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arXiv 2003.05778eess.SPstat.ME
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非高斯噪声下基于叠加数据的多目标检测前跟踪粒子滤波

A Multi-Target Track-Before-Detect Particle Filter Using Superpositional Data in Non-Gaussian Noise

Nobutaka Ito, Simon Godsill

备注：
Multi-target tracking (MTT), track-before-detect, superpositional data, particle filter, birth/death process
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摘要：

本文提出了一种新的粒子滤波器，用于从叠加数据中联合跟踪多个目标的时变状态，这些数据依赖于所有目标的贡献之和。许多传统的跟踪方法依赖于用于检测的预处理（例如，阈值化），这严重限制了低信噪比（SNR）下的跟踪性能。相比之下，所提出的方法直接对原始传感器信号进行操作，而不需要这样的预处理。虽然也有方法适用于原始传感器信号称为检测前跟踪，所提出的方法有显着的优势。首先，它是通用的，对观测/过程噪声统计没有任何限制（例如，高斯）或每个目标对传感器的贡献的函数形式（例如，线性的、可分离的、二进制的）。特别是，它包括Salmond et al.的检测前跟踪粒子滤波器的单个目标作为一个特定的例子，直到一些实现细节。第二，它可以跟踪未知的，时变数量的目标，而不知道他们的初始状态，由于目标的出生/死亡模型。我们提出了一个模拟的例子，射频断层扫描，它显着优于Nannuru等人。的国家的最先进的方法的基础上随机有限集的最佳子模式分配（OSPA）度量。

英文摘要：

This paper proposes a novel particle filter for tracking time-varying states of multiple targets jointly from superpositional data, which depend on the sum of contributions of all targets. Many conventional tracking methods rely on preprocessing for detection (e.g., thresholding), which severely limits tracking performance at a low signal-to-noise ratio (SNR). In contrast, the proposed method operates directly on raw sensor signals without requiring such preprocessing. Though there also exist methods applicable to raw sensor signals called track-before-detect, the proposed method has significant advantages over them. First, it is general without any restrictions on observation/process noise statistics (e.g., Gaussian) or the functional form of each target's contribution to the sensors (e.g., linear, separable, binary). Especially, it includes Salmond et al.'s track-before-detect particle filter for a single target as a particular example up to some implementation details. Second, it can track an unknown, time-varying number of targets without knowing their initial states owing to a target birth/death model. We present a simulation example of radio-frequency tomography, where it significantly outperformed Nannuru et al.'s state-of-the-art method based on random finite sets in terms of the optimal subpattern assignment (OSPA) metric.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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