A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM

Yu Ge, Ossi Kaltiokallio, Hyowon Kim, Fan Jiang, Jukka Talvitie, Mikko Valkama, Lennart Svensson, Sunwoo Kim, Henk Wymeersch

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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.

Original languageEnglish
Pages (from-to)2179-2192
Number of pages14
JournalIEEE Journal on Selected Areas in Communications
Issue number7
Publication statusPublished - Jul 2022
Publication typeA1 Journal article-refereed


  • Bistatic sensing
  • Complexity theory
  • Computational modeling
  • extended Kalman filter
  • Filtering algorithms
  • Kalman filters
  • mmWave sensing
  • Poisson multi-Bernoulli mixture filter
  • Receivers
  • Sensors
  • simultaneous localization and mapping
  • Simultaneous localization and mapping

Publication forum classification

  • Publication forum level 3

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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