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

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

doi:10.1109/CAMSAP58249.2023.10403518

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

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

^*Kontaktforfatter

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

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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.

Originalsprog	Engelsk
Titel	2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023
Antal sider	5
Forlag	IEEE (Institute of Electrical and Electronics Engineers)
Publikationsdato	31 jan. 2024
Sider	316-320
ISBN (Trykt)	979-8-3503-4453-0
ISBN (Elektronisk)	979-8-3503-4452-3
DOI	https://doi.org/10.1109/CAMSAP58249.2023.10403518
Status	Udgivet - 31 jan. 2024
Begivenhed	9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) - Los Suenos, Costa Rica Varighed: 10 dec. 2023 → 13 dec. 2023 https://camsap23.ig.umons.ac.be/

Workshop

Workshop	9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Land/Område	Costa Rica
By	Los Suenos
Periode	10/12/2023 → 13/12/2023
Internetadresse	https://camsap23.ig.umons.ac.be/

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10.1109/CAMSAP58249.2023.10403518

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Citationsformater

Westerkam, A. M., Manchon, C. N., E. Mogensen, P., & Pedersen, T. (2024). Bayesian Joint Localization and Tracking Algorithm Using Multiple-Input Multiple-Output Radar. I 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023 (s. 316-320). IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/CAMSAP58249.2023.10403518

@inproceedings{2f44b630f4564b9b9f3973e14a78f81f,

title = "Bayesian Joint Localization and Tracking Algorithm Using Multiple-Input Multiple-Output Radar",

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{\textquoteright}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.",

keywords = "Active Sensing, Bayesian Learning, MIMO-radar, localization and Tracking",

author = "Westerkam, {Anders Malthe} and Manchon, {Carles Navarro} and {E. Mogensen}, Preben and Troels Pedersen",

year = "2024",

month = jan,

day = "31",

doi = "10.1109/CAMSAP58249.2023.10403518",

language = "English",

isbn = "979-8-3503-4453-0",

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booktitle = "2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023",

publisher = "IEEE (Institute of Electrical and Electronics Engineers)",

address = "United States",

note = "9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), CAMSAP ; Conference date: 10-12-2023 Through 13-12-2023",

url = "https://camsap23.ig.umons.ac.be/",

}

Westerkam, AM, Manchon, CN, E. Mogensen, P & Pedersen, T 2024, Bayesian Joint Localization and Tracking Algorithm Using Multiple-Input Multiple-Output Radar. i 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023. IEEE (Institute of Electrical and Electronics Engineers), s. 316-320, 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Los Suenos, Costa Rica, 10/12/2023. https://doi.org/10.1109/CAMSAP58249.2023.10403518

Bayesian Joint Localization and Tracking Algorithm Using Multiple-Input Multiple-Output Radar. / Westerkam, Anders Malthe; Manchon, Carles Navarro; E. Mogensen, Preben et al.
2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023. IEEE (Institute of Electrical and Electronics Engineers), 2024. s. 316-320.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Bayesian Joint Localization and Tracking Algorithm Using Multiple-Input Multiple-Output Radar

AU - Westerkam, Anders Malthe

AU - Manchon, Carles Navarro

AU - E. Mogensen, Preben

AU - Pedersen, Troels

PY - 2024/1/31

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N2 - 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.

AB - 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.

KW - Active Sensing

KW - Bayesian Learning

KW - MIMO-radar

KW - localization and Tracking

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U2 - 10.1109/CAMSAP58249.2023.10403518

DO - 10.1109/CAMSAP58249.2023.10403518

M3 - Article in proceeding

SN - 979-8-3503-4453-0

SP - 316

EP - 320

BT - 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023

PB - IEEE (Institute of Electrical and Electronics Engineers)

T2 - 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

Y2 - 10 December 2023 through 13 December 2023

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Westerkam AM, Manchon CN, E. Mogensen P , Pedersen T. Bayesian Joint Localization and Tracking Algorithm Using Multiple-Input Multiple-Output Radar. I 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2023. IEEE (Institute of Electrical and Electronics Engineers). 2024. s. 316-320 doi: 10.1109/CAMSAP58249.2023.10403518

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

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