| 摘要: |
| 针对移动外辐射源跟踪问题，提出一种融合到达角(Angle of Arrival，AOA)与到达时间差(Time Difference of Arrival，TDOA)观测量的量测转换Kalman滤波(Converted Measurement Kalman Filter，CMKF)算法。首先，采用了一种考虑了传感器位置偏差影响的无源定位算法作为转换非线性的AOA与TDOA观测量至笛卡尔坐标系下观测量的方法，并证明了当AOA与TDOA的测量噪声以及传感器位置偏差都服从高斯分布且噪声强度不大时，该量测转换方法的位置转换误差能达到克拉美罗(Cramér-Rao Lower Bound，CRLB)界；其次，在量测转换的基础上构建了关于移动外辐射源的线性状态空间模型，将非线性的目标跟踪问题转化为线性滤波问题，并最终使用标准Kalman滤波器实时跟踪移动外辐射源位置。仿真结果不仅验证了量测转换精度与理论分析结论吻合，还表明了所提CMKF算法的跟踪精度同时优于扩展Kalman滤波器、无迹滤波器以及粒子滤波器。 |
| 关键词: 移动外辐射源 目标跟踪；协同跟踪；量测转换Kalman滤波 |
| DOI： |
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| 基金项目: |
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| A Multi-station Cooperative Tracking Algorithm for Maneuvering External Illuminator Using Converted Measurements |
| WU Qi,XIONG Jie,NING Jing |
| (Southwest China Institute of Electronic Technology,Chengdu 610036,China) |
| Abstract: |
| For the problem of tracking a moving external illuminator,a converted measurement Kalman filter(CMKF) algorithm is proposed by fusing angle of arrival(AOA) and time difference of arrival(TDOA).Firstly,the passive location algorithm in the presence of sensor location uncertainties suggested by our previous work is adopted as converting the nonlinear AOA and TDOA measurements to a Cartesian frame.Then,the converted measurement error covariance is proved to achieve the Cramér-Rao lower bound(CRLB) under the condition that the AOA and TDOA measurement noises and the sensor location errors are Gaussian and in a small region.In addition,based on the measurement conversion,a linear state space model for the moving external illuminator is established,therefore the nonlinear tracking problem is translated into a linear filtering problem.Finally,the standard Kalman filter is used to track the moving external illuminator.Numerical simulation results not only demonstrate that the conversion accuracy can reach the CRLB,but also show that tracking accuracy of the proposed CMKF algorithm is superior to that of the extended Kalman filer,the unscented Kalman filter,and the particle filter. |
| Key words: moving external illuminator target tracking cooperative tracking converted measurement Kalman filter |
《电讯技术》编辑部 