Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities
Abstract
In multi-object inference, the multi-object probability density captures the uncertainty in the number and the states of the objects as well as the statistical dependence between the objects. Exact computation of the multi-object density is generally intractable and tractable implementations usually require statistical independence assumptions between objects. In this paper we propose a tractable multi-object density approximation that can capture statistical dependence between objects. In particular, we derive a tractable Generalized Labeled Multi-Bernoulli (GLMB) density that matches the cardinality distribution and the first moment of the labeled multi-object distribution of interest. It is also shown that the proposed approximation minimizes the Kullback-Leibler divergence over a special tractable class of GLMB densities. Based on the proposed GLMB approximation we further demonstrate a tractable multi-object tracking algorithm for generic measurement models. Simulation results for a multi-object Track-Before-Detect example using radar measurements in low signal-to-noise ratio (SNR) scenarios verify the applicability of the proposed approach.
- Publication:
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IEEE Transactions on Signal Processing
- Pub Date:
- October 2015
- DOI:
- 10.1109/TSP.2015.2454478
- arXiv:
- arXiv:1412.5294
- Bibcode:
- 2015ITSP...63.5487P
- Keywords:
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- Statistics - Other Statistics
- E-Print:
- doi:10.1109/TSP.2015.2454478