基于不确定性分析的多传感器航迹融合算法

An Uncertainty Analysis-Based Algorithm Track Fusion

针对目前多传感器系统中常用的航迹融合方法精度与计算量不能兼顾,不能很好地处理不确定性,特别是对曲线航迹拐点的融合误差较大等问题,提出一种基于不确定性分析的航迹融合算法。该算法通过分析航迹融合所需的信息量,用标准熵量化每条航迹的不确定程度,从总体上删除质量较差的航迹,然后对每条参与融合的航迹进行分析,用正交多项式回归的方法剔除了测量误差较大的数据点。该算法有效的处理了传感器航迹中的不确定因素,解决了目前航迹融合方法中拐点融合误差较大的问题,以较小的计算开销达到了较高的精度,从而平衡了精度与计算量之间的矛盾。最后在多传感器多航迹的环境下讨论了其具体实现过程,仿真实验结果验证了该算法的有效性、优越性。

The common track fusion algorithms in the current multi-sensor system have some defects such as serious imbalance between accuracy and computational burden,inefficacy to uncertainties,especially high fusion errors for inflection points of tracks and so on.In response to these defects,a track fusion algorithm based on a two-stage paradigm of uncertainty analysis and track state estimate fusion is presented in the paper,and its implementation process in the multi-sensor and multi-target environment is discussed.The algorithm is used to delete poor tracks by analyzing the information demand for the fusion and to quantify the uncertainties of every track by adopting the standard entropy.Then the data points with high errors in the tracks to take part in the fusion are corrected by orthogonal polynomial regression.In contrast to existing methods,the algorithm takes full consideration of and deals with the uncertainties effectively,plays down the high fusion errors for inflection points of tracks,and approaches a high accuracy with less computational burden,thus gaining a tradeoff between accuracy and computational burden.Simulation results show effectiveness and superiority of the algorithm.