Soft errors, a form of transient errors that cause bit flips in memory and other hardware components, are a growing concern for embedded systems as technology scales down. While hardware-based approaches to detect/correct soft errors are important, software-based techniques can be much more flexible. One simple software-based strategy would be full duplication of computations and data, and comparing the results of the corresponding original and duplicate computations. However, while the performance overhead of this strategy can be hidden during execution if there are idle hardware resources, the memory demand increase due to data duplication can be dramatic, particularly for array-based applications that process large amounts of data. Focusing on array-based embedded computing, this paper presents a memory space conscious loop iteration duplication approach that can reduce memory requirements of full duplication (of array data), without decreasing the level of reliability the latter provides. Our "in-place duplication" approach reuses the memory locations from the same array to store the duplicates of the elements of a given array. Consequently, the memory overhead brought by the duplicates can be reduced. Further, we extend this approach to incorporate "global duplication", which reuses memory locations from other arrays to store duplicates of the elements of a given array. This paper also discusses how our approach operates under a memory size constraint. The experimental results from our implementation show that the proposed approach is successful in reducing memory requirements of the full duplication scheme for twelve array-based applications.