The intrinsic signal stability is an important performance parameter for long-term stable operation and high-precision measurement in optical current sensing. If the intrinsic signal exhibits fluctuations, drift, or jitter, it can lead to cumulative measurement errors and increased instability in the system. This can result in incorrect current measurement values, misjudgment, and misleading system operation status assessment, thereby affecting the stability and safety of the system. Therefore, ensuring the stability of the intrinsic signal is crucial for maintaining long-term stability in optical current sensing systems.Jitter in the intrinsic signal has a direct negative impact on the measurement accuracy of optical current sensing systems. Jitter introduces fluctuations in the instantaneous signal values, leading to instability and inaccuracy in the measurement results. Particularly in applications requiring high-precision current measurements, the presence of intrinsic signal jitter introduces additional errors, reducing the measurement accuracy and reliability of the system. Currently, conventional methods such as dual-path techniques can not eliminate jitter in the intrinsic signal effectively. Despite the use of dual-path techniques, the intrinsic signal is still influenced by factors such as optical components, circuit noise, and environmental interference, and the jitter can not be completely eliminated. To address the issue of intrinsic signal jitter in optical current sensing systems and improve measurement accuracy, a debouncing Kalman-based method for high-precision extraction of the intrinsic signal is proposed. This method involves a detailed analysis of the noise characteristics and optical path structure of the optical current sensing system, followed by the establishment of a mathematical model for the intrinsic signal. Subsequently, a debouncing function is introduced to modify the Kalman gain K, resulting in a Debouncing Kalman (DBKalgorithm. The debouncing Kalman algorithm aims to address the severe estimation jitter in the state estimates caused by the initial state dependence and measurement process uncertainty sensitivity of the standard Kalman gain K.In this method, the debouncing Kalman filtering algorithm utilizes the debouncing function to modify the Kalman gain K, thereby providing denoising processing for the intrinsic signal. The introduction of the debouncing function allows the Kalman filtering algorithm to better adapt to the jitter characteristics of the intrinsic signal, reducing the impact of jitter on state estimation. Compared to traditional Kalman filtering algorithms, the debouncing Kalman filtering algorithm exhibits greater stability in the state estimation process and can effectively extract high-precision estimates of the intrinsic signal. Additionally, a recursive estimation of the noise variance is introduced to ensure real-time correction of the noise variance during the filtering process.The debouncing Kalman algorithm was validated and compared with the standard Kalman algorithm through simulations in MATLAB. The simulation results show that, under the same set of parameters, the relative error of the standard Kalman algorithm reaches approximately 11% after convergence, while the debouncing Kalman algorithm achievs a relative error of approximately 2% after convergence. This validates the feasibility of the proposed algorithm. Furthermore, the stability of the algorithm was derived and verified using the Lyapunov stability analysis method. Finally, an optical current sensing experimental platform was constructed, and the proposed algorithm was implemented in parallel on the LabVIEW FPGA hardware platform. The experimental results demonstrate that the amplitude error of the filtered intrinsic component is within 2%. This verifies the real-time performance of the algorithm and its ability to meet practical engineering requirements. The successful construction of the experimental platform and the parallel implementation of the algorithm on the hardware platform further demonstrate the real-time capability and feasibility of the proposed algorithm. It provides strong support for practical applications in the field of optical current sensing and offers a high-precision and stable solution to current measurement problems in engineering practice.