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Multiple Sensor Measurement Updates for the Extended Target Tracking Random Matrix Model

机译:扩展目标跟踪随机矩阵模型的多传感器测量更新

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摘要

In this paper, multiple sensor measurement update is studied for a random matrix model. Four different updates are presented and evaluated: three updates based on parametric approximations of the extended target state probability density function and one update based on a Rao–Blackwellized (RB) particle approximation of the state density. An extensive simulation study shows that the RB particle approach shows best performance, at the price of higher computational cost, compared to parametric approximations.
机译:本文针对随机矩阵模型研究了多传感器测量更新。提出并评估了四个不同的更新:三个基于扩展目标状态概率密度函数的参数近似的更新,另一个基于状态密度的Rao-Blackwellized(RB)粒子近似的更新。广泛的仿真研究表明,与参数逼近相比,RB粒子方法显示出最佳性能,但以更高的计算成本为代价。

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