DocumentCode :
539149
Title :
Out-of-sequence processing of cluttered sensor data using multiple evolution models
Author :
Govaers, F. ; Koch, W.
Author_Institution :
Inst. of Comput. Sci. 4, Univ. of Bonn, Bonn, Germany
fYear :
2010
fDate :
26-29 July 2010
Firstpage :
1
Lastpage :
7
Abstract :
In target tracking applications, the full information on the kinematic target states accumulated over a certain time window up to the present time is contained in the joint probability density function of these state vectors, given the time series of all sensor data. In the structure of this Accumulated State Density (ASD) has been revealed. Furthermore, ASDs enable us to process Out-of-Sequence (OoS) measurements in a neat and straightforward way. This paper presents an algorithm for the processing of OoS measurements in situations with more relaxed assumptions. On the one hand, sensors often return ambiguous measurement data. Then, measurement association methodologies as the Multi-Hypothesis Tracker (MHT) are required. On the other hand, the evolution model in use might not be unique. The well-known approach to this challenge is the Interacting Multiple Model (IMM) filter. In this paper, an IMM/MHT extension to the ASD paradigm is discussed, tested by simulation, and evaluated.
Keywords :
distributed sensors; probability; target tracking; time series; accumulated state density; cluttered sensor data; interacting multiple model filter; joint probability density function; measurement association methodology; multihypothesis tracker; multiple evolution models; out-of-sequence processing; state vector; target tracking application; time series; Covariance matrix; Equations; Frequency modulation; Kalman filters; Mathematical model; Time measurement; Variable speed drives;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion (FUSION), 2010 13th Conference on
Conference_Location :
Edinburgh
Print_ISBN :
978-0-9824438-1-1
Type :
conf
DOI :
10.1109/ICIF.2010.5711959
Filename :
5711959
Link To Document :
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