科研之友
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摘要
A novel ant clustering filtering algorithm, under the guidance of first-order statistic moment of posterior multiple-target state (probability hypothesis density), is investigated and applied to estimate the time-varying number of targets and their individual states in a cluttered environment. The ant clustering filtering algorithm includes two clustering steps: the first step is called rough ant clustering, which involves the stochastic selection of each ant and its state local adjustment according to the current likelihood function and posterior intensity, respectively; while the second is called fine ant clustering, which employs these ants to extract the multiple-target state. Numerical simulations verify the tracking multiple-target capability of our proposed algorithm through performance comparison with the Sequential Monte Carlo (SMC) method.
