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Spatial aggregation using Bayesian aggregation (BA) is effective in combining multiple measurements to detect weak sources of gamma radiation in mobile source detection applications. To perform spatial aggregation of evidence, the position of the sensor must be estimated over time, in synchronization with gamma-ray measurements. Prevalent low-cost position estimation approaches often suffer from inaccuracies on a scale which can affect aggregation performance. Due to the presence of large buildings, positioning technology modalities such as GPS can show higher levels of error in urban environments. Additionally, urban environments can have highly varying structures, be crowded and dynamic, causing time-varying occlusions in the line-of-sight from the sensor to the source. Both occlusions and errors in sensor position estimation can degrade the source detection performance. We use asymptotic analysis to characterize the magnitude of this degradation. Natural maximum-likelihood and marginalization-based extensions to a BA framework are then used to improve robustness of aggregation in the face of these issues. The proposed approach shows substantially improved robustness to positioning errors and dynamic occlusions when compared to the baseline BA.