根据在线补偿对于实时性和精度的要求，提出利用粒子群算法优化支持向量回归的方法建立光纤陀螺温度误差补偿模型，并采用多数据窗的温度变化率实时获取方法，满足在线补偿和模型输入的要求.将光纤陀螺置于温箱内进行-15~50℃变温试验，获得实测数据，将温度和温度变化率作为输入，分别进行最小二乘、径向基函数神经网络以及粒子群优化支持向量回归建模，对比结果表明，提出的模型取得了最佳的补偿效果.通过实时补偿对比试验，验证了提出的模型具有良好的实时补偿性能及对于非训练数据的泛化能力.

According to real-time and precision requirements of online compensation, a support vector regression method improved by particle swarm optimization was proposed to establish the fiber optic gyroscope temperature error compensation model. A real-time acquisition method of temperature change rate based on multi-data window was adopted to meet the requirements of online compensation and model input. The fiber optic gyroscope was placed in a thermostat for temperature change test range from -15 to 50℃ to obtain the measured data. The temperature and temperature change rate were taken as inputs, and the least squares, radial basis function neural network and particle swarm optimization support vector regression algorithm were modeling respectively. The results show that the proposed model achieve the best compensation effect. The real-time compensation comparative experiment verified that the proposed model has good real-time compensation performance and generalization ability for non-training data.