A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM
Artikel i vetenskaplig tidskrift, 2022

Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment and the propagation channel. To solve the SLAM problem, existing approaches rely on sigma-point or particle-based approximations, leading to high computational complexity, precluding real-time execution. We propose a novel low-complexity SLAM filter, based on the Poisson multi-Bernoulli mixture (PMBM) filter. It utilizes the extended Kalman (EK) first-order Taylor series based Gaussian approximation of the filtering distribution, and applies the track-oriented marginal multi-Bernoulli/Poisson (TOMB/P) algorithm to approximate the resulting PMBM as a Poisson multi-Bernoulli (PMB). The filter can account for different landmark types in radio SLAM and multiple data association hypotheses. Hence, it has an adjustable complexity/performance trade-off. Simulation results show that the developed SLAM filter can greatly reduce the computational cost, while it keeps the good performance of mapping and user state estimation.

Filtering algorithms


mmWave sensing

Complexity theory

Bistatic sensing

Simultaneous localization and mapping

Poisson multi-Bernoulli mixture filter

extended Kalman filter

simultaneous localization and mapping

Computational modeling

Kalman filters



Yu Ge

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Ossi Kaltiokallio

Tampereen Yliopisto

Hyowon Kim

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Fan Jiang

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Jukka Talvitie

Tampereen Yliopisto

M. Valkama

Tampereen Yliopisto

Lennart Svensson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

Sunwoo Kim

Hanyang University

Henk Wymeersch

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

IEEE Journal on Selected Areas in Communications

0733-8716 (ISSN) 15580008 (eISSN)

Vol. 40 7 2179-2192

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