  We propose a scalable track-before-detect (TBD) tracking method based on a
Poisson/multi-Bernoulli model. To limit computational complexity, we
approximate the exact multi-Bernoulli mixture posterior probability density
function (pdf) by a multi-Bernoulli pdf. Data association based on the
sum-product algorithm and recycling of Bernoulli components enable the
detection and tracking of low-observable objects with limited computational
resources. Our simulation results demonstrate a significantly improved tracking
performance compared to a state-of-the-art TBD method.
