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, le...
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| Veröffentlicht in: | IEEE journal on selected areas in communications 2022-07, Vol.40 (7), p.2179-2192 |
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| creator | Ge, Yu Kaltiokallio, Ossi Kim, Hyowon Jiang, Fan Talvitie, Jukka Valkama, Mikko Svensson, Lennart Kim, Sunwoo Wymeersch, Henk |
| description | 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. |
| doi_str_mv | 10.1109/JSAC.2022.3155504 |
| format | Article |
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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.</description><identifier>ISSN: 0733-8716</identifier><identifier>ISSN: 1558-0008</identifier><identifier>EISSN: 1558-0008</identifier><identifier>DOI: 10.1109/JSAC.2022.3155504</identifier><identifier>CODEN: ISACEM</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Bistatic sensing ; Complexity ; Complexity theory ; Computational efficiency ; Computational modeling ; Computing costs ; extended Kalman filter ; Filtering algorithms ; Kalman filters ; Millimeter waves ; mmWave sensing ; Poisson multi-Bernoulli mixture filter ; Propagation ; Receivers ; Sensors ; Simultaneous localization and mapping ; State estimation ; Taylor series</subject><ispartof>IEEE journal on selected areas in communications, 2022-07, Vol.40 (7), p.2179-2192</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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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.</description><subject>Algorithms</subject><subject>Bistatic sensing</subject><subject>Complexity</subject><subject>Complexity theory</subject><subject>Computational efficiency</subject><subject>Computational modeling</subject><subject>Computing costs</subject><subject>extended Kalman filter</subject><subject>Filtering algorithms</subject><subject>Kalman filters</subject><subject>Millimeter waves</subject><subject>mmWave sensing</subject><subject>Poisson multi-Bernoulli mixture filter</subject><subject>Propagation</subject><subject>Receivers</subject><subject>Sensors</subject><subject>Simultaneous localization and mapping</subject><subject>State estimation</subject><subject>Taylor series</subject><issn>0733-8716</issn><issn>1558-0008</issn><issn>1558-0008</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>D8T</sourceid><recordid>eNp1kU1vEzEQhi0EEqHwAxAXSxzRBn-sP_a4DWkpJCoifBxHjndMXO3Wwd606r9no1S9cZqR5plnpHkJecvZnHPWfPyyaRdzwYSYS66UYvUzMpsaWzHG7HMyY0bKyhquX5JXpdwwxuvaihm5aukiDfvD6MaYbl3fP9BlCNFHvB3p8mv1bX2-phexHzHTkDI9j-WIejoMv90d0u-ui4luVu36NXkRXF_wzWM9Iz8vlj8Wn6vV9eXVol1Vfro4Vtg1tXXMdsikb7g1MnRGKSm97DwGpRqNPphpisx655XZGm6l1cGZjjMhz8jm5C33uD9sYZ_j4PIDJBchY0GX_Q78zvUD5gIFgRnNtVQMtg4F1F5LaLxyoJq681wFqfV2sn74r_VT_NVCyn9gt4O64cpO9PsTvc_p7wHLCDfpkKf3FRDaGKuEEWqi-InyOZWSMTxZOYNjanBMDY6pwWNq0867005ExCe-MaIWQsp_EuSRHg</recordid><startdate>20220701</startdate><enddate>20220701</enddate><creator>Ge, Yu</creator><creator>Kaltiokallio, Ossi</creator><creator>Kim, Hyowon</creator><creator>Jiang, Fan</creator><creator>Talvitie, Jukka</creator><creator>Valkama, Mikko</creator><creator>Svensson, Lennart</creator><creator>Kim, Sunwoo</creator><creator>Wymeersch, Henk</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Algorithms Bistatic sensing Complexity Complexity theory Computational efficiency Computational modeling Computing costs extended Kalman filter Filtering algorithms Kalman filters Millimeter waves mmWave sensing Poisson multi-Bernoulli mixture filter Propagation Receivers Sensors Simultaneous localization and mapping State estimation Taylor series |
| title | A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM |
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