A Track-Before-Detect Trajectory Multi-Bernoulli Filter for Generalised Superpositional Measurements
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2026
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| author | Lynch, Sion García-Fernández, Ángel F. Devlin, Lee |
| author_facet | Lynch, Sion García-Fernández, Ángel F. Devlin, Lee |
| contents | This paper proposes the Trajectory-Information Exchange Multi-Bernoulli (T-IEMB) filter to estimate sets of alive and all trajectories in track-before-detect applications with generalised superpositional measurements. This measurement model has superpositional hidden variables which are mapped to the conditional mean and covariance of the measurement, enabling it to describe a broad range of measurement models. This paper also presents a Gaussian implementation of the T-IEMB filter, which performs the update by approximating the conditional moments of the measurement model, and admits a computationally light filtering solution. Simulation results for a non-Gaussian radar-based tracking scenario demonstrate the performance of two Gaussian T-IEMB implementations, which provide improved tracking performance compared to a state-of-the-art particle filter based solution for track-before-detect, at a reduced computational cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02365 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Track-Before-Detect Trajectory Multi-Bernoulli Filter for Generalised Superpositional Measurements Lynch, Sion García-Fernández, Ángel F. Devlin, Lee Signal Processing This paper proposes the Trajectory-Information Exchange Multi-Bernoulli (T-IEMB) filter to estimate sets of alive and all trajectories in track-before-detect applications with generalised superpositional measurements. This measurement model has superpositional hidden variables which are mapped to the conditional mean and covariance of the measurement, enabling it to describe a broad range of measurement models. This paper also presents a Gaussian implementation of the T-IEMB filter, which performs the update by approximating the conditional moments of the measurement model, and admits a computationally light filtering solution. Simulation results for a non-Gaussian radar-based tracking scenario demonstrate the performance of two Gaussian T-IEMB implementations, which provide improved tracking performance compared to a state-of-the-art particle filter based solution for track-before-detect, at a reduced computational cost. |
| title | A Track-Before-Detect Trajectory Multi-Bernoulli Filter for Generalised Superpositional Measurements |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2602.02365 |