Spatial registration and track-to-track association (which are mutually coupled) are essential parts in the process of multi-sensor information fusion. The quality of the spatial registration and track association algorithm directly influences the subsequent fusion performance. Aiming to solve the spatial registration and track association problem in the case where incomplete measurements are provided by different sensors, this paper proposes a residual bias estimation registration (RBER) method based on maximum likelihood and the sequential m-best track association algorithm based on the new target density (SMBTANTD). The RBER method realizes the update of incomplete measurements by sequential filtering technology and eliminates the systematic bias of sensors by using information on the significant targets. The SMBTANTD method introduces a new target density in the correlation matrix, which effectively solves the association problem in the scenarios where the numbers of targets measured by multiple sensors are inconsistent. The reported simulation results demonstrate that the proposed algorithm can not only accurately estimate the systematic bias of the sensors, but also significantly improve the performance of track association.

中文翻译：

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不完全测量下的多传感器航迹关联与空间配准算法


空间配准和航迹关联（相互耦合）是多传感器信息融合过程中的重要组成部分。空间配准和轨迹关联算法的好坏直接影响后续的融合性能。针对不同传感器提供不完整测量情况下的空间配准和航迹关联问题，提出一种基于最大似然的残余偏差估计配准（RBER）方法和基于序列m-best航迹关联算法。新的目标密度（SMBTANTD）。 RBER方法通过顺序滤波技术实现不完整测量值的更新，并利用重要目标的信息消除传感器的系统偏差。 SMBTANTD方法在相关矩阵中引入了新的目标密度，有效解决了多个传感器测量的目标数量不一致场景下的关联问题。报告的仿真结果表明，所提出的算法不仅可以准确估计传感器的系统偏差，而且可以显着提高航迹关联的性能。