Department of Electrical and Computer Engineering, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan.
Department of Computer Science, Abbottabad Campus, COMSATS University Islamabad, Abbottabad 22060, Pakistan.
Sensors (Basel). 2022 Oct 16;22(20):7848. doi: 10.3390/s22207848.
The proposed work uses fixed lag smoothing on the interactive multiple model-integrated probabilistic data association algorithm (IMM-IPDA) to enhance its performance. This approach makes use of the advantages of the fixed lag smoothing algorithm to track the motion of a maneuvering target while it is surrounded by clutter. The suggested method provides a new mathematical foundation in terms of smoothing for mode probabilities in addition to the target trajectory state and target existence state by including the smoothing advantages. The suggested fixed lag smoothing IMM-IPDA (FLs IMM-IPDA) method's root mean square error (RMSE), true track rate (TTR), and mode probabilities are compared to those of other recent algorithms in the literature in this study. The results clearly show that the proposed algorithm outperformed the already-known methods in the literature in terms of these above parameters of interest.
本研究提出在交互式多模型集成概率数据关联算法(IMM-IPDA)中使用固定滞后平滑来提高其性能。该方法利用固定滞后平滑算法的优势,在杂波环境中跟踪机动目标的运动。所提出的方法通过包括平滑优势,除了目标轨迹状态和目标存在状态之外,还为模式概率提供了平滑的新数学基础。在这项研究中,将所提出的固定滞后平滑 IMM-IPDA(FLs IMM-IPDA)方法的均方根误差(RMSE)、真实轨迹率(TTR)和模式概率与文献中其他最近算法的进行了比较。结果清楚地表明,在所关注的这些参数方面,所提出的算法优于文献中已有的方法。