基于置信度传播的变分自适应协同导航方法

协同导航过程中先验信息的准确性是保证协同导航系统精度和可靠性的重要关键因素。针对协同导航系统在复杂环境下会因外界干扰产生未知且时变噪声问题,提出一种基于置信度传播的变分自适应协同导航方法(SWSP)。首先以置信度传播(SPBP)协同导航贝叶斯框架为基础,完成基于置信传播机制的前向滤波;随后通过IW处理过程噪声和量测噪声作为贝叶斯估计的先验信息;进而利用前向滤波值构造滑动窗口对噪声进行平滑估计,从而解决因噪声时变而造成的协同导航系统滤波精度下降问题。仿真结果表明:当噪声时变时,进行平滑操作的SWSP算法与未进行平滑操作的SPBP算法相比,位置误差降低了90%,精度更接近于最优opt SPBP算法。

A variational adaptive cooperative navigation method based on belief propagation

The accuracy of prior information is a key element to ensure accuracy and reliability of the collaborative navigation system. The unknown and time-varying noise will be generated by external disturbances in a complex environment. To address this issue, a variational adaptive cooperative navigation method based on belief propagation is proposed. Firstly, based on the basic model of sigma point belief propagation (SPBP) cooperative navigation, the forward filtering process of cooperative navigation based on the confidence propagation mechanism is completed. The process noise and measurement noise are treated as the prior information of Bayesian estimation by IW (Inverse-Wishart). Then, the forward filtering value is used to establish a sliding window to smooth the noise variable to solve the filtering accuracy decline caused by the time variation of noise. Compared with that of the SPBP algorithm without smoothing operation, simulation results show that the position error of the slide window variational adaptive sigma point-belief propagation (SWSP) algorithm with smoothing operation is reduced by 90% due to the noise time-varying. The accuracy is much close to that of the opt SPBP algorithm.