Joint Estimation of States and Parameters of a Reentry Ballistic Target Using Adaptive UKF

The problem of joint estimation of states and parameters of a reentry ballistic target in the situation where the measurement noise covariance is unknown or incorrectly known has been addressed here and towards that end an Adaptive Unscented Kalman Filter (AUKF) based joint estimation technique has been presented. The presented AUKF algorithm has utilized (i) residual sequences for the adaptation of measurement noise covariance matrix (R) to guarantee positive definiteness and (ii) an iterative measurement update step to further improve the estimation performance. Simulation results demonstrate that adapted measurement noise covariance converges to its truth value and can also successfully track the truth value when it is time varying. From Monte Carlo studies it is assessed that the joint estimation performance of the presented adaptive estimator is superior compared to its non adaptive counter part.