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.
Figures & tables
Fig. 1 : Multiobject tracking in a wireless communication network with a base station (BS) and user equipment (UE). Raw received data are used directly as measurements, enabling unmodeled propagation effects to be learned and exploited for improved tracking performance.
Fig. 2 : Block diagram of the normalizing-flow-based module used to learn the prior intensity PDF p(γ;c) online.
Object Index
Initial State (Pos. + Vel.)
Birth & Death Time
1
[10,25,0.5,0]
[2, 72]
2
[25,10,0,0.5]
[5, 75]
3
Random
[10, 70]
4
Random
[18, 78]
5
Random
[25, 75]
TABLE I : Parameters describing simulated object tracks.
Fig. 3 : Total GOSPA error and localization-error component obtained by the four considered tracking methods over 100 simulation runs.
Fig. 4 : Radar intensity maps. Left: ground-truth intensity ϱ(i)2 computed from the radar equations used for data generation. Right: mean of the learned prior intensity PDF p(γ;c) produced by the proposed neural-enhanced TkBD method.
Fig. 5 : Pointwise difference between the ground-truth radar-equation intensity and the learned prior intensity shown in Fig. 4 . The small residuals indicate that the proposed neural-enhanced TkBD method accurately learns the radar-intensity model online.
Fig. 6 : Representative tracking snapshots at time steps k=25 , 30 , 35 , and 40 , shown from top left to bottom right. Red crosses and black circles denote the true and estimated object positions at the current time step, respectively. Red curves show the true historical trajectories. The background shows the Bartlett spectrum obtained by matched filtering.
Applied Physics Laboratory University of Washington Seattle, WA 98105 USA · Paul G. Allen School of Computer Science University of Washington Seattle, WA 98105 USA