Tracking and data association
Detecting Moving Objects in Noisy Radar Data Using a Relational Database
ADBIS '09 Proceedings of the 13th East European Conference on Advances in Databases and Information Systems
AIMS: an SQL-based system for airspace monitoring
Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming
Estimation of radar cross section of a target under track
EURASIP Journal on Advances in Signal Processing
Towards a universal tracking database
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Efficient tracking of moving objects using a relational database
Information Systems
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Tracking multiple targets in a cluttered environment is a challenging task. Probabilistic multiple hypothesis tracking (PMHT) is an efficient approach for dealing with it. Essentially PMHT is based on expectation-maximization for handling with association conflicts. Linearity in the number of targets and measurements is the main motivation for a further development and extension of this methodology. In particular, the problem of track extraction and deletion is apparently not yet satisfactorily solved within this framework. A sequential likelihood-ratio (LR) test for track extraction has been developed and integrated into the framework of traditional Bayesian multiple hypothesis tracking by Günter van Keuk in 1998. As PMHT is a multiscan approach as well, it also has the potential for track extraction. In this paper, an analogous integration of a sequential LR test into the PMHT framework is proposed. We present an LR formula for track extraction and deletion using the PMHT update formulae. The LR is thus a by-product of the PMHT iteration process, as PMHT provides all required ingredients for a sequential LR calculation. Therefore, the resulting update formula for the sequential LR test affords the development of track-before-detect algorithms for PMHT. The approach is illustrated by a simple example.