Normalizing Flow for Neural-Enhanced Track-Before-Detect
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 th...
| Authors | Shaoxiu Wei, Bhaskar Rao, Florian Meyer |
|---|---|
| Primary category | eess.SP |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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