3 research outputs found
Fast Variational Block-Sparse Bayesian Learning
We present a fast update rule for variational block-sparse Bayesian learning
(SBL) methods. Using a variational Bayesian framework, we show how repeated
updates of probability density functions (PDFs) of the prior variances and
weights can be expressed as a nonlinear first-order recurrence from one
estimate of the parameters of the proxy PDFs to the next. Specifically, the
recurrent relation turns out to be a strictly increasing rational function for
many commonly used prior PDFs of the variances, such as Jeffrey's prior. Hence,
the fixed points of this recurrent relation can be obtained by solving for the
roots of a polynomial. This scheme allows to check for convergence/divergence
of individual prior variances in a single step. Thereby, the the computational
complexity of the variational block-SBL algorithm is reduced and the
convergence speed is improved by two orders of magnitude in our simulations.
Furthermore, the solution allows insights into the sparsity of the estimators
obtained by choosing different priors.Comment: 10 pages, 2 figures, submitted to IEEE Transactions on Signal
Processing on 1st of June, 202
"UWBCarGraz" Dataset for Car Occupancy Detection using Ultra-Wideband Radar
We present a data-driven car occupancy detection algorithm using
ultra-wideband radar based on the ResNet architecture. The algorithm is trained
on a dataset of channel impulse responses obtained from measurements at three
different activity levels of the occupants (i.e. breathing, talking, moving).
We compare the presented algorithm against a state-of-the-art car occupancy
detection algorithm based on variational message passing (VMP). Our presented
ResNet architecture is able to outperform the VMP algorithm in terms of the
area under the receiver operating curve (AUC) at low signal-to-noise ratios
(SNRs) for all three activity levels of the target. Specifically, for an SNR of
-20 dB the VMP detector achieves an AUC of 0.87 while the ResNet architecture
achieves an AUC of 0.91 if the target is sitting still and breathing naturally.
The difference in performance for the other activities is similar. To
facilitate the implementation in the onboard computer of a car we perform an
ablation study to optimize the tradeoff between performance and computational
complexity for several ResNet architectures. The dataset used to train and
evaluate the algorithm is openly accessible. This facilitates an easy
comparison in future works.Comment: v1 (17.11.2023). 6 pages, 5 figure