基于 SST-SCKF 的运动目标超宽带定位算法研究

使用超宽带(UWB)进行定位过程中，卡尔曼滤波是一种常见的降噪方法，但由于对非线性系统滤波性能差，且定位目标运动轨迹易超出基站布局区域以及受到异常噪声干扰，会影响定位系统的准确性和稳定性。针对这一问题，提出一种对称强跟踪(SST)平方根容积卡尔曼(SCKF)算法，通过引入对称时变渐消因子调节各协方差矩阵，实现改变误差协方差矩阵中多重衰落因子矩阵的工作方式，进而调整滤波增益，计算复杂度虽略有增加，但增强定位模型的适应性与鲁棒性。仿真验证表明，在异常噪声干扰下，改进后的算法(SST-SCKF)相较于SCKF/多重渐消因子的SCKF(ST-ASCKF)算法可有效提高定位准确度，且定位轨迹较于单渐消因子的SCKF算法(STSCKF)更为平滑；利用SST-SCKF算法设计基于UWB技术的定位方案，通过动态模拟实验表明，本文提出的SST-SCKF算法较之SCKF/STSCKF/ST-ASCKF滤波性能更优，为复杂环境噪声下人员UWB定位提供更好的降噪，使定位更为精准。

Research on ultra-wideband location algorithm of moving target based on SST-SCKF

Kalman filtering is a common noise reduction method in the process of positioning using ultra-wideband (UWB). However, this algorithm has poor filtering performance of nonlinear system. The moving track of positioning target is easy to exceed the layout area of base station and be disturbed by abnormal noise, which will affect the accuracy and stability of positioning system. In order to solve this problem, a symmetric strong tracking ( SST) square root volume Kalman ( SCKF) algorithm was proposed. By introducing a symmetric time-varying fading factor to adjust each covariance matrix, the working mode of the multiple fading factor matrix in the error covariance matrix is changed, and then the filter gain is adjusted. Although the computational complexity increases slightly, the adaptability and robustness of the positioning model are enhanced. Simulation results show that under the interference of abnormal noise, the improved algorithm (SST-SCKF) can effectively improve the positioning accuracy compared with SCKF/ SCKF for multiple fading factors ( ST-ASCKF) algorithm, and the positioning trajectory is smoother than SCKF with single fading factor ( STSCKF). The positioning scheme based on UWB technology was designed by SST-SCKF algorithm. The dynamic simulation experiments show that the SST-SCKF algorithm proposed in this paper has better filtering performance than SCKF/ STSCKF/ ST-ASCKF. This SST-SCKT algorithm provides better noise reduction for personnel UWB positioning under complex environmental noise and makes the positioning more accurate.