Abstract
We present a novel joint localization and tracking algorithm for multiple-input multiple-output active radars. The proposed algorithm, which we dub Bayesian localization and tracking (BLaT), relies on approximate Bayesian inference using the mean field approach and processes all available received data to jointly estimate the target’s track and location. This approach makes it possible to take advantage of the inherent synergy between the tracking and localization tasks. BLaT is shown by simulation to outperform a classical sequential processing baseline in terms of its ability to track targets in low signal-to-noise ratio conditions as well as superior tracking of manoeuvring targets.
Original language | English |
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Title of host publication | 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023 |
Number of pages | 5 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Publication date | 31 Jan 2024 |
Pages | 316-320 |
ISBN (Electronic) | 9798350344523 |
DOIs | |
Publication status | Published - 31 Jan 2024 |
Keywords
- Active Sensing
- Bayesian Learning
- MIMO-radar
- localization and Tracking