摘要
针对临近空间目标的低可探测性和多雷达采样周期与测量精度不一的问题,提出一种基于概率网格和多维Hough变换的多雷达检测前跟踪(TDB)算法。利用概率网格将雷达测量数据概率化,通过多维Hough变换在多维空间建立初步候选航迹,利用基于不敏变换的空间融合方法得到目标数目及轨迹。仿真实验表明:该算法能够在密集杂波下实现弱小目标的有效检测。
Aiming at problems that near space target is hard to detect and sampling period and measurement precision of multi-radars are different,a track-before-detect（ TBD） algorithm for multi-radar based on probabilistic grid and multi-dimensional Hough transform is proposed. Firstly,radar measurement data are transformed into probability values by using probabilistic grid. Then,primary candidate tracks are set up in multi-dimensional space through multi-dimensional Hough transform. At last,the number and tracks of the targets are confirmed by spacefusion method based on unscented transform. Simulation results show that this algorithm can realize effective for detection of dim targets under dense clutter environment.
出处
《传感器与微系统》
CSCD
2016年第10期116-119,共4页
Transducer and Microsystem Technologies
关键词
临近空间目标
多雷达
概率网格
不敏变换
多维Hough变换
检测前跟踪
near space target
multi-radar
probabilistic grid
unscented transform
multi-dimensional Hough transform
track-before-detect（TBD）
