In this paper, we propose a period estimation method suitable under periodic burst noise. It is known that frequency analysis methods such as Fast Fourier Transform (FFT) have low resolution in the low-frequency band, making it difficult to analyze low-frequency signals. To solve this problem, we proposed the accumulation for real-time serial-to-parallel converter (ARS). ARS consists only of signal splitting and arithmetic mean. Furthermore, its high resolution in the low-frequency band allows it to analyze low-frequency signals with lower computational complexity than the FFT. However, ARS has the problem that it loses accuracy in a periodic burst noise environment. This phenomenon may lead to the judgement of non-anomalous conditions as anomalous. In this paper, we propose a new method to improve the accuracy of period estimation of ARS in periodic burst noise environments. Furthermore, it is shown that the proposed method provides accurate period estimation in a periodic burst noise environment.