Optimal update with out-of-sequence measurements

Optimal update with out-of-sequence measurements

This paper is concerned with optimal filtering in a distributed multiple sensor system with the so-called out-of-sequence measurements (OOSM). Based on best linear unbiased estimation (BLUE) fusion, we present two algorithms for updating with OOSM that are optimal for the information available at th...

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Veröffentlicht in:IEEE transactions on signal processing 2005-06, Vol.53 (6), p.1992-2004
Hauptverfasser: Keshu Zhang, Li, X.R., Yunmin Zhu
Format: Artikel
Sprache:eng
Schlagworte:
Algorithm design and analysis
Algorithms
Analytical models
Applied sciences
Computer simulation
Delay effects
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Filtering
Filters
Filtration
Information, signal and communications theory
Kalman filter
LMMSE
Optimization
out-of-sequence measurement
Sensor fusion
Sensor systems
Sensors
Signal and communications theory
Signal processing
Signal, noise
State estimation
Storage of information
Target tracking
Telecommunications and information theory
Time measurement
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Beschreibung
Zusammenfassung:This paper is concerned with optimal filtering in a distributed multiple sensor system with the so-called out-of-sequence measurements (OOSM). Based on best linear unbiased estimation (BLUE) fusion, we present two algorithms for updating with OOSM that are optimal for the information available at the time of update. Different minimum storages of information concerning the occurrence time of OOSMs are given for both algorithms. It is shown by analysis and simulation results that the two proposed algorithms are flexible and simple.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2005.847830