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Association-Based Track-Before-Detect With Object Contribution Probabilities
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AI
DOI：10.1109/TSP.2026.3659742.png)
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Abstract
En 中文
Multiobject tracking provides situational awareness that enables new applications for modern convenience, applied ocean sciences, public safety, and homeland security. In many multiobject tracking applications, including radar and sonar tracking, after coherent prefiltering of the received signal, measurement data is typically structured in cells, where each cell represents, e.g., a different range and bearing value. While conventional detect-then-track (DTT) multiobject tracking approaches convert the cell-structured data within a detection phase into so-called point measurements to reduce the amount of data, track-before-detect (TBD) methods process the cell-structured data directly, avoiding a potential information loss. However, many TBD tracking methods are computationally intensive or achieve a reduced tracking accuracy when objects interact, i.e., when they come into close proximity. In contrast, our resulting TBD tracking method achieves good tracking accuracy even for interacting objects by introducing the new concept of probabilistic object-to-cell contributions. More specifically, our approach uses a probabilistic association of objects to data cells and a new object contribution model to further link cell contributions to objects occupying the same data cell. To keep computational complexity and filter runtimes low, we use an efficient Poisson/multi-Bernoull filter approach in combination with belief propagation for fast probabilistic data assignment. We demonstrate numerically that our method achieves significantly improved tracking performance compared to state-of-the-art TBD tracking approaches, where performance differences are particularly pronounced when multiple objects interact.
Keywords:
Multiobject tracking
multitarget tracking
track-before-detect
random finite sets
Journal
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5.8
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