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含未知非高斯噪声的自适应量测转换水下目标跟踪

Adaptive measurement conversion for underwater target tracking with unknown non-Gaussian noise

  • 摘要: 针对量测含未知野值的非高斯极坐标−笛卡尔坐标水下目标跟踪问题, 提出了基于变分贝叶斯方法的迭代量测转换学生t滤波 (VBICMSTF) 算法。将有源声呐目标距离及方位估计结果作为基于极坐标的非线性量测, 使用无偏量测转换对其进行基于学生t分布近似的先验线性化建模, 然后通过变分贝叶斯方法迭代地更新伪线性量测尺度阵及目标状态的后验分布, 并在迭代过程中利用目标位置的更新结果对量测转换二阶矩的计算进行校正, 由此形成先验−后验循环更新。仿真及湖上试验结果表明, VBICMSTF在含未知非高斯量测噪声的强非线性跟踪场景下, 相比伪线性学生t分布变分贝叶斯方法跟踪误差降低25%以上, 且维持了滤波的一致性。

     

    Abstract: Aiming at the non-Gaussian polar-Cartesian underwater target tracking problem with unknown measurement outliers, an iterative converted measurement Student’s t filter based on the variational Bayesian method (VBICMSTF) is proposed. Range and azimuth estimations of active sonar target are taken as nonlinear measurement based on polar coordinates, and the pseudo-linear measurement after unbiased conversion is modeled approximately using student’s t distribution. Then, the posterior distributions of the pseudo-linear measurement scale array and the target state are iteratively updated by the variational Bayesian method. During the iteration process, the updated target position is used to correct the prior calculation of the second moment of measurement conversion, forming a prior-posterior update loop. Simulation and lake experimental results show that the VBICMSTF reduces the tracking error by more than 25% compared with the pseudo-linear Student’s t distribution variational Bayesian algorithm, and maintains the consistency of filtering in the strong nonlinear tracking scene with unknown non-Gaussian measurement noise.

     

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