no code implementations • 15 Feb 2024 • Saqib Abbas Baba, Arpan Chattopadhyay
In the Bayesian setting where there is a known prior distribution of the attack beginning instant, we formulate a Bayesian quickest change detection (QCD) problem for FDI detection in order to minimize the mean detection delay subject to a false alarm probability constraint.
no code implementations • 14 May 2023 • Nirjhar Das, Arpan Chattopadhyay
In this work, we propose a novel inverse reinforcement learning (IRL) algorithm for constrained Markov decision process (CMDP) problems.
no code implementations • 4 Apr 2023 • Himali Singh, Kumar Vijay Mishra, Arpan Chattopadhyay
Rapid advances in designing cognitive and counter-adversarial systems have motivated the development of inverse Bayesian filters.
no code implementations • 18 Mar 2023 • Himali Singh, Kumar Vijay Mishra, Arpan Chattopadhyay
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.
no code implementations • 16 Mar 2023 • Ayush Aniket, Arpan Chattopadhyay
We study learning in periodic Markov Decision Process (MDP), a special type of non-stationary MDP where both the state transition probabilities and reward functions vary periodically, under the average reward maximization setting.
no code implementations • 28 Feb 2023 • Himali Singh, Arpan Chattopadhyay
In this context, we propose a novel multi-target localization algorithm in the range-angle domain for a MIMO FMCW radar with a sparse array of randomly placed transmit and receive elements.
no code implementations • 1 Oct 2022 • Himali Singh, Kumar Vijay Mishra, Arpan Chattopadhyay
In this paper, we address this scenario by formulating inverse cognition as a nonlinear Gaussian state-space model, wherein the adversary employs an unscented Kalman filter (UKF) to estimate the defender's state with reduced linearization errors.
no code implementations • 13 Aug 2022 • Himali Singh, Arpan Chattopadhyay, Kumar Vijay Mishra
The purpose of this paper and the companion paper (Part I) is to address the inverse filtering problem in non-linear systems by proposing an inverse extended Kalman filter (I-EKF).
no code implementations • 25 Jul 2022 • Ayush Aniket, Arpan Chattopadhyay
We study learning in periodic Markov Decision Process(MDP), a special type of non-stationary MDP where both the state transition probabilities and reward functions vary periodically, under the average reward maximization setting.
no code implementations • 21 Feb 2022 • Siddharth Sankar Acharjee, Arpan Chattopadhyay
In the third case, non-parametric Kolmogorov-Smirnov test is further simplified to a simple per-sample double threshold test.
no code implementations • 8 Jan 2022 • Akanksha Jaiswal, Arpan Chattopadhyay, Amokh Varma
setting, after probing a channel, the optimal source node sampling policy is shown to be a threshold policy involving the instantaneous age of the process, the available energy in the buffer and the instantaneous channel quality as the decision variables.
no code implementations • 5 Jan 2022 • Himali Singh, Arpan Chattopadhyay, Kumar Vijay Mishra
The purpose of this paper and the companion paper (Part II) is to develop the theory of I-EKF in detail.
no code implementations • 11 Oct 2021 • Akash Kumar Gupta, Arpan Chattopadhyay, Darpan Kumar Yadav
This paper proposes a novel Compressive sensing based Adaptive Defence (CAD) algorithm which combats distortion in frequency domain instead of time domain.
no code implementations • 24 May 2021 • Avinash Mohan, Arpan Chattopadhyay, Shivam Vinayak Vatsa, Anurag Kumar
Limiting the policy to this class reduces the problem to obtaining a queue switching policy at queue emptiness instants.
no code implementations • 14 Jan 2021 • Moulik Choraria, Arpan Chattopadhyay, Urbashi Mitra, Erik Strom
Each agent node computes an estimate of the process by using its sensor observation and messages obtained from neighboring nodes, via Kalman-consensus filtering.
no code implementations • 30 Oct 2020 • Mrigank Raman, Ojal Kumar, Arpan Chattopadhyay
A Lagrangian relaxation of the problem is solved by an artful blending of two tools: Gibbs sampling for MSE minimization and an on-line version of expectation maximization (EM) to estimate the unknown TPM.
no code implementations • 29 Oct 2020 • Akanshu Gupta, Abhinava Sikdar, Arpan Chattopadhyay
The quickest attack detection problem for a known {\em linear} attack scheme in the Bayesian setting with a Geometric prior on the attack initiation instant is posed as a constrained Markov decision process (MDP), in order to minimize the expected detection delay subject to a false alarm constraint, with the state involving the probability belief at the estimator that the system is under attack.
no code implementations • 9 Jul 2020 • Darpan Kumar Yadav, Kartik Mundra, Rahul Modpur, Arpan Chattopadhyay, Indra Narayan Kar
In such attacks, some or all pixel values of an image are modified by an external attacker, so that the change is almost invisible to the human eye but significant enough for a DNN-based classifier to misclassify it.