1 research outputs found
Multi-target Joint Detection, Tracking and Classification Based on Generalized Bayesian Risk using Radar and ESM sensors
In this paper, a novel approach is proposed for multi-target joint detection,
tracking and classification based on the labeled random finite set and
generalized Bayesian risk using Radar and ESM sensors. A new Bayesian risk is
defined for the labeled random finite set variables involving the costs of
multi-target cardinality estimation (detection), state estimation (tracking)
and classification. The inter-dependence of detection, tracking and
classification is then utilized with the minimum Bayesian risk. Furthermore,
the conditional labeled multi-Bernoulli filter is developed to calculate the
estimates and costs for different hypotheses and decisions of target classes
using attribute and dynamical measurements. Moreover, the performance is
analyzed. The effectiveness and superiority of the proposed approach are
verified using numerical simulations