Passive monitoring of acoustic or radio sources has important applications in modern convenience, public safety, and surveillance. A key task in passive monitoring is multiobject tracking (MOT). This paper presents a Bayesian method for multisensor MOT for challenging tracking problems where the object states are high-dimensional, and the measurements follow a nonlinear model. Our method is developed in the framework of factor graphs and the sum-product algorithm (SPA) and implemented using random samples or "particles". The multimodal probability density functions (pdfs) provided by the SPA are effectively represented by a Gaussian mixture model (GMM). To perform the operations of the SPA with improved sample efficiency, we make use of Particle flow (PFL). Here, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. This makes it possible to obtain good object detection and tracking performance even in challenging multisensor MOT scenarios with single sensor measurements that have a lower dimension than the object positions. We perform a numerical evaluation in a passive acoustic monitoring scenario where multiple sources are tracked in 3-D from 1-D time-difference-of-arrival (TDOA) measurements provided by pairs of hydrophones. Our numerical results demonstrate favorable detection and estimation accuracy compared to state-of-the-art reference techniques.

翻译：声学或无线电源的被动监测在现代便利性、公共安全及监视领域具有重要应用。被动监测中的核心任务是多目标跟踪（MOT）。本文针对状态维度高、测量模型非线性的复杂跟踪问题，提出一种用于多传感器MOT的贝叶斯方法。该方法基于因子图框架与和积算法（SPA）构建，并通过随机样本（即"粒子"）实现。SPA生成的多模态概率密度函数（pdf）采用高斯混合模型（GMM）高效表征。为提升采样效率以执行SPA运算，我们引入粒子流（PFL）技术——通过求解偏微分方程将粒子迁移至高似然区域，从而在单传感器测量维度低于目标位置维度的严峻多传感器MOT场景中，仍能获得良好的目标检测与跟踪性能。我们在被动声学监测场景下开展数值评估，该场景基于水听器阵列提供的1维到达时间差（TDOA）测量信号，对3维空间中的多个声源进行跟踪。实验结果表明，相较于现有顶尖参考技术，本方法在检测与估计精度方面具有显著优势。