The rapidly and continually back-calculating source term is important for nuclear emergency response .The Gaussian multi-puff atmospheric dispersion model was used to produce regional environment monitoring data virtually , and then a Kalman filter was designed to inverse radionuclide release rate of nuclear accidents in nuclear facility and the release rate tracking in real time was achieved .The results show that the Kalman filter combined with Gaussian multi-puff atmospheric dispersion model can successfully track the virtually stable ,linear or nonlinear release rate after being iterated about 10 times .The standard error of inversion results increases with the true value . Meanwhile extended Kalman filter cannot inverse the height parameter of accident release as interceptive error is too large to converge . Kalman filter constructed from environment monitoring data and Gaussian multi-puff atmospheric dispersion model can be applied to source inversion in nuclear accident w hich is characterized by static height and position ,short and continual release in nuclear facility .Hence it turns out to be an alternative source inversion method in nuclear emergency response .%为能在核设施发生核事故时快速连续反演核素释放率，本文结合高斯多烟团大气扩散模型，模拟固定区域的连续监测数据，设计并实现了核设施核事故核素释放率的卡尔曼滤波实时跟踪反演。研究结果表明：与高斯多烟团大气扩散模型结合的卡尔曼滤波器，在约10次滤波后，跟踪到虚设的稳定、线性及非线性变化的释放率真值，反演值标准差随真值的增大而增大；在扩展卡尔曼滤波反演释放高度时，由于截断误差过大，滤波结果不收敛。利用环境监测数据，通过与高斯多烟团大气扩散模型结合的卡尔曼滤波器可用于固定高度和位置、短时连续排放的核事故核素释放率参数反演，是核设施核事故应急可选择的源项反演手段。
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