Learning IMM Filter Parameters from Measurements using Gradient Descent
This addresses the challenge of tuning complex sensor systems without ground-truth data, which is incremental as it applies gradient descent to an existing filter method.
The paper tackles the problem of automatically optimizing parameters for an interacting multiple model (IMM) filter in data fusion and tracking, achieving performance that matches a filter with ground-truth parameters on simulated data.
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.