为了满足陆战场识别、民用目标监视等军民多用途应用中目标行为预测需求，需要基于目标位置、运动状态等信息进行推理，实现目标机动意图的有效判断。针对目标原始地理位置无法提供语义信息问题，采用模糊 隶属理论构建道路网格模型，对目标的位置语义特征进行提取，并基于 K最近邻法克服位置误差可能导致的位置语义错误；在位置语义建模基础上，利用隐马尔可夫模型 (HMM)，对目标的机动意图进行推理。最后结合机场 场面监视的应用，通过仿真验证了采用位置语义建模和 K最近邻方法后的行为推理相较于一般隐马尔可夫推理的准确性改善。

It is necessary to carry out the reasoning to realize effective judgement of target maneuver recognition based on information of target position and motion state in order to meet the needs of target behavior prediction in the multi-purpose application such as battlefield identification and civilian target surveillance. Aiming at the problem that the original geographical location cannot provide semantic information, this paper adopts fuzzy membership theory to construct road grid model to extract semantic feature of target position, and the possible derivation error of location semantic is avoided by using K Nearest Neighbor(KNN) method. On the basis of positional semantic modeling, the target maneuver action is inferred with Hidden Markov Model(HMM). Combining with the application of airport surface surveillance, the simulation results show that the accuracy of the proposed method based on model of location sematic feature and KNN is improved compared with the general HMM.