针对水面无人艇（USV）在进行障碍物规避过程中需要对船舶的干扰意图进行识别的问题，提出基于高斯混合模型（Gaussian mixture model，GMM）和连续隐马尔科夫模型（Continuous Hidden Markov Model，CHMM）的船舶干扰意图识别模型。首先，考虑到意图识别的复杂性，基于USV与船舶的速度障碍模型，提出多周期干扰系数等运动特征参数用作模型的输入；考虑到船舶运动的连续性，使用GMM作为观测-状态转移概率分布。然后，通过Baum-Welch算法对训练数据进行训练，得到意图识别模型。最后，获取预测样本对模型的准确性进行评估。试验结果表明：模型具有良好的识别效果，可提高USV的安全性。

Aiming at the problem that the unmanned surface vehicle (USV) needs to recognize the ship’s interference intention in the process of obstacle avoidance, a ship interference intention recognition model based on Gaussian mixture model (GMM) and continuous hidden Markov model (CHMM) was proposed. Firstly, considering the complexity of intention recognition, based on the speed obstacle model of USV and ship, the motion characteristic parameters such as multi period interference coefficient were proposed as the input of the model; and considering the continuity of ship motion, GMM was used as the observationstate transition probability distribution. Then, the training data were trained by BaumWelch algorithm to obtain the intention recognition model. Finally, the prediction samples are obtained and the accuracy of the model is evaluated. The results show that the model has good recognition effect and can improve the safety of USV.