Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities

Francesco Papi, Ba-Ngu Vo, Ba-Tuong Vo, Claudio Fantacci, Michael Beard
2015 IEEE Transactions on Signal Processing  
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
more » ... en 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.
doi:10.1109/tsp.2015.2454478 fatcat:goubkue7m5bh7o5vs6sr7crbny