Pseudolinear Kalman Filter Algorithm for Target Tracking with Doppler-Bearing Measurements

This article addresses the nonlinearity of Dopplerbearing measurements for target tracking with a pseudolinear Kalman filtering (PLKF) scheme. A pseudolinear equation for range rates is derived by taking the Taylor series expansion of the radial unit vector, and a precise model of the corresponding noise is presented along with statistical analysis. To achieve superior estimation performance with low computational complexity, one-step state predictions are employed to construct an instrumental variable-based PLKF (IV-PLKF). A strategy to handle initial uncertainties is developed. Monte Carlo simulations are provided to illustrate the performance by comparing the IV-PLKF with existing algorithms and theoretical bounds.