Title :
Architectures and algorithms for track association and fusion
Author :
Chong, Chee-Yee ; Mori, Shozo ; Barker, William H. ; Chang, Kuo-Chu
Author_Institution :
Booz, Allen & Hamilton Inc., San Francisco, CA, USA
fDate :
1/1/2000 12:00:00 AM
Abstract :
Target tracking using multiple sensors can provide better performance than using a single sensor. One approach to multiple target tracking with multiple sensors is to first perform single sensor tracking and then fuse the tracks from the different sensors. Two processing architectures for track fusion are presented: sensor to sensor track fusion, and sensor to system track fusion. Technical issues related to the statistical correlation between track estimation errors are discussed. Approaches for associating the tracks and combining the track state estimates of associated tracks that account for this correlation are described and compared by both theoretical analysis and Monte Carlo simulations
Keywords :
Monte Carlo methods; correlation theory; covariance matrices; decorrelation; deterministic algorithms; distributed algorithms; distributed tracking; sensor fusion; state estimation; target tracking; Monte Carlo simulations; algorithms; bipartite assignment; correlated estimation errors; covariance matrices; decorrelation; deterministic target dynamics; distributed data processing; distributed tracking; global restart; multiple sensors; multiple target tracking; processing architectures; sensor to sensor track fusion; sensor to system track fusion; single sensor tracking; statistical correlation; track association; track estimation errors; track fusion; track state estimates; Computer architecture; Data processing; Estimation error; Fuses; Fusion power generation; Sensor fusion; Sensor systems; State estimation; Systems engineering and theory; Target tracking;
Journal_Title :
Aerospace and Electronic Systems Magazine, IEEE
