具有自动回溯的机动目标航迹精细化分段识别

机动目标航迹分段识别是判断目标行为意图的基础，然而现有航迹分段算法对模式变化检测能力弱，难以满足机动目标航迹快速精细化分段的需求。提出双层精细化航迹分段框架，预分段层检测目标运动过程中的模式切换，确定模式变化明显的预分段区，得到目标模式变化明显区域的预分段点；再分段层对模型差异小的非预分段区航迹进行回溯迭代优化再分段，得到更为精细的分段点。该框架具有从粗到精的航迹分段处理能力，实现了对于机动目标航迹的精细化分段识别。选取两个典型的目标机动仿真场景验证了所提算法的有效性，不仅减少了迭代优化时间，而且提高了分段识别精度。

Automatic backtracking-based refined segment recognition of maneuvering target track

Segment recognition of the maneuvering target track is the basis for judging the intention of target's behavior. However, existing track segmentation algorithms have a weak ability to detect changes of pattern, and are thus difficult to meet the requirement of fast and refined track segmentation for maneuvering targets. To this end, our paper proposes a two-layer refined track segmentation framework. The pre-segmentation layer is used to detect the pattern switching during the movement of the target, so as to determine the pre-segment area with obvious pattern changes and obtain the pre-segment points of the area with obvious target pattern changes. Then, iterative backtracking optimization is used to segment the track of the non-pre-segmented area with small differences, so as to obtain more refined segmentation points. The framework has the ability to process the track from coarse to fine segmentation, and can realize the recognition of refined segment of the maneuvering target track. Finally, the simulation results of two typical target maneuvering scenarios are given to demonstrate the effectiveness of our proposed method, which can not only reduce the time of iterative optimization, but also improve segmentation accuracy.