Efficient Inverse Cubature and Quadrature Kalman Filters for Estimating Adversarial Cognitive Agents

This paper develops efficient inverse cubature Kalman filter (I-CKF), inverse quadrature Kalman filter (I-QKF), and inverse cubature-quadrature Kalman filter (I-CQKF) to estimate the state inferred by an adversarial cognitive radar. The proposed methods can handle highly non-linear system models where extended Kalman filter's linearization often fails. The paper also derives stability and consistency conditions for the inverse filters and demonstrates their improved estimation accuracy compared to the recursive Cramér-Rao lower bound.