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.