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Sequential measurement-driven multi-target Bayesian filter for nonlinear multi-target models

机译：非线性多目标模型的顺序测量驱动多目标贝叶斯滤波器

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The sequential measurement-driven multiple target Bayesian (SMB) filter is a valid method for multiple target tracking in situation of clutter interference and detection uncertainty. The known SMB algorithm spread the marginal distribution and existence probability of objective, and sequentially handles every receiving measurements. It satisfy closed solution in linear multiple objective models. Nevertheless, the solution is inapplicable to nonlinear Gaussian multiple target models. To handle this problem, we recommended a SMB filter algorithm to adapt nonlinear Gaussian multiple objective models. The recommended implementation applies the unscented transform method to handle the nonlinearity problems. The simulation experiment conclusions show that the recommended filter is more efficient on tracking multi-targets than the traditional PHD algorithm in situation of some clutter interference.
机译：顺序测量驱动的多目标贝叶斯（SMB）滤波器是在杂波干扰和检测不确定性情况下进行多目标跟踪的有效方法。已知的SMB算法分散目标的边际分布和存在概率，并顺序处理每次接收的测量。它满足线性多目标模型中的封闭解。然而，该解决方案不适用于非线性高斯多目标模型。为了解决这个问题，我们建议使用SMB滤波器算法来适应非线性高斯多目标模型。推荐的实现方法采用无味变换方法来处理非线性问题。仿真实验结果表明，在某些杂波干扰情况下，推荐的滤波器比传统的PHD算法在跟踪多目标方面更为有效。

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