Inverse Particle Filter

• URL: http://arxiv.org/abs/2407.16623v2
• Date: Tue, 10 Sep 2024 14:26:03 GMT
• Title: Inverse Particle Filter
• Authors: Himali Singh, Arpan Chattopadhyay, Kumar Vijay Mishra,
• Abstract summary: In cognitive systems, recent emphasis has been placed on studying the cognitive processes of the subject whose behavior was the primary focus of the system's cognitive response. This paper adopts a global filtering approach and presents the development of an inverse particle filter (I-PF) The particle filter framework employs Monte Carlo (MC) methods to approximate arbitrary posterior distributions.
• Score: 16.975704972827305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In cognitive systems, recent emphasis has been placed on studying the cognitive processes of the subject whose behavior was the primary focus of the system's cognitive response. This approach, known as inverse cognition, arises in counter-adversarial applications and has motivated the development of inverse Bayesian filters. In this context, a cognitive adversary, such as a radar, uses a forward Bayesian filter to track its target of interest. An inverse filter is then employed to infer the adversary's estimate of the target's or defender's state. Previous studies have addressed this inverse filtering problem by introducing methods like the inverse Kalman filter (I-KF), inverse extended KF (I-EKF), and inverse unscented KF (I-UKF). However, these filters typically assume additive Gaussian noise models and/or rely on local approximations of non-linear dynamics at the state estimates, limiting their practical application. In contrast, this paper adopts a global filtering approach and presents the development of an inverse particle filter (I-PF). The particle filter framework employs Monte Carlo (MC) methods to approximate arbitrary posterior distributions. Moreover, under mild system-level conditions, the proposed I-PF demonstrates convergence to the optimal inverse filter. Additionally, we propose the differentiable I-PF to address scenarios where system information is unknown to the defender. Using the recursive Cramer-Rao lower bound and non-credibility index (NCI), our numerical experiments for different systems demonstrate the estimation performance and time complexity of the proposed filter.

Related papers

• Closed-form Filtering for Non-linear Systems [83.91296397912218]
  We propose a new class of filters based on Gaussian PSD Models, which offer several advantages in terms of density approximation and computational efficiency. We show that filtering can be efficiently performed in closed form when transitions and observations are Gaussian PSD Models. Our proposed estimator enjoys strong theoretical guarantees, with estimation error that depends on the quality of the approximation and is adaptive to the regularity of the transition probabilities.
  arXiv  Detail & Related papers  (2024-02-15T08:51:49Z)
• Inverse Unscented Kalman Filter [16.975704972827305]
  A cognitive 'adversary' tracks its target of interest via a framework such as a Kalman filter (KF) The target or 'defender' then employs another inverse unscented filter to infer the forward filter estimates of the defender computed by the adversary. For linear systems, the inverse Kalman filter (I-KF) has been recently shown to be effective in these counter-adversarial applications.
  arXiv  Detail & Related papers  (2023-04-04T10:51:43Z)
• Inverse Cubature and Quadrature Kalman filters [16.975704972827305]
  We develop inverse cubature KF (I-CKF), inverse quadrature KF (I-QKF), and inverse cubature-quadrature KF (I-CQKF) We derive the stability conditions for the proposed filters in the exponential-mean-squared-boundedness sense and prove the filters' consistency.
  arXiv  Detail & Related papers  (2023-03-18T03:48:39Z)
• Counter-Adversarial Learning with Inverse Unscented Kalman Filter [18.244578289687123]
  In counter-adversarial systems, to infer the strategy of an intelligent adversarial agent, the defender agent needs to cognitively sense the information that the adversary has gathered about the latter. We formulate inverse cognition as a nonlinear Gaussian state-space model. We then derive theoretical guarantees for the stability of IUKF in the mean-squared boundedness sense.
  arXiv  Detail & Related papers  (2022-10-01T20:31:47Z)
• Inverse Extended Kalman Filter -- Part II: Highly Non-Linear and Uncertain Systems [18.244578289687123]
  This paper proposes an inverse extended Kalman filter (I-EKF) to address the inverse filtering problem in non-linear systems. Part I: Theory of I-EKF (with and without unknown inputs) and I-KF (with unknown inputs) Part II: Theory of I-EKF (with and without unknown inputs) and I-KF (with unknown inputs)
  arXiv  Detail & Related papers  (2022-08-13T16:55:39Z)
• Computational Doob's h-transforms for Online Filtering of Discretely Observed Diffusions [65.74069050283998]
  We propose a computational framework to approximate Doob's $h$-transforms. The proposed approach can be orders of magnitude more efficient than state-of-the-art particle filters.
  arXiv  Detail & Related papers  (2022-06-07T15:03:05Z)
• Deep Learning for the Benes Filter [91.3755431537592]
  We present a new numerical method based on the mesh-free neural network representation of the density of the solution of the Benes model. We discuss the role of nonlinearity in the filtering model equations for the choice of the domain of the neural network.
  arXiv  Detail & Related papers  (2022-03-09T14:08:38Z)
• Inverse Extended Kalman Filter -- Part I: Fundamentals [19.078991171384015]
  In this paper, we develop the theory of inverse extended Kalman filter (I-EKF) in detail. We provide theoretical stability guarantees using both bounded non-linearity and unknown matrix approaches. In the companion paper (Part II), we propose reproducing kernel Hilbert space-based EKF to handle incomplete system model information.
  arXiv  Detail & Related papers  (2022-01-05T10:56:58Z)
• Reverse image filtering using total derivative approximation and accelerated gradient descent [82.93345261434943]
  We address a new problem of reversing the effect of an image filter, which can be linear or nonlinear. The assumption is that the algorithm of the filter is unknown and the filter is available as a black box. We formulate this inverse problem as minimizing a local patch-based cost function and use total derivative to approximate the gradient which is used in gradient descent to solve the problem.
  arXiv  Detail & Related papers  (2021-12-08T05:16:11Z)
• Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions [73.33133942934018]
  2D filter parametrization technique plays an important role when designing equivariant convolutions. New equivariant convolution method based on the proposed filter parametrization method, named F-Conv. F-Conv evidently outperforms previous filter parametrization based method in image super-resolution task.
  arXiv  Detail & Related papers  (2021-07-30T10:01:52Z)
• Innovative And Additive Outlier Robust Kalman Filtering With A Robust Particle Filter [68.8204255655161]
  We propose CE-BASS, a particle mixture Kalman filter which is robust to both innovative and additive outliers, and able to fully capture multi-modality in the distribution of the hidden state. Furthermore, the particle sampling approach re-samples past states, which enables CE-BASS to handle innovative outliers which are not immediately visible in the observations, such as trend changes.
  arXiv  Detail & Related papers  (2020-07-07T07:11:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.