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Penalty Dynamic Programming Algorithm for Dim Targets Detection in Sensor Systems

机译:传感器系统中昏暗目标检测的惩罚性动态规划算法

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摘要

In order to detect and track multiple maneuvering dim targets in sensor systems, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. The performances of tracking techniques are used as a feedback to the detection part. The feedback is constructed by a penalty term in the merit function, and the penalty term is a function of the possible target state estimation, which can be obtained by the tracking methods. With this feedback, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation. Thus, the algorithm can be used in the multi-target situation with unknown target numbers. The efficiency and advantages of PDP-TBD compared with two existing methods are demonstrated by several simulations.
机译:为了检测和跟踪传感器系统中的多个机动昏暗目标,提出了一种改进的动态编程先探测后跟踪算法(DP-TBD),称为罚DP-TBD(PDP-TBD)。跟踪技术的性能被用作对检测部分的反馈。反馈是由价值函数中的惩罚项构建的,惩罚项是可能的目标状态估计的函数,可以通过跟踪方法获得该目标状态估计。有了这种反馈,该算法将传统的跟踪技术与DP-TBD相结合,可以应用于同时检测和跟踪机动的暗淡目标。同时,提出了一种合理的约束条件,即传感器测量值可以源自一个目标或杂波,以最大程度地减小轨迹间隔。因此,该算法可用于目标编号未知的多目标情况。通过几次仿真证明了PDP-TBD与两种现有方法相比的效率和优势。

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