Maximum likelihood estimation and Cramer-Rao bounds for direction of arrival parameters of a large sensor array

1996 ◽  
Vol 44 (4) ◽  
pp. 478-491 ◽  
Author(s):  
A. Satish ◽  
R.L. Kashyap
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Sensor Array ◽  
Likelihood Estimation ◽  
Direction Of Arrival

Improving the threshold performance of maximum likelihood estimation of direction of arrival

2010 ◽  
Vol 90 (5) ◽  
pp. 1582-1590 ◽  
Author(s):  
R. Krummenauer ◽  
M. Cazarotto ◽  
A. Lopes ◽  
P. Larzabal ◽  
P. Forster
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Direction Of Arrival

Ultrasonic classification and location of 3D room features using maximum likelihood estimation - Part I

Robotica ◽  
1997 ◽  
Vol 15 (5) ◽  
pp. 483-491 ◽  
Author(s):  
Hong Mun-Li ◽  
Lindsay Kleeman
Keyword(s):  
Kalman Filter ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Horizontal Plane ◽  
Sensor Array ◽  
Likelihood Estimation ◽  
Implicit Assumption ◽  
Likelihood Estimator ◽  
Arrival Times ◽  
Theoretical Results

Current mobile robot ultrasonic localisation techniques use sensor systems which rely on features in a horizontal plane. The implicit assumption is that the room boundary on the horizontal plane is not obstructed by objects such as furniture. This assumption is often not realistic and restricts the versatility and portability of these systems. The solution proposed in this paper is the provision of sensing flexibility to use other 3D room boundaries (e.g. ceiling-wall intersections) as 3D natural beacons. We propose a 3D ultrasonic sensor array that uses a Maximum Likelihood Estimator to match the echo arrival times to different object classes and to determine the location of the 3D target. This method does not require fast data acquisition or powerful computing. It has been implemented on a robot localisation application with the Extended Kalman Filter. This paper is the first of two parts, and presents theoretical results on target classification and minimum transducer requirements. The second part, in the next issue of Robotica, presents experimental results on the characterisation of the sensor and its application to robot localisation, and includes the references for the both papers.

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