光纤陀螺(FOG)输出易受环境温度的影响，发生漂移导致光纤陀螺测量精度降低。采用传统的BP 神经网络容易陷入局部极小值，导致网络训练失败。为了优化BP 神经网络，本文提出了一种粒子群(PSO)优化BP 神经网络与小波降噪相结合的光纤陀螺温度漂移补偿方法。首先分析了光纤陀螺温度漂移产生的原因；然后在不同温度下对光纤陀螺进行测试，最后采用该方法建立了光纤陀螺温度漂移模型并根据模型对光纤陀螺进行补偿，结果表明采用该方法补偿后光纤陀螺在不同温度下的输出标准差降低了60.19%，与传统的BP 神经网络相比补偿效果显著提高。

The output of fiber optic gyroscope (FOG) is easily affected by the temperature variations, so it leads to produce drift and the measurement accuracy of FOG is reduced. The traditional BP neural network is an optimization method of local search, which is easy to fall into local minimum, leading to the failure of network training. In order to optimize BP neural network, a temperature drift compensation method for FOG based on particle swarm optimization (PSO) and wavelet denoising is proposed. Firstly, the mechanism of FOG temperature drift is analyzed. Next, FOG static state test in different temperatures is finished. Finally, the FOG temperature drift model has been built by the method and compensate. The results show that the output standard deviation of FOG at different temperatures is reduced by 60.19%, and the compensation effect is better than traditional BP neural network.