杂波中多传感器数据融合改善目标航迹丢失的理论分析

本文从理论上讨论杂波环境中,多传感器数据融合对目标航迹丢失的改善。通过建立融合预测估计误差的转移概率密度函数,分析了目标航迹丢失的机理。在最近邻关联准则下,计算了融合航迹维持和融合航迹起始时的跟踪性能融合航迹平均丢失时间和融合航迹累积丢失概率与杂波密度的关系,并与单传感器的情形作了比较。结果表明,多传感器的航迹融合减小了目标丢失的可能性,提高了跟踪性能。这一结论对进一步理解数据融合的作用具有重要的理论意义。

THEORETICAL ANALYSIS OF IMPROVEMENT OF TRACK LOSS IN CLUTTER WITH MULTISENSOR DATA FUSION

The paper analyses the improvement of track loss in clutter with multisensor data fusion. By detemination of the transition probability desity function for the fusion prediction error, one can study the mechanism of track loss analytically. For nearest-neighbor association algorithm, the paper studies the fusion tracking performance parameters,such as mean time to lose fusion track and the fraction of lost fusion track versus the normalized clutter density, for track continuation and track initiation, respectively. A comparison of the results obtained with the case of a single sensor is presented. These results show that the fusion tracks of multisensor reduce the possibility of track loss and improve the tracking performance. The analysis is of great importance for further understanding the action of data fusion.