Filtering of systems with nonlinear measurements with an application to target tracking

This paper studies the problem of recursive state estimation of stochastic linear systems with nonlinear measurements. The main idea is to rewrite the measurement map in a linear form by considering, as system output, a vector of “virtual” measurements. The result is a linear system with a non-Gaussian and nonstationary output noise. State estimation is therefore obtained using a Kalman filter or, alternatively, a quadratic filter, suitably designed for non-Gaussian systems. This work provides two sufficient conditions for the application of the virtual measurement approach and shows its effectiveness in the case of the maneuvering target tracking problem.