Title :
Multisensor traffic mapping filters
Author_Institution :
Metron, Inc., Reston, VA, USA
Abstract :
A traffic intensity filter is derived using a probability generating functional approach. Traffic filters estimate, or map, the mean rate at which different regions of state space generate target detection opportunities in a field of distributed sensors. They are Bayesian filters that incorporate sensor measurement likelihood functions and target detection capabilities. Traffic maps contribute to situational awareness for heterogeneous sensor fields. They are practical for applications with large numbers of sensors because their computational complexity is linear in the numbers of sensors and measurements.
Keywords :
Bayes methods; computational complexity; distributed sensors; filters; sensor fusion; signal detection; Bayesian filters; computational complexity; distributed sensors; heterogeneous sensor fields; multisensor traffic mapping filters; probability generating functional approach; sensor measurement likelihood functions; situational awareness; target detection capability; traffic intensity filter; Bayesian methods; Clutter; Computational modeling; Joints; Object detection; Probability density function; Scattering; Finite point processes; Intensity filter; PHD filter; Probability generating functionals; Sensor fields; Situational Awareness; Traffic filter;
Conference_Titel :
Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2012 Workshop on
Conference_Location :
Bonn
Print_ISBN :
978-1-4673-3010-7
DOI :
10.1109/SDF.2012.6327906
