In the target tracking literature, the benefits of a centralized architecture are well documented. Similarly, the performance of decentralized estimation algorithms is often measured against the upper bound provided by centralized, full-information estimation. However, the benefits that exist in single-stage centralized tracking must be measured against the computational cost that is incurred. Under certain circumstances, even when sensor data is available at a centralized processing center, a decoupled approach to multi-sensor multi-target tracking may provide improved performance. In this paper, we propose a centralized, hierarchical framework for data fusion, which we refer to as hierarchical multi-hypothesis tracking (HMHT). The HMHT architecture requires multiple fusion modules that perform combined detection-level and track-level tracking, with all fusion modules having access to sensor data. We explore the benefits of the HMHT approach in multi-sensor settings where sensor update rates and the information content of the data differ significantly. The approach also shows promise for processing low-observability bearings-only data, as well as for road-constrained tracking.
Hierarchical multi-hypothesis tracking with application to multi-scale sensor data
01.01.2002
1335012 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
6.0207 Hierarchical Multi-Hypothesis Tracking with Application to Multi-Scale Sendor Data
British Library Conference Proceedings | 2002
|Multi-hypothesis database for large-scale data fusion
IEEE | 2002
|Multi-Hypothesis Database for Large-Scale Data Fusion
British Library Conference Proceedings | 2002
|Multi-robot multiple hypothesis tracking for pedestrian tracking
British Library Online Contents | 2012
|