OPTIMIZING THE PERFORMANCE OF RMS MEASUREMENT ALGORITHM USING GAUSSIAN FILTER
Mohammed A. T. Alrubei · Kufa Journal of Engineering · 2025
Root mean square (RMS) values must be measured precisely in the present era in order to improve the performance precision and high dependability of wireless communication equipment. The usual method of measuring RMS, which involves calculating the average of the squares of the input signal samples, has been the subject of numerous research articles. However, there are certain disadvantages to this approach, particularly when working with harmonic signals. In this paper, three formulas are proposed to estimate the systematic error in RMS measurement to contribute to solving this problem. The proposed formulas take a new approach and demonstrate the effect of the number of input signal samples and the type of domain used (time or frequency) on measurement accuracy. To demonstrate this change, graphs were created using MATLAB simulation. In this paper, the Gaussian window was tested in both the time and frequency domains, and the study demonstrated that the RMS error can be significantly reduced if the correct parameter is chosen and a suitable sample size is used for both domains. At an SNR of 30 dB, the impact of sample size on the outcomes is also described. When the simulation parameters were set to Fc/Fs = 0.25, the MEE findings were (0.00029, 0.000016, and 0.0000054) for sample sizes of 16, 32, and 64, respectively. The results obtained in this paper demonstrate the importance of selecting appropriate parameters for each application, confirming and enhancing the applicability of this research in most applications to support the high performance, high accuracy, and reliability of modern wireless communication devices. Several techniques and methods in this field have been reviewed, and the current RMS evaluation error was found to be approximately 10-4 (Sean Reiter and Stephen W. 2024), while the methodology proposed in this paper produces lower errors of approximately 10-5