Research Institute of Information Fusion, Naval Aviation University, Yantai 264001, China.
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel). 2018 Jun 22;18(7):2012. doi: 10.3390/s18072012.
Networked multiple sensors are used to solve the problem of maneuvering target tracking. To avoid the linearization of nonlinear dynamic functions, and to obtain more accurate estimates for maneuvering targets, a novel adaptive information-weighted consensus filter for maneuvering target tracking is proposed. The pseudo measurement matrix is computed with unscented transform to utilize the information form of measurements, which is necessary for consensus iterations. To improve the maneuvering target tracking accuracy and get a unified estimation in each sensor node across the entire network, the adaptive current statistical model is exploited to update the estimate, and the information-weighted consensus protocol is applied among neighboring nodes for each dynamic model. Based on posterior probabilities of multiple models, the final estimate of each sensor is acquired with weighted combination of model-conditioned estimates. Experimental results illustrate the superior performance of the proposed algorithm with respect tracking accuracy and agreement of estimates in the whole network.
网络多传感器用于解决机动目标跟踪问题。为了避免非线性动态函数的线性化，并为机动目标获得更准确的估计，提出了一种新的机动目标跟踪自适应信息加权共识滤波器。利用无迹变换计算伪量测矩阵，以利用量测信息的形式，这对于共识迭代是必要的。为了提高机动目标跟踪精度，并在整个网络中的每个传感器节点获得统一的估计，利用自适应当前统计模型来更新估计，并且针对每个动态模型在相邻节点之间应用信息加权共识协议。基于多个模型的后验概率，使用模型条件估计的加权组合获得每个传感器的最终估计。实验结果表明，该算法在跟踪精度和整个网络中估计的一致性方面具有优越的性能。
