用于通用叠加测量的轨迹先检测多伯努利滤波器
A Track-Before-Detect Trajectory Multi-Bernoulli Filter for Nested Superpositional Measurements
AI总结：
本文提出了一种适用于通用叠加测量的轨迹先检测多伯努利滤波器，通过高斯实现实现了计算高效的跟踪性能提升。
摘要：
本文提出了一种轨迹信息交换多伯努利（T-IEMB）滤波器，用于在具有通用叠加测量的轨迹先检测应用中估计存活和所有轨迹集。该测量模型具有叠加隐藏变量，这些变量被映射到测量的条件均值和协方差，使其能够描述广泛范围的测量模型。本文还提出了T-IEMB滤波器的高斯实现，该实现通过近似测量模型的条件矩来进行更新，并允许计算轻量的过滤解决方案。非高斯雷达跟踪场景的仿真结果展示了两种高斯T-IEMB实现的性能，相较于基于轨迹先检测的最先进粒子滤波解决方案，其在降低计算成本的同时提供了改进的跟踪性能。
英文摘要：
This paper proposes the Trajectory-Information Exchange Multi-Bernoulli (T-IEMB) filter to estimate sets of alive and all trajectories in track-before-detect applications with nested superpositional measurements. This measurement model has a nested architecture with two layers. The first layer includes superpositional hidden variables which are then mapped in the second layer to the conditional mean and covariance of the measurement, enabling it to model a broad range of measurement models. This paper also presents a Gaussian implementation of the T-IEMB filter, which performs the update by approximating the conditional moments of the measurement model, a computationally light filtering solution. Simulation results for a non-Gaussian radar-based tracking scenario demonstrate the performance of two Gaussian T-IEMB implementations, which provide improved tracking performance compared to state-of-the-art particle filters for track-before-detect, at a reduced computational cost.
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