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主要涉及两个核心操作：航迹关联与估计融合。在估计融合方面，针对单目标跟踪已提出基于传感器测量的无损测量变换方法。本文聚焦于多目标跟踪中基础且重要的航迹关联问题。首先，鉴于最优航迹关联属于多维分配问题，我们证明基于线性变换的航迹测量进行多维分配数据关联（无论是否利用先验航迹信息）具有无损性，且与直接使用原始航迹测量等价。其次，近年来信念传播算法展现出的卓越可扩展性与性能，使其在资源受限设备上实现多目标跟踪实时应用成为可能。为此，我们提出基于变换测量的BP多传感器航迹关联方法，并证明其与原始测量方法等效。最后，考虑通信约束，本地传感器采用压缩数据传输具有显著优势。本文给出两种用于航迹关联的解析无损变换方法，证明其从各传感器到融合中心的通信需求低于原始航迹测量融合方案。针对未知数量目标跟踪的数值实验表明：基于变换航迹测量的关联方法在保持与原始测量等效性能的同时，有效降低了通信带宽需求。