Maneuvering Target State Estimation in Range-Squared Coordinate

In tracking applications without angle measurements, multiple sensors are usually applied. Accurate range-Doppler (R-D) estimates with a single sensor play an important role in clutter eliminating and date fusion in multisensor target tracking. In this article, maneuvering models are first established in the range-squared (RS) coordinate, then the R-D estimation of a maneuvering target is investigated. According to the Cartesian nearly constant acceleration and constant turn models, pseudostate vectors are defined in the RS coordinate and linear time-evolution equations of pseudostate are derived to describe the corresponding maneuvers. The motion models are incorporated into the converted measurement Kalman filter to extract R-D estimates from converted measurements. Corresponding to the models, the filter initialization approach, which utilizes the prior information on motion model and kinematic parameters of target, is developed. Based on the model set in the RS coordinate and the interacting multiple model (IMM) method, a recursive estimator is developed to produce pseudostate estimates of a maneuvering target. Final R-D estimates are provided through the conversion adopting the scaled unscented transformation outside the estimation recursions to avoid the propagation of nonlinearity approximation errors. Numerical experiments in different scenarios demonstrate that the maneuvering models accurately describe the time-evolution of pseudostate and the IMM estimator achieves superior estimation accuracy and consistency thanks to the exploitation of linear motion models.