Advanced optimization strategy for using Kalman filter to deeply eliminate the impact of interference on the accuracy of effective signals in measurement and control systems

In the measurement and control system, A/D (Analog-to-Digital) acquisition of signals is extremely crucial. However, due to factors such as external interference, the signals are contaminated, leading to significant errors between the digital values acquired by A/D conversion and the actual signals, thus failing to guarantee the accuracy of A/D sampling. To address this issue and effectively reduce the impact of interference on the genuine sampling signals, this paper analyzes common A/D filtering algorithms and employs the Kalman filter algorithm to eliminate the influence of large-amplitude sinusoidal interference on effective signals within the measurement and control system. Experimental results demonstrate that the sampling accuracy achieved by the Kalman filter algorithm for software filtering reaches 8‰, with an 11% improvement in convergence speed. Furthermore, the A/D sampling results are less susceptible to large fluctuations and errors caused by random changes in interference, maintaining a high level of precision.