| 非线性非高斯模型的高斯和PHD滤波算法(英文) |
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| 作者单位： | 复旦大学电子工程系 |
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| 摘 要： | A new multi-target filtering algorithm, termed as the Gaussian sum probability hypothesis density （GSPHD） filter, is proposed for nonlinear non-Gaussian tracking models. Provided that the initial prior intensity of the states is Gaussian or can be identified as a Gaussian sum, the analytical results of the algorithm show that the posterior intensity at any subsequent time step remains a Gaussian sum under the assumption that the state noise, the measurement noise, target spawn intensity, new target birth intensity, target survival probability, and detection probability are all Gaussian sums. The analysis also shows that the existing Gaussian mixture probability hypothesis density （GMPHD） filter, which is unsuitable for handling the non-Gaussian noise cases, is no more than a special case of the proposed algorithm, which fills the shortage of incapability of treating non-Gaussian noise. The multi-target tracking simulation results verify the effectiveness of the proposed GSPHD.
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| 关 键 词： | 高斯和可能假设密度滤波算法 信号处理 数值模拟 非线性非高斯模型 |
| 收稿时间： | 6 September 2007 |
Gaussian Sum PHD Filtering Algorithm for Nonlinear Non-Gaussian Models |
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| Authors: | Yin Jianjun Zhang Jianqiu Zhuang Zesen |
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| Institution: | Department of Electronic Engineering, Fudan University, Shanghai 200433, China |
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| Abstract: | A new multi-target filtering algorithm, termed as the Gaussian sum probability hypothesis density (GSPHD) filter, is proposed for nonlinear non-Gaussian tracking models. Provided that the initial prior intensity of the states is Gaussian or can be identified as a Gaus- sian sum, the analytical results of the algorithm show that the posterior intensity at any subsequent time step remains a Gaussian sum under the assumption that the state noise, the measurement noise, target spawn intensity, new target birth intensity, target survival prob- ability, and detection probability are all Gaussian sums. The analysis also shows that the existing Gaussian mixture probability hypothe- sis density (GMPHD) filter, which is unsuitable for handling the non-Gaussian noise cases, is no more than a special case of the pro- posed algorithm, which fills the shortage of incapability of treating non-Gaussian noise. The multi-target tracking simulation results verify the effectiveness of the proposed GSPHD. |
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| Keywords: | signal processing Gaussian sum probability hypothesis density simulation nonlinear non-Gaussian tracking |
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