Data‐driven XGBoost‐based filter for target tracking

In recent years, the data‐driven approach has been introduced in the field of target tracking as a powerful tool developing the end‐to‐end mapping relationship between input features and outputs. Typically, in data‐driven methods, neural networks serve as a supplement of traditional Bayesian filters for improved estimation accuracy. However, these hybrid methods are somehow complicated to realise. In this study, inspired by the idea of direct‐mapping from measurements to states, simpler method by developing a data‐driven XGBoost‐based Filter (DXGBF) is proposed. The DXGBF consists of four components, namely data generator, sliding window, centralisation strategy and XGBoost learner (XL). The data generator generates simulated data from the probabilistic model in the training phase. By intercepting the measurements, the sliding window enables DXGBF to track targets online. The centralisation strategy extracts the relevant kinematic information from different tracks that enables DXGBF to track randomly initialised targets. The XL is responsible for learning a function that mapping to estimate states. Simulation results show that the estimation accuracy of DXGBF is higher than those of Kalman filter, sampling importance resampling particle filter and the data‐driven random‐forest‐based filter.