National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.
Key laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xi'an 710071, China.
Sensors (Basel). 2019 Feb 25;19(4):980. doi: 10.3390/s19040980.
This paper presents a novel multi-objective optimization based sensor selection method for multi-target tracking in sensor networks. The multi-target states are modelled as multi-Bernoulli random finite sets and the multi-Bernoulli filter is used to propagate the multi-target posterior density. The proposed method is designed to select the sensor that provides the most reliable cardinality estimate, since more accurate cardinality estimate indicates more accurate target states. In the multi-Bernoulli filter, the updated multi-target density is a multi-Bernoulli random finite set formed by a union of legacy tracks and measurement-updated tracks. The legacy track and the measurement-updated track have different theoretical and physical meanings, and hence these two kinds of tracks are considered separately in the sensor management problem. Specifically, two objectives are considered: (1) maximizing the mean cardinality of the measurement-updated tracks, (2) minimizing the cardinality variance of the legacy tracks. Considering the conflicting objectives simultaneously is a multi-objective optimization problem. Tradeoff solutions between two conflicting objectives will be derived. Theoretical analysis and examples show that the proposed approach is effective and direct. The performance of the proposed method is demonstrated using two scenarios with different levels of observability of targets in the passive sensor network.
本文提出了一种新的基于多目标优化的传感器选择方法，用于传感器网络中的多目标跟踪。多目标状态被建模为多伯努利随机有限集，多伯努利滤波器用于传播多目标后验密度。所提出的方法旨在选择提供最可靠基数估计的传感器，因为更准确的基数估计表示更准确的目标状态。在多伯努利滤波器中，更新后的多目标密度是由遗留轨迹和测量更新轨迹的并集形成的多伯努利随机有限集。遗留轨迹和测量更新轨迹具有不同的理论和物理意义，因此在传感器管理问题中分别考虑这两种轨迹。具体来说，考虑了两个目标：（1）最大化测量更新轨迹的平均基数，（2）最小化遗留轨迹的基数方差。同时考虑两个冲突的目标是一个多目标优化问题。将推导出两个冲突目标之间的权衡解决方案。理论分析和示例表明，所提出的方法是有效和直接的。使用具有不同目标可观测性水平的无源传感器网络中的两个场景来演示所提出方法的性能。
