在预警监视领域，及时地预测目标行为模式对于态势认知至关重要。首先，针对当前大多数目标行为分类器实时性不强、对离群轨迹不敏感等问题，提出了带有离群检测的归纳式一致性多类预测器（ICMP）。其次，针对目标行为分类算法仅考虑目标的空间位置信息，而忽略航向和速度信息的问题，基于轨迹的时间和空间2个维度的信息提出了时空Hausdorff距离（STHD），从而可以有效区分空间位置相似但运动速度和方向不同的行为。然后，基于定向时空Hausdorff距离和K最近邻思想构造了定向时空Hausdorff最近邻不一致性度量函数（DSHNN-NCM）；在此基础上，提出了带有离群检测的序贯时空Hausdorff最近邻归纳式一致性多类预测器（SSHNN-ICMP），能够在预警监视场景下对目标频繁出现的行为进行在线学习和分类。最后，分别在仿真军事场景和真实民用场景中进行实验分析，结果表明本文算法具备较好的准确性和实时性，在预警监视任务中有良好的应用前景。

In the early warning and monitoring field， it is very important to predict target behavior patterns in time for situation cognition. Firstly， an Inductive Conformal Multi-class Predictor （ICMP） with outlier detection is proposed to solve the problem that most current classifiers are in low computational efficiency and not sensitive to outliers. Secondly， to overcome the problem that most behavior pattern classifiers only consider the spatial location information of the target and ignore the course and velocity information， a concept of Spatio-Temporal Hausdorff Distance （STHD） is proposed to distinguish behaviors with similar spatial position but different course and velocity effectively. Then， a Directed Spatiotemporal Hausdorff Nearest Neighbor Nonconformity Measure （DSHNN-NCM） function is constructed by combining the idea of KNN and STHD. On this basis， a Sequential Spatiotemporal Hausdorff Nearest Neighbor Inductive Conformal Multi-class Predictor （SSHNN-ICMP） with outlier detector is proposed， which can online learn and predict the frequent behaviors of targets in the early warning and monitoring scenario. Finally， experiments and analyses are carried out in a military simulation scenario and a real civilian scenario. The results show that the proposed algorithm has better accuracy and real-time performance， showing good applicability in early warning and surveillance tasks.