Recent research in inverse cognition with cognitive radar has led to the development of inverse stochastic filters that are employed by the target to infer the information the cognitive radar may have learned. Prior works addressed this inverse cognition problem by proposing an inverse Kalman filter and an inverse extended Kalman filter, respectively, for linear and nonlinear Gaussian state-space models. However, in practice, many counter-adversarial settings involve highly nonlinear system models, wherein extended Kalman filter's linearization often fails. In this article, we consider the efficient numerical integration techniques to address such nonlinearities and, to this end, develop an inverse cubature Kalman filter (I-CKF), an inverse quadrature Kalman filter (I-QKF), and an inverse cubature–quadrature Kalman filter (I-CQKF). For the unknown system model case, we develop the reproducing-kernel-Hilbert-space-based cubature Kalman filter. We derive the stochastic stability conditions for the proposed filters in the exponential-mean-squared-boundedness sense and prove the filters' consistency. Numerical experiments demonstrate the estimation accuracy of our I-CKF, I-QKF, and I-CQKF with the recursive Cramér–Rao lower bound as a benchmark.
Inverse Cubature and Quadrature Kalman Filters
IEEE Transactions on Aerospace and Electronic Systems ; 60 , 4 ; 5431-5444
01.08.2024
1224659 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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