单站无源定位中的抗野值鲁棒CKF算法

单站无源定位系统的测量噪声中如果出现野值,会影响滤波器的估计精度和稳定性,严重时还会导致滤波器发散。针对这一问题,基于Bayes定理并结合归一化受污染正态模型,提出了一种抗野值鲁棒容积卡尔曼滤波算法。该算法采用球面径向积分原则直接计算非线性函数的均值和方差,并对测量误差建立一个归一化的受污染正态模型,然后根据野值出现的后验概率来自适应调整测量预测残差的方差阵。结合空频域单站无源定位模型进行仿真实验表明,该算法可以较好地抑制测量噪声中的离散或成片连续野值的不利影响,具有较强的鲁棒性。

An Outlier Rejecting Robust CKF Algorithm for Single Observer Passive Location

The precision and stability of filters could be influenced owing to the fact that outliers may occur in measurement noise for single observer passive location system, and the filter may be diverged seriously. For the sake of this reason, combining with the scaled--contaminated normal distribution model, a robust cubature Kalman filter algorithm is proposed based on Bayes theory. The cubature rule based numerical integration method is directly used to calculate the mean and covariance of the nonlinear random function in this algorithm. The scaled--contaminated normal distribution model for the measurement error is set up and the measurement prediction residual error variance matrix is adjusted adaptively according to the posterior probability of outliers. Combining with the spatial-frequency domain model, simulation results show that this algorithm has strong robustness and can effectively reduce the impact of discrete or continuous outliers in measurement noise.