基于系统误差诊断的多测元融合算法

在多测元目标跟踪定位中,结合测元的匹配特性,提出"三选二"原则诊断并估计少数测元上的系统误差,以多测元最小二乘多项式滤波抑制随机误差,从而提高目标定位精确度的数据融合新方法。以三测元为例给出了具体在微分域和积分域进行匹配诊断的步骤。在估计并扣除系统误差之后,进行三通道最小二乘多项式滤波以更有效地抑制随机误差。仿真计算结果表明该方法能够有效地发现和估计系统误差,同时指出在积分域进行匹配诊断和估计的精度要优于微分域匹配诊断。

Multi-Measurement Data Fusion Algorithm Based on System Error Diagnose

In the problem of multi-measurement data target tracking system, by using the prior information that measuring data are matching, a novel data fusion method is proposed to improve the precision of trajectory parameters, in which the system error is diagnosed and estimated by a "three choosing two "principle and the random noise is filtered by multi-measurement polynomial least square filter. For simplicity, a three-measurement case is discussed and the following detailed step for matched diagnose in both differential domain and integral domain is put forward. After the system error is estimated and deducted, the random error is effectively reduced by three channels polynomial least square filter. Simulation results show that this method can detect and estimate the system error notably. Meanwhile the precision of matching diagnose in differential domain is higher than that in integral domain.