To address the challenge of fluctuation jamming in radar target detection, this paper utilizes probability density functions to analyze the fluctuation characteristics of Radar Cross Section (RCS) measurement data. By leveraging existing mature distribution models, the study fits these models to the RCS distribution and compares the results with empirical data. Through this process, an improved statistical distribution model of RCS is developed, specifically tailored for accurately describing vehicle targets. The newly established model achieves a goodness of fit exceeding 90%, indicating a high level of accuracy and reliability. This model provides a crucial foundation for advancing research in vehicle target detection and recognition, offering enhanced performance in identifying and tracking vehicle targets in various radar applications. By addressing the limitations of previous models and providing a robust solution to fluctuation jamming, this work contributes significantly to the field of radar technology and its practical implementations in both civilian and military contexts.