为解决杂波环境下雷达多目标检测与跟踪时的目标漏检及数据关联错误问题，提出了一种联合分类信息、目标检测与目标跟踪的框架，利用目标与杂波在距离-多普勒谱中的分类信息辅助检测与跟踪。检测阶段设计了一种自适应检测方法，能够自适应漏检目标所处杂波背景并调整检测阈值重新检测，分类信息可过滤重检测产生的杂波量测。跟踪阶段采用神经增强消息传递算法，统一数据关联与量测分类模块，利用Dempster- Shafer规则融合概率数据关联置信与分类置信，该方式能够更好地分配不同信息源间的置信度，有效解决信息冲突的问题，提高数据关联的精度。该框架下分类信息、目标检测与目标跟踪能够有效联动，雷达实测数据与仿真数据结合的实验结果表明所提出的方法能够有效避免目标漏检导致的目标航迹跟踪不完整以及杂波与目标误关的问题，提高杂波环境下雷达目标检测与跟踪的性能。

In order to address the challenges of target omission and data association errors in radar multi-target detection and tracking in cluttered environments, we respectfully propose a framework that integrates joint classification information, target detection, and target tracking. It is proposed that the classification information of targets and clutter, based on distance-Doppler spectrum, could be utilised to assist detection and tracking. In the detection stage, we have designed an adaptive detection method which has the potential to adaptively miss detecting the clutter background in which the target is located, and adjust the detection threshold to re-detect it. Furthermore, the classification information has the capability to filter the clutter measurements generated by re-detection. In the tracking phase, a neural enhancement message passing algorithm is used to unify the data association and measurement classification module. The Dempster-Shafer rule is used to fuse the probabilistic data association confidence and classification confidence, which may help to better allocate the confidence between different information sources, effectively solve the problem of information conflict, and improve the accuracy of data association. It is thought that this framework could be an effective way of linking classification information, target detection and target tracking. The experimental results, which combine radar measurement data with simulation data, suggest that the proposed method could help to avoid the problems of incomplete target tracking caused by target omission and the problem of erroneous shutdown between clutter and target. It is also thought that this method could improve the performance of radar target detection and tracking under the clutter environment.