Comparison of twosensor tracking methods based on state vector fusion and measurement fusion
Abstract
There are two approaches to the twosensor trackfusion problem. BarShalom and Campo (1986) presented the state vector fusion method, which combines state vectors from the two sensors to form a new estimate while taking into account the correlated process noise. The measurement fusion method or data compression of Willner et al. (1976) combines the measurements from the two sensors first and then uses this fused measurement to estimate the state vector. The two methods are compared and an example shows the amount of improvement in the uncertainty of the resulting estimate of the state vector with the measurement fusion method.
 Publication:

IEEE Transactions on Aerospace Electronic Systems
 Pub Date:
 July 1988
 DOI:
 10.1109/7.7186
 Bibcode:
 1988ITAES..24..447R
 Keywords:

 Kalman Filters;
 Multisensor Applications;
 State Vectors;
 Tracking Problem;
 Covariance;
 Matrices (Mathematics);
 Recursive Functions;
 Communications and Radar