This paper addresses the issue of detecting point objects in a clutter background and estimating their position by image processing. We are interested in the specific context where the object signature significantly varies with its random subpixel location because of aliasing. Conventional matched filter neglects this phenomenon and causes consistent loss of detection performance. Thus, alternative detectors are proposed and numerical results show the improvement brought by approximate and generalized likelihood ratio tests in comparison with pixel matched filtering. We also study the performance of two types of subpixel position estimators. Finally, we put forward the major influence of sensor design on both estimation and point object detection performance.