Multisensor dynamic bias estimation with earth-centered earth-fixed coordinate system
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摘要: 配准是多传感器系统进行数据融合必不可少的处理过程.使用一种新的基于地心地固(ECEF, Earth-Centered Earth-Fixed)坐标系的多传感器配准算法,能对传感器的动态偏差进行估计,同时充分考虑了地球曲率对配准算法本身的影响.首先利用多传感器的局部航迹数据来构造关于传感器偏差的伪测量模型,其噪声满足零均值高斯白噪声特性,协方差信息也易于计算,然后使用序贯卡尔曼滤波算法(SKF, Sequential Kalman Filter)对偏差进行动态估计.当传感器偏差恒定时,算法经过简单修改后依然适用.最后通过仿真试验对新算法的性能进行了评估,结果说明新算法能有效地进行传感器配准.Abstract: Registration is the necessary process of data fusion of multisensors system. A new registration algorithm was presented to estimate sensor biases with earth-centered earth-fixed (ECEF) coordinate system. This algorithm was used to correct dynamic systematic errors, and improved estimation performance by considering the effect of the geometry of the global. It was accomplished by constructing pseudomeasurements of the sensor biases based on local tracks, while the pseudomeasurements with additive noises that are zero-mean, white and with easily calculated covariances. Then the sensor bias estimates were obtained dynamically by using sequential process of Kalman filter (SKF). The algorithm could also be used for constant system biases estimation after some simple modification. Finally, Monte Carlo simulations were employed to evaluate the performance of the proposed algorithm, the result shows it can solve the registration problem effectively.
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Key words:
- multisensors /
- ECEF /
- registration /
- dynamic bias /
- sequential Kalman filter
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