Title :
Track association and track fusion with nondeterministic target dynamics
Author :
Mori, Shozo ; Barker, W.H. ; Chee-Yee Chong ; Kuo-Chu Chang
fDate :
4/1/2002 12:00:00 AM
Abstract :
Representative track fusion algorithms and track association metrics are quantitatively compared using a simple linear-Gaussian-Poisson model, under various degrees of nondeterministicity of the target dynamics, i.e., process noises, and of the initial condition uncertainty. Track fusion algorithms are compared using an analytical method, while track association metrics are evaluated by Monte Carlo simulations.
Keywords :
Gaussian distribution; Kalman filters; Monte Carlo methods; Poisson distribution; distributed tracking; filtering theory; maximum likelihood estimation; sensor fusion; target tracking; tracking filters; Bar-Shalom-Campo algorithm; Monte Carlo simulations; additive zero-mean Gaussian noise; convex combination; covariance matrix; distributed data fusion architecture; distributed filtering; distributed tracking; initial condition uncertainty; linear-Gaussian-Poisson model; local Kalman filter; maximum a posteriori probability density estimate; nondeterministic target dynamics; performance comparison; process noises; tactical data fusion; track association metrics; track fusion algorithms; track-to-track association; tracklet fusion; Adaptive control; Algorithm design and analysis; Covariance matrix; Data processing; Filtering; Programmable control; Sensor fusion; State estimation; Target tracking; Uncertainty;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
DOI :
10.1109/TAES.2002.1008994
