论文标题
使用非高斯噪声中的叠加数据在检测粒子过滤器之前进行多目标轨道轨迹
A Multi-Target Track-Before-Detect Particle Filter Using Superpositional Data in Non-Gaussian Noise
论文作者
论文摘要
本文提出了一个新的粒子过滤器，用于从叠加数据共同跟踪多个目标的时变状态，该滤波器取决于所有目标的贡献之和。许多常规的跟踪方法依赖于预处理进行检测（例如阈值），这严重限制了以低信噪比（SNR）跟踪性能。相反，提出的方法直接在原始传感器信号上运行，而无需进行此类预处理。尽管也存在适用于称为Track-Be-Be-dectect的原始传感器信号的方法，但所提出的方法比它们具有显着优势。首先，这是对观察/过程噪声统计量（例如高斯）或每个目标对传感器贡献的功能形式（例如线性，可分离，二进制）的功能形式的一般限制。尤其是，它包括Salmond等人的曲目前粒子滤波器作为单个目标的粒子过滤器，作为一个特定示例，直到某些实现细节。其次，它可以跟踪一个未知的，随时间变化的目标，而不知道其初始状态，因为目标出生/死亡模型。我们提供了一个射频断层扫描的模拟示例，它在其中明显优于Nannuru等人的最新方法，基于随机有限集，就最佳的子图案（OSPA）度量而言。
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.
