Tracking using the ground moving target indicator (GMTI) sensor measurements plays an important role in situation awareness of the battlefield, surveillance, and precision tracking of ground moving targets. The GMTI sensor measurements range, azimuth, and range-rate are nonlinear functions of the target state. The extended Kalman filter (EKF) is widely used to solve the GMTI filtering problem. Since the GMTI measurement model is nonlinear, the use of an EKF is sub-optimal. The sub-optimality depends on the degree of nonlinearity of the measurement function and GMTI measurement error covariance. We can convert polar measurements range and azimuth to Cartesian measurements and approximately treat the range-rate as a linear function of the target velocity by considering the radar line-of-sight (RLOS) vector as a constant. This allows the use linear Kalman filter (KF) with linearized measurements in an approximate way. The unscented Kalman filter (UKF) and particle filter (PF) have been shown recently as robust alternate algorithms for a wide range of nonlinear estimation problems. This paper compares the performance of the KF with linearized measurements, EKF, iterated EKF (IEKF), UKF, and PF for the GMTI measurement filtering problem using a wide range of operating conditions. Estimation accuracy, statistical consistency, and computational speed and storage are used to evaluate the performance of these estimators. We use Monte-Carlo simulations and calculate the average mean square error (MSE) matrix, normalized estimation error squared (NEES), and normalized innovation squared (NIS) to analyze the accuracy and statistical consistency.