Inverse Cognition in Nonlinear Sensing Systems

Singh, Himali; Chattopadhyay, Arpan; Mishra, Kumar Vijay

doi:10.1109/ieeeconf56349.2022.10052061

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…The forward gain matrix K k+1 in case of I-KF is deterministic [5]. For I-EKF [9], this matrix depends on only the linearized system model at the state estimates. Hence, I-KF and I-EKF treat K k+1 as a time-varying parameter of the inverse filter's state transition.…”

Section: A Inverse Ckfmentioning

confidence: 99%

“…These works do not address the highly non-linear counteradversarial systems encountered in practice. In this regard, our recent work proposed inverse extended KF (I-EKF) for non-linear system settings in [9,10]. However, even EKF performs poorly in case of severe non-linearities and modeling errors [11], for which our follow-up work [12] introduced inverses of several EKF variants such as high-order and dithered EKFs.…”

Section: Introductionmentioning

confidence: 99%

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Inverse Cubature and Quadrature Kalman filters

Singh¹,

Mishra²,

Chattopadhyay³

2023

Preprint

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Recent developments in counter-adversarial system research have led to the development of inverse stochastic filters that are employed by a defender to infer the information its adversary may have learned. Prior works addressed this inverse cognition problem by proposing inverse Kalman filter (I-KF) and inverse extended KF (I-EKF), respectively, for linear and non-linear Gaussian state-space models. However, in practice, many counter-adversarial settings involve highly non-linear system models, wherein EKF's linearization often fails. In this paper, we consider the efficient numerical integration techniques to address such nonlinearities and, to this end, develop inverse cubature KF (I-CKF) and inverse quadrature KF (I-QKF). We derive the stochastic stability conditions for the proposed filters in the exponential-mean-squared-boundedness sense. Numerical experiments demonstrate the estimation accuracy of our I-CKF and I-QKF with the recursive Cramér-Rao lower bound as a benchmark.

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Section: A Inverse Ckfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inverse Cubature and Quadrature Kalman filters

Singh¹,

Mishra²,

Chattopadhyay³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Kalman filter is optimal for linear state space model of the target [16]. However, in practice, the state space models are non-linear, and extensions of Kalman filters are used [16], [14], [17]. In this paper, we assume that a perfect estimate, of α t , which is used by the radar controller to find the optimal radar parameter, β t , by solving the optimization problem in (2), is available to the target.…”

Section: Introductionmentioning

confidence: 99%