| 201. |
- Li, Xuhong, et al.
(författare)
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A Belief Propagation Algorithm for Multipath-based SLAM with Multiple Map Features: A mmWave MIMO Application
- 2024
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, we present a multipath-based simultaneous localization and mapping (SLAM) algorithm that continuously adapts mulitiple map feature (MF) models describing specularly reflected multipath components (MPCs) from flat surfaces and point-scattered MPCs, respectively. We develop a Bayesian model for sequential detection and estimation of interacting MF model parameters, MF states and mobile agent's state including position and orientation. The Bayesian model is represented by a factor graph enabling the use of belief propagation (BP) for efficient computation of the marginal posterior distributions. The algorithm also exploits amplitude information enabling reliable detection of weak MFs associated with MPCs of very low signal-to-noise ratios (SNRs). The performance of the proposed algorithm is evaluated using real millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) measurements with single base station setup. Results demonstrate the excellent localization and mapping performance of the proposed algorithm in challenging dynamic outdoor scenarios.
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| 202. |
- Li, Xuhong, et al.
(författare)
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Detection and Tracking of Multipath Channel Parameters Using Belief Propagation
- 2021
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Ingår i: IEEE Asilomar Conference on Signals, Systems, and Computers. - 9781665447072 - 9780738131269
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Konferensbidrag (refereegranskat)abstract
- We present a belief propagation (BP) algorithm with probabilistic data association (DA) for detection and tracking of specular multipath components (MPCs). In real dynamic measurement scenarios, the number of MPCs reflected from visible geometric features, the MPC dispersion parameters, and the number of false alarm contributions are unknown and time-varying. We develop a Bayesian model for specular MPC detection and joint estimation problem, and represent it by a factor graph which enables the use of BP for efficient computation of the marginal posterior distributions. A parametric channel estimator is exploited to estimate at each time step a set of MPC parameters, which are further used as noisy measurements by the BP-based algorithm. The algorithm performs probabilistic DA, and joint estimation of the time-varying MPC parameters and mean false alarm rate. Preliminary results using synthetic channel measurements demonstrate the excellent performance of the proposed algorithm in a realistic and very challenging scenario. Furthermore, it is demonstrated that the algorithm is able to cope with a high number of false alarms originating from the prior estimation stage.
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| 203. |
- Li, Xuhong, et al.
(författare)
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Massive MIMO-based Localization and Mapping Exploiting Phase Information of Multipath Components
- 2019
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Ingår i: IEEE Transactions on Wireless Communications. - 1536-1276. ; 18:9, s. 4254-4267
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present a robust multipath-based localization and mapping framework that exploits the phases of specular multipath components (MPCs) using a massive multiple-input multipleoutput (MIMO) array at the base station. Utilizing the phase information related to the propagation distances of the MPCs enables the possibility of localization with extraordinary accuracy even with limited bandwidth. The specular MPC parameters along with the parameters of the noise and the dense multipath component (DMC) are tracked using an extended Kalman filter (EKF), which enables to preserve the distance-related phase changes of the MPC complex amplitudes. The DMC comprises all non-resolvable MPCs, which occur due to finite measurement aperture. The estimation of the DMC parameters enhances the estimation quality of the specular MPCs and therefore also the quality of localization and mapping. The estimated MPC propagation distances are subsequently used as input to a distance-based localization and mapping algorithm. This algorithm does not need prior knowledge about the surrounding environment and base station position. The performance is demonstrated with real radio-channel measurements using an antenna array with 128 ports at the base station side and a standard cellular signal bandwidth of 40 MHz. The results show that high accuracy localization is possible even with such a low bandwidth.
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| 204. |
- Li, Xuhong, et al.
(författare)
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Robust Phase-Based Positioning Using Massive MIMO with Limited Bandwidth
- 2018
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Ingår i: 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2017.. - 9781538635292 - 9781538635322
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Konferensbidrag (refereegranskat)abstract
- This paper presents a robust phase-based positioningframework using a massive multiple-input multiple-output(MIMO) system. The phase-based distance estimates of MPCstogether with other parameters are tracked with an ExtendedKalman Filter (EKF), the state dimension of which varies withthe birth-death processes of paths. The iterative maximumlikelihoodestimation algorithm (RIMAX) and the modeling ofdense multipath component (DMC) in the framework furtherenhance the quality of parameter tracking by providing anaccurate initial state and the underlying noise covariance.The tracked MPCs are fed into a time-of-arrival (TOA) selfcalibrationpositioning algorithm for simultaneous trajectoryand environment estimation. Throughout the positioning process,no prior knowledge of the surrounding environment andbase station position is needed. The performance is evaluatedwith the measurement of a 2D complex movement, which wasperformed in a sports hall with an antenna array with 128 portsas base station using a standard cellular bandwidth of 40 MHz.The positioning result shows that the mean deviation of theestimated user equipment trajectory from the ground truth is13 cm. In summary, the proposed framework is a promisinghigh-resolution radio-based positioning solution for current andnext generation cellular systems.
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| 205. |
- Li, Xuhong, et al.
(författare)
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RSS-Based Localization of Low-Power IoT Devices Exploiting AoA and Range Information
- 2021
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Ingår i: 54th Asilomar Conference on Signals, Systems, and Computers. - 9780738131269 - 9781665447072
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Konferensbidrag (refereegranskat)abstract
- We present a localization algorithm for low-power long-range Internet-of-things (IoT) networks, which exploits angle of arrival (AoA) and range information from non-coherent received signal strength (RSS) measurements. In this work, each anchor node is equipped with array antennas of known geometry and radiation patterns. The position of the target node and the path-loss exponent to each anchor are unknown and possibly time-varying. The joint estimation problem is formulated with a Bayesian model, where the likelihood functions are derived from the classical path-loss model and an RSS difference model. A message passing method is then exploited for efficient computation of the marginal posterior distribution of each unknown variable. The proposed algorithm is validated using real outdoor measurements from a low-power wide area network based IoT system in a challenging scenario. Results show that the proposed algorithm can adapt to dynamic propagation conditions, and improve the localization accuracy compared to a method that exploits only single geometric feature. Furthermore, the algorithm scales well in different antenna array configurations, and is compatible with various existing IoT standards.
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| 206. |
- Li, Xuhong, et al.
(författare)
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Sequential Detection and Estimation of Multipath Channel Parameters Using Belief Propagation
- 2022
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Ingår i: IEEE Transactions on Wireless Communications. - 1536-1276. ; 21:10, s. 8385-8402
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a belief propagation (BP)-based algorithm for sequential detection and estimation of multipath component (MPC) parameters based on radio signals. Under dynamic channel conditions with moving transmitter/receiver, the number of MPCs, the MPC dispersion parameters, and the number of false alarm contributions are unknown and time-varying. We develop a Bayesian model for sequential detection and estimation of MPC dispersion parameters, and represent it by a factor graph enabling the use of BP for efficient computation of the marginal posterior distributions. At each time step, a snapshot-based parametric channel estimator provides parameter estimates of a set of MPCs which are used as noisy measurements by the proposed BP-based algorithm. It performs joint probabilistic data association, and estimation of the time-varying MPC parameters and the mean number of false alarm measurements, by means of the sum-product algorithm rules. The algorithm also exploits amplitude information enabling the reliable detection of “weak” MPCs with very low component signal-to-noise ratios (SNRs). The performance of the proposed algorithm compares well to state-of-the-art algorithms for high SNR MPCs, but it significantly outperforms them for medium or low SNR MPCs. Results using real radio measurements demonstrate the excellent performance of the proposed algorithm in realistic and challenging scenarios.
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| 207. |
- Li, Xuhong, et al.
(författare)
-
Sequential Detection and Estimation of MultipathChannel Parameters Using Belief Propagation
- 2022
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This paper proposes a BP-based algorithm for sequential detection and estimation of MPC parameters based on radio signals. Under dynamic channel conditions with moving transmitter and/or receiver, the number of MPCs reflected from visible geometric features, the MPC dispersion parameters (delay, angle, Doppler frequency, etc), and the number of false alarm contributions are unknown and time-varying. We develop a Bayesian model for sequential detection and estimation of MPC dispersion parameters, and represent it by a factor graph enabling the use of BP for efficient computation of the marginal posterior distributions. At each time instance, a snapshot-based channel estimator provides parameter estimates of a set of MPCs which are used as noisy measurements by the proposed BP-based algorithm. It performs joint probabilistic data association, estimation of the time-varying MPC parameters, and the mean number of false alarm measurements by means of the sum-product algorithm rules. The results using synthetic measurements show that the proposed algorithm is able to cope with a high number of false alarm measurements originating from the snapshot-based channel estimator and to sequentially detect and estimate MPC parameters with very low SNR. The performance of the proposed algorithm compares well to existing algorithms for high SNR MPCs, but significantly it outperforms them for medium or low SNR MPCs. In particular, we show that our algorithm outperforms the KEST algorithm, a state-of-the-art sequential channel parameters estimation method. Furthermore, results with real radio measurements demonstrate the excellent performance of the algorithm in realistic and challenging scenarios.
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| 208. |
- Li, Xuhong, et al.
(författare)
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Target Tracking using Signal Strength Differences for Long-Range IoT Networks
- 2020
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Ingår i: 8th IEEE ICC Workshop on Advances in Network Localization and Navigation (ANLN), 2020. - 9781728174402 - 9781728174419
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Konferensbidrag (refereegranskat)abstract
- Radio based positioning or tracking solutions typically require wideband signals or phase coherent antennas. In this paper, we present a target tracking method based on received non-coherent signal strength differences (RSSDs) between antennas for outdoor Internet-of-things (IoT) scenarios. We introduce an RSSD model based on classical path-loss models. With known antenna patterns and antenna array geometries, the RSSD model enables direct mapping between RSSD and angle of arrival, without involving parameters like transmit power, path-loss coefficient, etc. The RSSD model is then exploited in a recursive Bayesian filtering method for target tracking where a particle filter-based implementation is used. The performance is evaluated using outdoor measurements in a low-power wide area network (LoRaWAN) based IoT system. Besides, we also investigate the potential of the RSSD model for AoA estimation. The experimental results show the capability of the proposed framework for real-time target/AoA tracking; reasonable accuracy is achieved even when using non-averaged RSS measurements and under non line-of-sight (NLoS) conditions. Furthermore, the non-coherent approach has low computational complexity, scales well, and is flexible to allow for different antenna array configurations.
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| 209. |
- Liu, Lingfeng, et al.
(författare)
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The COST 2100 MIMO Channel Model
- 2012
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Ingår i: IEEE Wireless Communications. - 1536-1284. ; 19:6, s. 92-99
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Tidskriftsartikel (refereegranskat)abstract
- The COST 2100 channel model is a geometry-based stochastic channel model (GSCM) that can reproduce the stochastic properties of multi-link Multiple-Input Mulitple-Output (MIMO) channels over time, frequency and space. By contrast to other popular GSCMs, the COST 2100 approach is generic and flexible, making it suitable to model multi-user or distributed MIMO scenarios. In this paper a concise overview of the COST 2100 channel model is presented. Main concepts are described, together with useful implementation guidelines. Recent developments, including dense multipath components, polarization and multi-link aspects are also discussed.
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| 210. |
- Luan, Fengyu, et al.
(författare)
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Geometrical Cluster-Based Scatterer Detection Method with the Movement of Mobile Terminal
- 2015
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Ingår i: IEEE 81st Vehicular Technology Conference (VTC Spring), 2015.
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Konferensbidrag (refereegranskat)abstract
- When a mobile station moves along a trajectory, it will see different parts of the same scatterers or scatterer groups during its movement. In this paper, we present a new method to identify such physical clusters of scatterers, and the corresponding groups of multipath components (MPCs) interacting with those clusters, from measurements of channel impulse responses. The method is based on identifying MPCs that have similar long-term properties - in the sense that they ``effectively interact with'' the same physical clusters, The method consists of four steps: (1) estimate the delays, the DOAs and the amplitudes of the MPCs in each time snapshot; (2) track the MPCs in the time-delay domain; (3) localize all the scattering points in a 2-D Cartesian coordinate system and cluster them on a map with a traditional Kmeans clustering algorithm; (4) merge the scattering points of different MPCs into physical clusters. The method is evaluated using both synthetic data and real measurement data from a suburban area. In the latter case, the locations and the sizes of the interacting objects identified from the measurements show excellent agreement with the location of physical objects in the environment such as pillars and buildings.
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