Estimation & Recognition under Perspective of Random-Fuzzy Dual
Interpretation of Unknown Quantity: with Demonstration of IMM Filter
- URL: http://arxiv.org/abs/2110.10572v2
- Date: Tue, 2 Nov 2021 00:51:20 GMT
- Title: Estimation & Recognition under Perspective of Random-Fuzzy Dual
Interpretation of Unknown Quantity: with Demonstration of IMM Filter
- Authors: Wei Mei, Yunfeng Xu, Limin Liu
- Abstract summary: Two related key issues are addressed: 1) the random-fuzzy dual interpretation of unknown quantity being estimated; and 2) the principle of selecting sigma-max operator for practical problems.
We show that continuous unknown quantity involved in estimation with inaccurate prior should be more appropriately modeled as randomness and handled by sigma inference.
For our example of maneuvering target tracking using simulated data from both a short-range fire control radar and a long-range surveillance radar, the updated IMM filter shows significant improvement over the classic IMM filter.
- Score: 1.7188280334580195
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper is to consider the problems of estimation and recognition from the
perspective of sigma-max inference (probability-possibility inference), with a
focus on discovering whether some of the unknown quantities involved could be
more faithfully modeled as fuzzy uncertainty. Two related key issues are
addressed: 1) the random-fuzzy dual interpretation of unknown quantity being
estimated; 2) the principle of selecting sigma-max operator for practical
problems, such as estimation and recognition. Our perspective, conceived from
definitions of randomness and fuzziness, is that continuous unknown quantity
involved in estimation with inaccurate prior should be more appropriately
modeled as randomness and handled by sigma inference; whereas discrete unknown
quantity involved in recognition with insufficient (and inaccurate) prior could
be better modeled as fuzziness and handled by max inference. The philosophy was
demonstrated by an updated version of the well-known interacting multiple model
(IMM) filter, for which the jump Markovian System is reformulated as a hybrid
uncertainty system, with continuous state evolution modeled as usual as
model-conditioned stochastic system and discrete mode transitions modeled as
fuzzy system by a possibility (instead of probability) transition matrix, and
hypotheses mixing is conducted by using the operation of "max" instead of
"sigma". For our example of maneuvering target tracking using simulated data
from both a short-range fire control radar and a long-range surveillance radar,
the updated IMM filter shows significant improvement over the classic IMM
filter, due to its peculiarity of hard decision of system model and a faster
response to the transition of discrete mode.
Related papers
- TrackDiffuser: Nearly Model-Free Bayesian Filtering with Diffusion Model [23.40376181606577]
We present TrackDiffuser, a generative framework addressing both challenges by reformulating Bayesian filtering as a conditional diffusion model.
Our approach implicitly learns system dynamics from data to mitigate the effects of inaccurate SSM.
TrackDiffuser exhibits remarkable robustness to SSM inaccuracies, offering a practical solution for real-world state estimation problems.
arXiv Detail & Related papers (2025-02-08T16:21:18Z) - DiverseAgentEntropy: Quantifying Black-Box LLM Uncertainty through Diverse Perspectives and Multi-Agent Interaction [53.803276766404494]
Existing methods, which gauge a model's uncertainty through evaluating self-consistency in responses to the original query, do not always capture true uncertainty.
We propose a novel method, DiverseAgentEntropy, for evaluating a model's uncertainty using multi-agent interaction.
Our method offers a more accurate prediction of the model's reliability and further detects hallucinations, outperforming other self-consistency-based methods.
arXiv Detail & Related papers (2024-12-12T18:52:40Z) - Continuous Bayesian Model Selection for Multivariate Causal Discovery [22.945274948173182]
Current causal discovery approaches require restrictive model assumptions or assume access to interventional data to ensure structure identifiability.
Recent work has shown that Bayesian model selection can greatly improve accuracy by exchanging restrictive modelling for more flexible assumptions.
We demonstrate the competitiveness of our approach on both synthetic and real-world datasets.
arXiv Detail & Related papers (2024-11-15T12:55:05Z) - Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation [50.920911532133154]
The intrinsic ill-posedness and ordinal-sensitive nature of monocular depth estimation (MDE) models pose major challenges to the estimation of uncertainty degree.
We propose to model the uncertainty of MDE models from the perspective of the inherent probability distributions.
By simply introducing additional training regularization terms, our model, with surprisingly simple formations and without requiring extra modules or multiple inferences, can provide uncertainty estimations with state-of-the-art reliability.
arXiv Detail & Related papers (2023-07-19T12:11:15Z) - Regularized Vector Quantization for Tokenized Image Synthesis [126.96880843754066]
Quantizing images into discrete representations has been a fundamental problem in unified generative modeling.
deterministic quantization suffers from severe codebook collapse and misalignment with inference stage while quantization suffers from low codebook utilization and reconstruction objective.
This paper presents a regularized vector quantization framework that allows to mitigate perturbed above issues effectively by applying regularization from two perspectives.
arXiv Detail & Related papers (2023-03-11T15:20:54Z) - On Uncertainty in Deep State Space Models for Model-Based Reinforcement
Learning [21.63642325390798]
We show that RSSMs use a suboptimal inference scheme and that models trained using this inference overestimate the aleatoric uncertainty of the ground truth system.
We propose an alternative approach building on well-understood components for modeling aleatoric and epistemic uncertainty, dubbed Variational Recurrent Kalman Network (VRKN)
Our experiments show that using the VRKN instead of the RSSM improves performance in tasks where appropriately capturing aleatoric uncertainty is crucial.
arXiv Detail & Related papers (2022-10-17T16:59:48Z) - Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic
Dynamical Models with Epistemic Uncertainty [68.00748155945047]
Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers.
Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability.
Our contribution is a novel abstraction-based controller method for continuous-state models with noise, uncertain parameters, and external disturbances.
arXiv Detail & Related papers (2022-10-12T07:57:03Z) - Dense Uncertainty Estimation via an Ensemble-based Conditional Latent
Variable Model [68.34559610536614]
We argue that the aleatoric uncertainty is an inherent attribute of the data and can only be correctly estimated with an unbiased oracle model.
We propose a new sampling and selection strategy at train time to approximate the oracle model for aleatoric uncertainty estimation.
Our results show that our solution achieves both accurate deterministic results and reliable uncertainty estimation.
arXiv Detail & Related papers (2021-11-22T08:54:10Z) - Identification of Probability weighted ARX models with arbitrary domains [75.91002178647165]
PieceWise Affine models guarantees universal approximation, local linearity and equivalence to other classes of hybrid system.
In this work, we focus on the identification of PieceWise Auto Regressive with eXogenous input models with arbitrary regions (NPWARX)
The architecture is conceived following the Mixture of Expert concept, developed within the machine learning field.
arXiv Detail & Related papers (2020-09-29T12:50:33Z)
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.