| 一种改进的方差自适应滤波算法 |
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| 引用本文： | 骆荣剑,李颖,钱广华. 一种改进的方差自适应滤波算法[J]. 电子设计工程, 2014, 0(8): 131-134 |
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| 作者姓名： | 骆荣剑 李颖 钱广华 |
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| 作者单位： | 中国人民解放军重庆通信学院,重庆400035 |
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| 基金项目： | 国家自然科学基金项目，应急通信重庆市重点实验室开放课题 |
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| 摘 要： | 标准的基于”当前”统计模型的自适应卡尔曼滤波算法中机动频率和加速度极限值存在靠经验预先设定的问题,以及在跟踪非机动和弱机动目标时存在精度不高的问题,本文在分析已有的加速度方差自适应算法的基础上,提出了一种改进的加速度方差自适应算法.仿真结果表明本文提出的改进的加速度方差自适应算法是有效性的,较已有算法提高了跟踪非机动或弱机动目标的精度.
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| 关 键 词： | “当前”统计模型 机动目标跟踪 加速度方差 自适应卡尔曼滤波 |
An improved acceleration variance adaptive filtering algorithm |
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| LUO Rong-jian,LI Ying,QIAN Guang-hua. An improved acceleration variance adaptive filtering algorithm[J]. Electronic Design Engineering, 2014, 0(8): 131-134 |
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| Authors: | LUO Rong-jian LI Ying QIAN Guang-hua |
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| Affiliation: | ( Chongqing Communication Institute of PLA , Chongqing 400035, China) |
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| Abstract: | The standard adaptive Kalman filter algorithm based on the ＂current＂ statistical model exists the problem of selecting Maneuvering frequency and maximum acceleration based on experience, and the problem of low accuracy in tracking non-maneuvering or weak maneuvering target. By analyzing the existing acceleration variance self-adapting algorithm, an improved acceleration variance self-adapting （IAVS） algorithm is proposed. The contrasting simulation results have showed IAVS＇s validity. IAV algorithm also obtains a better tracking accuracy, especially for non-maneuvering or weak maneuvering target. |
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| Keywords: | ＂Current＂ Statistical Model maneuvering target tracking acceleration variance adaptive Kalman filter |
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