| 基于粒子滤波理论的雷达多目标TBD检测 |
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| 引用本文： | 荆丹,王俊.基于粒子滤波理论的雷达多目标TBD检测[J].雷达科学与技术,2008,6(1):48-51. |
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| 作者姓名： | 荆丹 王俊 |
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| 作者单位： | 西安电子科技大学雷达信号处理国家重点实验室,陕西西安,710071 |
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| 基金项目： | 国家自然科学基金，国防科技预研基金 |
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| 摘 要： | 粒子滤波理论适用于在非线性和非高斯环境下的目标跟踪与检测。文中基于序列重要性采样定理，提出了模型环境和多雷达目标检测的递归贝叶斯TBD算法。此算法在基本粒子滤波算法SIR的基础上，采用多模型粒子滤波器实现了多雷达目标的检测。仿真结果表明，算法能够有效地进行目标跟踪与检测。
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| 关 键 词： | 粒子滤波 贝叶斯估计 重要性采样 重采样 |
| 文章编号： | 1672-2337（2008）01-0048-04 |
| 收稿时间： | 2007-09-12 |
| 修稿时间： | 2007-10-10 |
TBD Algorithm for Multiple Radar Targets Based on Particle Filter |
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| JING Dan,WANG Jun.TBD Algorithm for Multiple Radar Targets Based on Particle Filter[J].Radar Science and Technology,2008,6(1):48-51. |
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| Authors: | JING Dan WANG Jun |
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| Affiliation: | (National Key Lab of Radar Signal Processing, Xidian University, Xi＇an 710071, China) |
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| Abstract: | Particle filter is a robust algorithm for target tracking and detection in non-linear and non- Gauss circumstances. Based on the theories of sequential importance resampling（SIR）, this paper sets up a model and proposes a recursive Bayesian algorithm for multiple target TBD application. The algorithm is realized by using multiple model particle filter. Simulated results show that the method can work efficiently in tracking and detection. |
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| Keywords: | particle filter Bayesian estimation importance sampling resampling |
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