Bayesian Joint Localization and Tracking Algorithm Using Multiple-Input Multiple-Output Radar

Anders Malthe Westerkam*, Carles Navarro Manchon, Preben E. Mogensen, Troels Pedersen

*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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 languageEnglish
Title of host publication2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023
Number of pages5
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Publication date31 Jan 2024
Pages316-320
ISBN (Electronic)9798350344523
DOIs
Publication statusPublished - 31 Jan 2024

Keywords

  • Active Sensing
  • Bayesian Learning
  • MIMO-radar
  • localization and Tracking

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