基于无迹卡尔曼滤波和协方差交叉融合的分层多簇无线传感器网络多速率跟踪算法

针对无线传感器网络(WSNs)在跟踪过程中精度低，性能差等缺点，提出基于无迹卡尔曼滤波(UKF)和协方差交叉(CI)融合的分层多簇WSNs多速率跟踪算法。将传感器分成多个簇，同一簇中的传感器可以采用不同的采样和传输速率对目标的数据进行采集和传输。首先，采用UKF处理传感器节点采集的数据，生成局部估计。然后，利用CI融合算法将收集到的局部估计值形成融合估计。通过定义一个附加权重因子，为真实协方差的不确定性定义一个更严格的界限。仿真验证了方法的有效性，采用多速率分层融合估计的精度更高，效果更明显。

Multi-rate Tracking Algorithm for Hierarchical Multi-cluster Wireless Sensor Networks Based on Unscented Kalman filter and Covariance Intersection Fusion

Aiming at the shortcomings of low precision and poor performance in the tracking process of wireless sensor networks (WSNs), a multi-rate tracking algorithm based on unscented Kalman filter (UKF) and covariance intersection (CI) fusion was proposed for hierarchical multi-cluster WSNs. The sensors were divided into several clusters. The sensors in the same cluster can use different sampling and transmission rates to collect and transmit the target data. Firstly, UKF was used to process the data collected by sensor nodes and generate local estimation. Then, the CI fusion algorithm was used to form the fusion estimation from the collected local estimates. By defining an additional weight factor, we can define a stricter limitation for the uncertainty of the real covariance. The simulation results show the effectiveness of the method. The accuracy of multi-rate hierarchical fusion estimation is higher and the effect is more obvious.