Multi-target Track-Before-Detect using labeled random finite set
Multi-target tracking requires the joint estimation of the number of target trajectories and their states from a sequence of observations. In low signal-to-noise ratio (SNR) scenarios, the poor detection probability and large number of false observations can greatly degrade the tracking performance....
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2013
|
| Online Access: | http://hdl.handle.net/20.500.11937/26035 |
| Summary: | Multi-target tracking requires the joint estimation of the number of target trajectories and their states from a sequence of observations. In low signal-to-noise ratio (SNR) scenarios, the poor detection probability and large number of false observations can greatly degrade the tracking performance. In this case an approach called Track-Before-Detect (TBD) that operates on the pre-detection signal, is needed. In this paper we present a labeled random finite set solution to the multitarget TBD problem. To the best of our knowledge this is the first provably Bayes optimal approach to multi-target tracking using image data. Simulation results using realistic radar-based TBD scenarios are also presented to demonstrate the capability of the proposed approach. |
|---|