The histogram-probabilistic multihypothesis tracker (H-PMHT) is an efficient, track-before-detect algorithm that can operate directly on detection surfaces such as data from a focal-plane array (FPA). It provides good performance for low computational cost but is sensitive to sensor parameter changes, e.g. number of channels, gain, normalization, etc. This is because H-PMHT interprets the measured data as a histogram of point measurements; this sensor model is analogous to an FPA counting the number of photons that fall within each of its pixels. Typically this photon count is enormous, which can overwhelm the prior distribution describing target motion. The H-PMHT algorithm addresses this issue by applying a target's prior distribution to every measurement. For a linear, Gaussian target, this results in a data-dependent scaling term on both the measurement and the process noise covariance matrices. However, if the size or gain of an FPA is changed, the photon count changes significantly, and these scaling terms can cause unpredictable performance. One solution to this problem is to change the H-PMHT so that a target's prior distribution is only applied to photons from the same target. This article describes two different approaches to constructing target prior distributions that satisfy this constraint. The benefits of adopting either of these new target prior distributions are demonstrated using simulation.

中文翻译：

---

三位先辈的故事


直方图概率多假设跟踪器 (H-PMHT) 是一种高效的检测前跟踪算法，可以直接在检测表面上运行，例如来自焦平面阵列 (FPA) 的数据。它以较低的计算成本提供良好的性能，但对传感器参数变化敏感，例如通道数、增益、归一化等。这是因为 H-PMHT 将测量数据解释为点测量的直方图；该传感器模型类似于 FPA，计算每个像素内的光子数量。通常，这个光子计数是巨大的，这可能压倒描述目标运动的先验分布。 H-PMHT 算法通过将目标的先验分布应用于每个测量来解决此问题。对于线性高斯目标，这会在测量和过程噪声协方差矩阵上产生依赖于数据的缩放项。然而，如果 FPA 的尺寸或增益发生变化，光子计数就会显着变化，并且这些缩放项可能会导致不可预测的性能。该问题的一种解决方案是更改 H-PMHT，以便目标的先验分布仅应用于来自同一目标的光子。本文介绍了两种不同的方法来构造满足此约束的目标先验分布。通过模拟证明了采用这些新目标先验分布中的任何一个的好处。