CPHD Filtering With Unknown Clutter Rate and Detection Profile
CPHD Filtering With Unknown Clutter Rate and Detection Profile
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DOI：
10.1109/tsp.2011.2128316
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发表时间：
2011-08-01
影响因子：
5.4
通讯作者：
Ba-Ngu Vo
中科院分区：
文献类型：
--
作者：
Mahler, Ronald P. S.;Ba-Tuong Vo;Ba-Ngu Vo
在贝叶斯多目标滤波中，我们必须处理两个显著的不确定性来源：杂波和检测。在概率假设密度（PHD）和基数化PHD（CPHD）滤波器等多目标滤波器中，杂波率和检测轮廓等参数的知识至关重要。杂波和检测模型参数的显著失配导致有偏估计。在实践中，这些模型参数通常是手动调整或从训练数据离线估计。在本文中，我们提出了PHD/CPHD滤波器，可以适应模型失配的杂波率和检测轮廓。特别是，我们设计的PHD/CPHD过滤器，可以自适应地学习杂波率和检测配置文件，同时过滤的版本。此外，封闭形式的解决方案，这些过滤递归导出使用Beta和高斯混合。仿真结果验证了所提出的解决方案。
In Bayesian multi-target filtering, we have to contend with two notable sources of uncertainty, clutter and detection. Knowledge of parameters such as clutter rate and detection profile are of critical importance in multi-target filters such as the probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters. Significant mismatches in clutter and detection model parameters result in biased estimates. In practice, these model parameters are often manually tuned or estimated offline from training data. In this paper we propose PHD/CPHD filters that can accommodate model mismatch in clutter rate and detection profile. In particular we devise versions of the PHD/CPHD filters that can adaptively learn the clutter rate and detection profile while filtering. Moreover, closed-form solutions to these filtering recursions are derived using Beta and Gaussian mixtures. Simulations are presented to verify the proposed solutions.