Signal to Noise Ratio(SNR) estimation is important for channel estimation. Many communication systems and signal processing algorithms need SNR as prior information. SNR estimation algorithms such as max likelihood, statistics and space breaking algorithms are simulated based on Multiple Phase Shift Keying(MPSK) signal models. The estimation bias of these algorithms can be less than 1 dB under some conditions in [0,20] dB. Max likelihood algorithm is the most exact one, but is liable to be influenced by demodulation errors. Statistics algorithm is better in low SNR than in high SNR because of the algorithms’ noises. Space breaking algorithm is the most adaptable, but has worse real-time performance. Through analyzing the consistency and diversity, the research advancements and existing problems are summarized and the research direction such as complex modulation signals SNR estimation in wide area and space breaking methods are confirmed. In the end, some solutions and improvements are proposed to resolve the existing problems.