Title :
Advanced Sensor Models: Benefits for Target Tracking and Sensor Data Fusion
Author_Institution :
Res. Inst. for Inf. Process., Commun., & Ergonomics, FGAN
Abstract :
Modern sensor systems are typically characterized by advanced signal processing techniques which have direct impact on the quantitative and qualitative properties of the sensor data produced. This makes a more advanced modeling of the statistical characteristics of the sensor output inevitable. Via constructing appropriate likelihood functions based on these models the performance of Bayesian tracking and sensor data fusion techniques can be much improved. The proposed paper discusses the benefits by selected examples from various applications
Keywords :
Bayes methods; sensor fusion; statistical analysis; target tracking; Bayesian tracking; advanced sensor models; advanced signal processing techniques; sensor data fusion; statistical characteristics; target tracking; Bayesian methods; Brain modeling; Intelligent sensors; Intelligent systems; Predictive models; Radar tracking; Sensor fusion; Sensor phenomena and characterization; Sensor systems; Target tracking;
Conference_Titel :
Multisensor Fusion and Integration for Intelligent Systems, 2006 IEEE International Conference on
Conference_Location :
Heidelberg
Print_ISBN :
1-4244-0566-1
Electronic_ISBN :
1-4244-0567-X
DOI :
10.1109/MFI.2006.265652
