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

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. Yu Ge, Fan Jiang, Lennart Svensson and Henk Wymeersch are with the Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden. Emails: {yuge,fan.jiang,lennart.svensson,henkw}@chalmers.se. Ossi Kaltiokallio, Jukka Talvitie and Mikko Valkama are with the Unit of Electrical Engineering, Tampere University, Tampere, Finland. Emails: {ossi.kaltiokallio,jukka.talvitie,mikko.valkama}@tuni.fi Hyowon Kim and Sunwoo Kim are with the Department of Electronic Engineering, Hanyang University, Seoul, South Korea. Email: {khw870511,remero}@hanyang.ac.kr. This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation, and the Vinnova 5GPOS project under grant 2019-03085, by the Swedish Research Council under grant 2018-03705. ar X iv :2 10 9. 03 56 1v 1 [ ee ss .S P] 8 S ep 2 02 1