aiqbench analysis
This paper presents a neural-enhanced track-before-detect framework that leverages normalizing flows to model unmodeled propagation effects in complex-valued raw signals for multiobject tracking. By combining belief propagation with sequential Monte Carlo methods, the approach enables scalable, unsupervised online learning within a sequential Bayesian inference procedure. The work advances signal processing and machine learning techniques critical for autonomous navigation, radar/sonar systems, and 6G integrated sensing-and-communication networks.
Normalizing Flow for Neural-Enhanced Track-Before-Detect
arXiv:2610.25044v1 Announce Type: cross Abstract: Multiobject tracking (MOT) is a fundamental capability in autonomous navigation, radar and sonar systems, as well as 6G integrated sensing-and-communication (ISAC) networks. In this paper, we propose a neural-enhanced track-before-detect (TkBD) framework that operates directly on complex-valued raw signals and learns a latent representation of unmodeled propagation effects during runtime. We introduce an environment-dependent latent intensity variable that characterizes the signal power of each object in the shared propagation environment. The probability density function of this intensity variable is represented by a normalizing flow and trained online in an unsupervised manner within the sequential Bayesian inference procedure. The learning objective is derived from a variational Bayes formulation by maximizing the evidence lower bound (ELBO), thereby accounting for the uncertainties inherent in the Bayesian TkBD problem. To enable scalable inference, we make use of a recently introduced Bayesian TkBD framework that combines belief propagation, which avoids high-dimensional operations, with sequential Monte Carlo methods for efficient low-dimensional updates.
Abstract:Multiobject tracking (MOT) is a fundamental capability in autonomous navigation, radar and sonar systems, as well as 6G integrated sensing-and-communication (ISAC) networks. In this paper, we propose a neural-enhanced track-before-detect (TkBD) framework that operates directly on complex-valued raw signals and learns a latent representation of unmodeled propagation effects during runtime. We introduce an environment-dependent latent intensity variable that characterizes the signal power of each object in the shared propagation environment. The probability density function of this intensity variable is represented by a normalizing flow and trained online in an unsupervised manner within the sequential Bayesian inference procedure. The learning objective is derived from a variational Bayes formulation by maximizing the evidence lower bound (ELBO), thereby accounting for the uncertainties inherent in the Bayesian TkBD problem. To enable scalable inference, we make use of a recently introduced Bayesian TkBD framework that combines belief propagation, which avoids high-dimensional operations, with sequential Monte Carlo methods for efficient low-dimensional updates.
Signal Processing (eess.SP); Machine Learning (cs.LG)
arXiv:2610.25044 [eess.SP]
(or arXiv:2610.25044v1 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2610.25044 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 251 words