| 摘要: |
| 讨论了用采样的方法近似非线性分布来解决无源定位中的非线性问题，提出了一种简单的正则粒子滤波，克服了标准粒子滤波用于单站无源定位中出现的粒子贫乏现象，将粒子滤波成功应用到无源定位中，计算机仿真表明该算法的定位精度较Unscented卡尔曼滤波（UKF）有一定的提高。 |
| 关键词: 无源定位 贝叶斯估计 unscented卡尔曼滤波 正则粒子滤波 |
| DOI：10.3969/j.issn.1001-893X. |
| 投稿时间：2006-04-15修订日期：2006-07-20 |
| 基金项目:国防重点实验室基金 |
|
| Application of Sample Filtering Algorithm in Single Observer Passive Location |
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| () |
| Abstract: |
| A sample filtering method to solve nonlinear problem in single observer passive location is discussed.A simple regularized particle filter is presented,which can overcome particle impoverishment phenomenon,and can be successfully introduced into single observe passive location.Simulations show that this algorithm can improve the location precision compared with UKF(unscented Kalman filter). |
| Key words: passive location,bayesian estimation,unscented Kalman filter(UKF),regularized particle filter |
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