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通常依赖于参数，这些参数不仅描述传感器系统，还可能具有任务特异性。尽管针对传感器系统调整这些变量耗时且通常需要专家知识，被跟踪目标的固有参数甚至在系统部署前可能完全不可观测。随着先进传感器系统日益复杂，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对交互式多模型滤波器的参数进行优化，无需任何真实参考数据。通过模拟数据上的消融研究评估所提方法，训练后的模型能够匹配使用真实值参数化的滤波器性能。