Sensor registration is an important problem that must be considered when attempting to perform any kind of data fusion in multimodal, multisensor target tracking. In this multiple target tracking (MTT) application, any inaccuracies in the registration can lead to false tracks being created, and tracks of true targets being stopped prematurely. This article introduces a method for simultaneously tracking multiple targets in a surveillance region and estimating appropriate sensor registration parameters so that sensor fusion can be performed accurately. The proposed method is based around particle belief propagation (BP), a recent but highly efficient framework for tracking multiple targets. The proposed method also uses a hierarchical model which allows for multiple processes to be linked and interact with one another. We present a comprehensive set of simulations and results using differing, asynchronous sensor setups, and compare with a random finite set (RFS) approach, namely the sequential Monte Carlo (SMC)-probability hypothesis density (PHD) filter. The results show the proposed method is 17% more accurate than the RFS approach on average.

中文翻译：

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用于同时跟踪和注册的消息传递和分层模型

传感器配准是在多模式、多传感器目标跟踪中尝试执行任何类型的数据融合时必须考虑的重要问题。在这种多目标跟踪 (MTT) 应用中，配准中的任何不准确都可能导致创建错误轨迹，并过早停止真实目标的轨迹。本文介绍了一种在监视区域内同时跟踪多个目标并估计合适的传感器配准参数以便准确执行传感器融合的方法。所提出的方法基于粒子置信传播（BP），这是一种用于跟踪多个目标的最新但高效的框架。所提出的方法还使用了一种分层模型，该模型允许多个进程相互链接和交互。我们使用不同的异步传感器设置提供了一组全面的模拟和结果，并与随机有限集 (RFS) 方法进行比较，即顺序蒙特卡罗 (SMC)-概率假设密度 (PHD) 过滤器。结果表明，所提出的方法平均比 RFS 方法准确 17%。