Ministry of Education Key Laboratory for Intelligent Networks and Network Security (MOE KLINNS), School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2018 Nov 23;18(12):4115. doi: 10.3390/s18124115.
A new optimization algorithm of sensor selection is proposed in this paper for decentralized large-scale multi-target tracking (MTT) network within a labeled random finite set (RFS) framework. The method is performed based on a marginalized δ-generalized labeled multi-Bernoulli RFS. The rule of weighted Kullback-Leibler average (KLA) is used to fuse local multi-target densities. A new metric, named as the label assignment (LA) metric, is proposed to measure the distance for two labeled sets. The lower bound of LA metric based mean square error between the labeled multi-target state set and its estimate is taken as the optimized objective function of sensor selection. The proposed bound is obtained by the information inequality to RFS measurement. Then, we present the sequential Monte Carlo and Gaussian mixture implementations for the bound. Another advantage of the bound is that it provides a basis for setting the weights of KLA. The coordinate descent method is proposed to compromise the computational cost of sensor selection and the accuracy of MTT. Simulations verify the effectiveness of our method under different signal-to- noise ratio scenarios.
本文提出了一种新的传感器选择优化算法，用于在标记随机有限集（RFS）框架内实现分散式大规模多目标跟踪（MTT）网络。该方法基于边缘化 δ-广义标记多伯努利 RFS 进行操作。采用加权 Kullback-Leibler 平均（KLA）规则融合局部多目标密度。提出了一种新的度量标准，称为标签分配（LA）度量标准，用于测量两个标记集之间的距离。基于标记多目标状态集与其估计之间的均方误差的 LA 度量的下界被用作传感器选择的优化目标函数。该界通过 RFS 测量的信息不等式获得。然后，我们为该界提出了序贯蒙特卡罗和高斯混合实现。该界的另一个优点是它为 KLA 的权重设置提供了依据。提出了坐标下降法来平衡传感器选择的计算成本和 MTT 的准确性。仿真验证了在不同信噪比场景下该方法的有效性。
