Abstract
This paper proposes a new solution to multi-target joint detection, tracking and classification based on labeled random finite set (RFS) and belief function theory. A class dependent multi-model marginal generalized labeled multi-Bernoulli (MGLMB) filter is developed to analytically calculate the multi-target number, state estimates and model probabilities. In addition, a two-level classifier based on continuous transferable belief model (cTBM) is designed for target classification. To make full use of the kinematic characteristics for classification, both the dynamic modes and states are considered in the classifier, the model dependent class beliefs are computed on the continuous state feature subspace corresponding to different dynamic modes and then fused. As a result that the uncertainty about the classes is well described for decision, the classification results are more reasonable and robust. Moreover, as the estimation and classification problems are jointly solved, the tracking and classification performance are both improved. In the simulation, a scenario contains multi-target with miss detection and dense clutter is used. The performance of multi-target detection, tracking and classification is better than traditional methods based on Bayesian classifier or single model. Simulation results are illustrated to demonstrate the effectiveness and superiority of the proposed algorithm.
Highlights
Multiple target joint detection, tracking and classification is a critical problem in radar system, this problem consists of three subproblems: estimate the number of the targets, estimate their kinematic states and determine their classes
Within the transferable belief model (TBM) framework, the beliefs allocated to the elements of the class focal set, which is the power set of the classes, and the explicit class probability is calculated until the decision is made
This section first presents the mathematical formulation of the problem, proposes the multi-target joint detection, tracking and classification algorithm based on the labeled random finite set (RFS) and continuous transferable belief model (cTBM)
Summary
Multiple target joint detection, tracking and classification is a critical problem in radar system, this problem consists of three subproblems: estimate the number of the targets, estimate their kinematic states and determine their classes. Due to track information of the RFS based filters can not be obtained directly, these algorithms only calculate the class-dependent multi-target density without the explicit classification results for each target. In [35,36,37], multi-target detection, tracking and classification problems have been solved using the GLMB filter, these approaches take advantages of the GLMB filter, and explicit class probabilities and state estimates of each target are produced. A joint solution is proposed to multi-target detection, tracking and classification based on labeled RFS and belief function theory in the case of only the position measurements are available. A class dependent multi-model MGLMB filter is developed to calculate the multi-target cardinality, states estimates and model probabilities, and a two-level classifier is designed based on the cTBM to identify the target classes using the kinematic data.
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