
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