| 常加速度模型的简化自协方差最小二乘法 |
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| 引用本文： | 林旭,罗志才,姚朝龙.常加速度模型的简化自协方差最小二乘法[J].测绘学报,2014,43(11):1144-1150. |
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| 作者姓名： | 林旭 罗志才 姚朝龙 |
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| 作者单位： | 1. 武汉大学 测绘学院;
2. 武汉大学 地球空间环境与大地测量教育部重点实验室;
3. 测绘遥感信息工程国家重点实验室 |
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| 基金项目： | 国家自然科学基金(41174062；41131067)；中央高校基本科研业务费专项资金(2012214020206)；地球空间环境与大地测量教育部重点实验室开放基金 |
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| 摘 要： | “当前”统计模型自适应算法并非真正意义上的自适应目标跟踪算法，其性能与其中关键参数的选择有着直接的关系。本文以常加速度模型对机动载体进行建模，顾及其状态噪声协方差矩阵满足特定结构，提出了简化的自协方差最小二乘噪声协方差估计方法，该方法通过建立新息的相关函数序列与未知噪声协方差矩阵间的函数模型，并结合最小二乘法进行噪声协方差估计。数值仿真结果表明，当载体进行阶跃加速度运动或变加速度运动时，本文所提方法的目标跟踪精度均优于“当前”统计模型自适应算法。
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| 关 键 词： | 目标跟踪 Kalman滤波 简化的自协方差最小二乘法 状态噪声 噪声估计 常加速度模型 |
| 收稿时间： | 2013-12-02 |
| 修稿时间： | 2014-03-20 |
Simplified Autocovari ance Least-squares Method for Constant Acceleration Model |
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| LIN Xu,LUO Zhicai,YAO Chaolong.Simplified Autocovari ance Least-squares Method for Constant Acceleration Model[J].Acta Geodaetica et Cartographica Sinica,2014,43(11):1144-1150. |
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| Authors: | LIN Xu LUO Zhicai YAO Chaolong |
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| Institution: | 1. School of Geodesy and Geomatics, Wuhan University;
2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing;
3. Key Laboratory of Geospace Environment and Geodesy, Ministry of Education |
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| Abstract: | Adaptive “current” statistical model algorithm is not the really adaptive target tracking algorithm, the performance of the algorithm depends on the key parameters. In this paper, the maneuvering targets are modeled by the constant acceleration model, and considering the special structure of the process noise covariance matrix, a simplified autocovariance least-squares method is proposed to estimate noise covariances. And this method establishes a relationship between the autocovairnace of the innovation and the unknown covariances, thus, the noise covariance can be estimated by the least-squares method. The simulation results show that, when the maneuvering targets with unit-step acceleration or variable acceleration, the accuracy of the proposed method is better than the adaptive “current” statistical model algorithm. |
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| Keywords: | target tracking Kalman filter simplified autocovariance least-squares process noise noise estimation constant acceleration model |
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