Accurate pitch estimation is essential for numerous speech processing applications, yet it remains challenging in high-distortion environments. This paper proposes a robust pitch estimation method that delivers robust pitch estimates in challenging noise environments. Our approach computes the Normalized Average Magnitude Difference Function (NAMDF), transforms it into a likelihood function, and generates probabilistic pitch states for frames at each sample shift. To enhance noise robustness, we aggregate likelihood values across integer multiples of the pitch period and neighboring frames. Furthermore, we introduce a simple yet effective continuity constraint in the Viterbi algorithm to refine pitch selection among multiple candidates. Experimental results show that our method consistently achieves lower Gross Pitch Error (GPE) and Voicing Decision Error (VDE) across various SNR levels, outperforming existing methods in both noisy and reverberant conditions.