Efficient Direct Localization Based on Sparsity‐Gradient Hybrid Optimization

To address the trade‐off between localization accuracy and computational efficiency in direct position determination (DPD), this paper proposes a novel hybrid optimization method that integrates correlation‐domain sparsity with gradient‐guided refinement. The proposed method achieves sub‐grid localization accuracy while significantly reducing computational complexity. Extensive simulations indicate that the method maintains high precision across a wide range of signal‐to‐noise ratios (SNRs) and sample sizes, achieving an order‐of‐magnitude reduction in runtime compared to both conventional and sparse DPD techniques.