The performance of data fusion and tracking algorithms often depends on parameters that not only describe the sensor system, but can also be task-specific. While for the sensor system tuning these variables is time-consuming and mostly requires expert knowledge, intrinsic parameters of targets under track can even be completely unobservable until the system is deployed. With state-of-the-art sensor systems growing more and more complex, the number of parameters naturally increases, necessitating the automatic optimization of the model variables. In this paper, the parameters of an interacting multiple model (IMM) filter are optimized solely using measurements, thus without necessity for any ground-truth data. The resulting method is evaluated through an ablation study on simulated data, where the trained model manages to match the performance of a filter parametrized with ground-truth values.

翻译：数据融合与跟踪算法的性能常取决于描述传感器系统的参数，这些参数也可能具有任务特异性。虽然为传感器系统调整这些变量既耗时又通常需要专业知识，但被跟踪目标的固有参数甚至可能在系统部署前完全无法观测。随着先进传感器系统的日益复杂，参数数量自然增加，因此需要自动优化模型变量。本文仅利用测量数据优化交互式多模型（IMM）滤波器的参数，从而无需任何真实数据。通过在模拟数据上的消融研究评估所提方法，结果表明训练后的模型能够达到与使用真实值参数化的滤波器相当的性能。