Multisensor track-to-track fusion for target tracking involves two primary operations: track association and estimation fusion. For estimation fusion, lossless measurement transformation of sensor measurements has been proposed for single target tracking. In this paper, we investigate track association which is a fundamental and important problem for multitarget tracking. First, since the optimal track association problem is a multi-dimensional assignment (MDA) problem, we demonstrate that MDA-based data association (with and without prior track information) using linear transformations of track measurements is lossless, and is equivalent to that using raw track measurements. Second, recent superior scalability and performance of belief propagation (BP) algorithms enable new real-time applications of multitarget tracking with resource-limited devices. Thus, we present a BP-based multisensor track association method with transformed measurements and show that it is equivalent to that with raw measurements. Third, considering communication constraints, it is more beneficial for local sensors to send in compressed data. Two analytical lossless transformations for track association are provided, and it is shown that their communication requirements from each sensor to the fusion center are less than those of fusion with raw track measurements. Numerical examples for tracking an unknown number of targets verify that track association with transformed track measurements has the same performance as that with raw measurements and requires fewer communication bandwidths.

翻译：多传感器航迹-航迹融合用于目标跟踪涉及两个主要操作：航迹关联和估计融合。对于估计融合，已在单目标跟踪中提出传感器测量的无损测量变换。本文研究航迹关联，这是多目标跟踪中的一个基础且重要的问题。首先，由于最优航迹关联问题是一个多维分配（MDA）问题，我们证明基于MDA的数据关联（使用和不使用先验航迹信息）通过航迹测量的线性变换是无损的，且等效于使用原始航迹测量。其次，信念传播（BP）算法近期在可扩展性和性能上的优越性使其能够实现资源受限设备上的新型实时多目标跟踪应用。因此，我们提出一种基于BP的多传感器航迹关联方法，使用变换后的测量，并证明其与使用原始测量的方法等效。第三，考虑通信约束，本地传感器发送压缩数据更为有利。我们提供两种用于航迹关联的解析无损变换，并证明从每个传感器到融合中心的通信需求低于使用原始航迹测量的融合方式。跟踪未知数量目标的数值示例验证了使用变换航迹测量的航迹关联与使用原始测量具有相同性能，且需要更少的通信带宽。