Department of Defense Systems Engineering, Sejong University, Seoul 05006, Korea.
Department of Computer Science, Bahria University, Karachi Campus, Karachi 74200, Pakistan.
Sensors (Basel). 2022 Jun 23;22(13):4759. doi: 10.3390/s22134759.
This research work extends the fixed interval smoothing based on the joint integrated track splitting (FIsJITS) filter in the multi-maneuvering-targets (MMT) tracking environment. We contribute to tackling unknown dynamics of the multi-maneuvering-targets (MMT) using the standard kinematic model. This work is referred to as smoothing MMT using the JITS (MMT-sJITS). The existing FIsJITS algorithm is computationally more complex to solve for the MMT situation because it enumerates a substantial number of measurement-to-track assignments and calculates their posteriori probabilities globally. The MMT-sJITS updates a current target track by assuming the joint (common) measurements detected by neighbor tracks are modified clutters (or pretended spurious measurements). Thus, target measurement concealed by a joint measurement is optimally estimated based on measurement density of the modified clutter. This reduces computational complexity and provides improved tracking performance. The MMT-sJITS generates forward tracks and backward tracks using the measurements collected by a sensor such as a radar. The forward and backward multi-tracks state predictions are fused to obtain priori smoothing multi-track state prediction, as well as their component existence probabilities. This calculates the smoothing estimate required to compute the forward JITS state estimate, which reinforces the MMT tracking efficiently. Monte Carlo simulation is used to verify best false-track discrimination (FTD) analysis in comparison with existing multi-targets tracking algorithms.
这项研究工作扩展了基于联合积分航迹分裂（FITS）滤波器的固定间隔平滑（FITS）在多机动目标（MMT）跟踪环境中的应用。我们通过使用标准运动学模型来解决多机动目标（MMT）的未知动态问题。这项工作被称为使用 JITS 的 MMT 平滑（MMT-sJITS）。由于需要枚举大量的测量到航迹分配，并全局计算它们的后验概率，因此现有的 FITS 算法在解决 MMT 情况时计算复杂度更高。MMT-sJITS 通过假设由邻居航迹检测到的联合（共同）测量是修改后的杂波（或假装的虚假测量）来更新当前目标航迹。因此，基于修改后的杂波的测量密度，对被联合测量隐藏的目标测量进行了最优估计。这降低了计算复杂度并提供了更好的跟踪性能。MMT-sJITS 使用传感器（如雷达）收集的测量值生成前向航迹和后向航迹。融合前向和后向多航迹状态预测，以获得先验平滑多航迹状态预测及其分量存在概率。这计算了所需的平滑估计值，以计算前向 JITS 状态估计值，从而有效地增强了 MMT 跟踪。通过与现有的多目标跟踪算法进行比较，使用蒙特卡罗模拟验证了最佳虚假航迹判别（FTD）分析。
