临近空间高超声速多目标检测前跟踪算法

Track-before-detection algorithm for multiple hypersonic targets in near space

针对临近空间不同径向距离高超声速目标难以同时被有效检测的问题,提出一种通过峰值聚优改进Hough变换积累结果的检测前跟踪(TBD)算法。采用径向距离-时间坐标描述数据,减少量测误差影响,规格化坐标使2个维度处于同一量级。进行Hough变换,分割参数单元进行点数积累和能量积累,采用标签矩阵记录每个量测点曲线经过的参数单元,遍历所有量测点将每个量测点只存于其所能构成能量积累值最大的参数单元,得到新的点数积累和能量积累结果。只对点数积累空间设置门限提取目标航迹,进行航迹约束和合并,输出最终航迹。仿真结果显示,算法将3个目标全部检测出的概率可达到90%,具有较好的多目标检测性能。

To overcome the problem that hypersonic targets with different radial distances in near space are difficult to be effectively detected at the same time,this paper proposes a Track-Before-Detect(TBD) algorithm,which improves the accumulation of Hough transform by peak convergence.First,the radial distance-time coordinate is used to describe the data to reduce the influence of measurement errors,and the normalized coordinate allows the two dimensions to be at the same order of magnitude.Hough transform is carried out,and the parameter unit is divided for point accumulation and energy accumulation.The label matrix is used to record the parameter units that each measurement point curve passes through,and all measurement points are traversed,and each measurement point is stored only in its largest parameter unit of the energy accumulation value.New points accumulation and energy accumulation results are then obtained.The threshold is only set for the point accumulation space to extract the target track,and track constraints and merge are performed to output the final track.The simulation results show that the algorithm proposed can detect all three targets with a probability of 90%,demonstrating good multi-target detection performance.