IMM-CKF and Posterior Cramér-Rao Lower Bound for a Highly Maneuvering Target

We consider the dynamic state estimation of a highly maneuvering target with 6g and 8g accelerations. The target motion consists of the nearly constant velocity and nearly constant turn in clockwise andanticlockwise directions. An air moving target indicator (AMTI) radar measures the range, azimuth and radial velocity of the target. We use the interacting multiple model (IMM) filter based on the cubature Kalmanfilter (CKF) as an efficient and effective algorithm to estimate the state of the highly maneuvering target.The performance of the IMM-CKF is analyzed using the posterior Cramer-Rao lower bound (PCRLB), rootmean square (RMS) position and velocity errors, and average normalized estimation error squared(ANEES).