Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates
This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scat...
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| Format: | Article |
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MDPI AG
2019-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/21/4802 |
| _version_ | 1798042224400793600 |
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| author | Martin Schmidhammer Christian Gentner Benjamin Siebler Stephan Sand |
| author_facet | Martin Schmidhammer Christian Gentner Benjamin Siebler Stephan Sand |
| author_sort | Martin Schmidhammer |
| collection | DOAJ |
| description | This paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scatterers is formulated as a nonlinear optimization problem. An iterative nonlinear least squares algorithm following Levenberg and Marquardt is used for solving the optimization problem initially, and an extended Kalman filter is used for estimating the scatterer location recursively over time. The corresponding performance bounds are derived for both the snapshot based position estimation and the nonlinear sequential Bayesian estimation with the classic and the posterior Cramér−Rao lower bound. Thereby, a comparison of simulation results to the posterior Cramér−Rao lower bound confirms the applicability of the extended Kalman filter. The proposed approach is applied to estimate the position of a walking pedestrian sequentially based on wideband measurement data in an outdoor scenario. The evaluation shows that the pedestrian can be localized throughout the scenario with an accuracy of <inline-formula> <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>8</mn> </mrow> </semantics> </math> </inline-formula> m at 90% confidence. |
| first_indexed | 2024-04-11T22:32:31Z |
| format | Article |
| id | doaj.art-ef92654a62364adc8e4e0f98a2667b36 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:32:31Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ef92654a62364adc8e4e0f98a2667b362022-12-22T03:59:20ZengMDPI AGSensors1424-82202019-11-011921480210.3390/s19214802s19214802Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay EstimatesMartin Schmidhammer0Christian Gentner1Benjamin Siebler2Stephan Sand3German Aerospace Center (DLR), Institute of Communications and Navigation, 82234 Wessling, GermanyGerman Aerospace Center (DLR), Institute of Communications and Navigation, 82234 Wessling, GermanyGerman Aerospace Center (DLR), Institute of Communications and Navigation, 82234 Wessling, GermanyGerman Aerospace Center (DLR), Institute of Communications and Navigation, 82234 Wessling, GermanyThis paper describes an approach to detect, localize, and track moving, non-cooperative objects by exploiting multipath propagation. In a network of spatially distributed transmitting and receiving nodes, moving objects appear as discrete mobile scatterers. Therefore, the localization of mobile scatterers is formulated as a nonlinear optimization problem. An iterative nonlinear least squares algorithm following Levenberg and Marquardt is used for solving the optimization problem initially, and an extended Kalman filter is used for estimating the scatterer location recursively over time. The corresponding performance bounds are derived for both the snapshot based position estimation and the nonlinear sequential Bayesian estimation with the classic and the posterior Cramér−Rao lower bound. Thereby, a comparison of simulation results to the posterior Cramér−Rao lower bound confirms the applicability of the extended Kalman filter. The proposed approach is applied to estimate the position of a walking pedestrian sequentially based on wideband measurement data in an outdoor scenario. The evaluation shows that the pedestrian can be localized throughout the scenario with an accuracy of <inline-formula> <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>.</mo> <mn>8</mn> </mrow> </semantics> </math> </inline-formula> m at 90% confidence.https://www.mdpi.com/1424-8220/19/21/4802mulitlaterationlocalizationnonlinear least-squareslevenberg–marquardttrackingextended kalman filterbayesian performance boundsposterior cramér–rao lower bound |
| spellingShingle | Martin Schmidhammer Christian Gentner Benjamin Siebler Stephan Sand Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates Sensors mulitlateration localization nonlinear least-squares levenberg–marquardt tracking extended kalman filter bayesian performance bounds posterior cramér–rao lower bound |
| title | Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates |
| title_full | Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates |
| title_fullStr | Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates |
| title_full_unstemmed | Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates |
| title_short | Localization and Tracking of Discrete Mobile Scatterers in Vehicular Environments Using Delay Estimates |
| title_sort | localization and tracking of discrete mobile scatterers in vehicular environments using delay estimates |
| topic | mulitlateration localization nonlinear least-squares levenberg–marquardt tracking extended kalman filter bayesian performance bounds posterior cramér–rao lower bound |
| url | https://www.mdpi.com/1424-8220/19/21/4802 |
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