Interference significantly impacts the performance of the Global Navigation Satellite Systems (GNSS), highlighting the need for advanced interference localization technology to bolster anti-interference and defense capabilities. The Uniform Circular Array (UCA) enables concurrent estimation of the Direction of Arrival (DOA) in both azimuth and elevation. Given the paramount importance of stability and real-time performance in interference localization, this work proposes an innovative approach to reduce the complexity and increase the robustness of the DOA estimation. The proposed method reduces computational complexity by selecting a reduced number of array elements to reconstruct a non-uniform sparse array from a UCA. To ensure DOA estimation accuracy, minimizing the Cramér-Rao Bound (CRB) is the objective, and the Spatial Correlation Coefficient (SCC) is incorporated as a constraint to mitigate side-lobe. The optimization model is a quadratic fractional model, which is solved by Semi-Definite Relaxation (SDR). When the array has perturbations, the mathematical expressions for CRB and SCC are re-derived to enhance the robustness of the reconstructed array. Simulation and hardware experiments validate the effectiveness of the proposed method in estimating interference DOA, showing high robustness and reductions in hardware and computational costs associated with DOA estimation.