| 1. |
- Bergström, Andreas, 1978-, et al.
(författare)
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TOA Estimation Improvements in Multipath Environments by Measurement Error Models
- 2017
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Ingår i: Proceedings of the 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538635315 - 9781538635308 ; , s. 1-8
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Konferensbidrag (refereegranskat)abstract
- Many positioning systems rely on accuratetime of arrival measurements. In this paper, we addressnot only the accuracy but also the relevance of Time ofArrival (TOA) measurement error modeling. We discusshow better knowledge of these errors can improve relativedistance estimation, and compare the impact of differentlydetailed measurement error information. These models arecompared in simulations based on models derived froman Ultra Wideband (UWB) measurement campaign. Theconclusion is that significant improvements can be madewithout providing detailed received signal information butwith a generic and relevant measurement error model.
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| 2. |
- Kasebzadeh, Parinaz, 1985-
(författare)
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Parameter Estimation for Mobile Positioning Applications
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The availability and reliability of mobile positioning algorithms depend on both the quality of measurements and the environmental characteristics. The positioning systems based on global navigation satellite systems (GNSS), for example, have typically a few meters accuracy but are unavailable in signal denied conditions and unreliable in multipath environments. Other radio network based positioning algorithms have the same drawbacks. This thesis considers a couple of cases where these drawbacks can be mitigated by model-based sensor fusion techniques.The received signal strength (RSS) is commonly used in cellular radio networks for positioning due to its high availability, but its reliability depends heavily on the environment. We have studied how the directional dependence in the antenna gain in the base stations can be compensated for. We propose a semiempirical model for RSS measurements, composed of an empirical log-distance model of the RSS decay rate, and a deterministic antenna gain model that accounts for non-uniform base station antenna radiation. Evaluations and comparisons presented in this study demonstrate an improvement in estimation performance of the joint model compared to the propagation model alone.Inertial navigation systems (INS ) rely on integrating inertial sensor measurements. INS as a standalone system is known to have a cubic drift in the position error, and it needs supporting sensor information, for instance, position fixes from GNSS whenever available. For pedestrians, special tricks such as parametric gait models and step detections can be used to limit the drift. In general, the more accurate gait parameters, the better position estimation accuracy. An improved pedestrian dead reckoning (PDR) algorithm is developed that learns gait parameters in time intervals when direct position measurements (such as GNSS positions) are available. We present a multi-rate filtering solution that leads to improved estimates of both gait parameters and position. To further extend the algorithm to more realistic scenarios, a joint classifier of the user’s motion and the device’s carrying mode is developed. Classification of motion mode (walking, running, standing still) and device mode (hand-held, in pocket, in backpack) provides information that can assist in the gait learning process and hence improve the position estimation. The algorithms are applied to collected data and promising results are reported. Furthermore, one of the most extensive datasets for personal navigation systems using both rigid body motion trackers and smartphones is presented, and this dataset has also been made publicly available.
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| 3. |
- Radnosrati, Kamiar, 1987-, et al.
(författare)
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Performance of OTDOA Positioning in Narrowband IoT Systems
- 2017
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Ingår i: 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). - : IEEE. - 9781538635315 - 9781538635292
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Konferensbidrag (refereegranskat)abstract
- Narrowband Internet of Things (NB-IoT) is an emerging cellular technology designed to target low-cost devices, high coverage, long device battery life (more than ten years), and massive capacity. We investigate opportunities for device tracking in NB-IoT systems using Observed Time Difference of Arrival (OTDOA) measurements. Reference Signal Time Difference (RSTD) reports are simulated to be sent to the mobile location center periodically or on an ondemand basis. We investigate the possibility of optimizing the number of reports per minute budget on horizontal positioning accuracy using an on-demand reporting method based on the Signal to Noise Ratio (SNR) of the measured cells received by the User Equipment (UE). Wireless channels are modeled considering multipath fading propagation conditions. Extended Pedestrian A (EPA) and Extended Typical Urban (ETU) delay profiles corresponding to low and high delay spread environments, respectively, are simulated for this purpose. To increase the robustness of the filtering method, measurement noise outliers are detected using confidence bounds estimated from filter innovations.
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| 4. |
- Gustafsson, Fredrik, et al.
(författare)
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Measurements used in Wireless Sensor Networks Localization
- 2009. - 1
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Ingår i: Localization Algorithms and Strategies for Wireless Sensor Networks. - : Information Science Reference. - 9781605663968 - 9781605663975 ; , s. 33-53
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Bokkapitel (refereegranskat)abstract
- Wireless sensor networks (WSN) localization relies on measurements. Availability of, and the information content in, these measurements depend on the network architecture, connectivity, node time synchronization and the signaling bandwidth between the sensor nodes. This chapter addresses wireless sensor networks measurements in a general framework based on a set of nodes, where each node either emits or receives signals. The emitted signal can for example be a radio, acoustic, seismic, infrared or sonic wave that is propagated in a certain media to the receiver. This general observation model does not make any difference between localization of sensor network nodes or unknown objects, or whether the nodes or objects are stationary or mobile. The information available for localization in wireless cellular networks (WCN) is in literature classified as direction of arrival (DOA), time of arrival (TOA), time difference of arrival (TDOA) and received signal strength (RSS). This chapter generalizes these concepts to the more general wireless sensor networks.
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| 5. |
- Kasebzadeh, Parinaz, 1985-, et al.
(författare)
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Improved Pedestrian Dead Reckoning Positioning With Gait Parameter Learning
- 2016
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Ingår i: Proceedings of the 19th International Conference on Information Fusion. - : IEEE conference proceedings. - 9780996452748 ; , s. 379-385
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Konferensbidrag (refereegranskat)abstract
- We consider personal navigation systems in devices equipped with inertial sensors and GPS, where we propose an improved Pedestrian Dead Reckoning (PDR) algorithm that learns gait parameters in time intervals when position estimates are available, for instance from GPS or an indoor positioning system (IPS). A novel filtering approach is proposed that is able to learn internal gait parameters in the PDR algorithm, such as the step length and the step detection threshold. Our approach is based on a multi-rate Kalman filter bank that estimates the gait parameters when position measurements are available, which improves PDR in time intervals when the position is not available, for instance when passing from outdoor to indoor environments where IPS is not available. The effectiveness of the new approach is illustrated on several real world experiments.
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| 6. |
- Zhao, Yuxin, 1986-, et al.
(författare)
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Cramér–Rao Bounds for Filtering Based on Gaussian Process State-Space Models
- 2019
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Ingår i: IEEE Transactions on Signal Processing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-587X .- 1941-0476. ; 67:23, s. 5936-5951
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Tidskriftsartikel (refereegranskat)abstract
- Posterior Cramér-Rao bounds (CRBs) are derived for the estimation performance of three Gaussian process-based state-space models. The parametric CRB is derived for the case with a parametric state transition and a Gaussian process-based measurement model. We illustrate the theory with a target tracking example and derive both parametric and posterior filtering CRBs for this specific application. Finally, the theory is illustrated with a positioning problem, with experimental data from an office environment where the obtained estimation performance is compared to the derived CRBs.
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| 7. |
- Zhao, Yuxin
(författare)
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Position Estimation in Uncertain Radio Environments and Trajectory Learning
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- To infer the hidden states from the noisy observations and make predictions based on a set of input states and output observations are two challenging problems in many research areas. Examples of applications many include position estimation from various measurable radio signals in indoor environments, self-navigation for autonomous cars, modeling and predicting of the traffic flows, and flow pattern analysis for crowds of people. In this thesis, we mainly use the Bayesian inference framework for position estimation in an indoor environment, where the radio propagation is uncertain. In Bayesian inference framework, it is usually hard to get analytical solutions. In such cases, we resort to Monte Carlo methods to solve the problem numerically. In addition, we apply Bayesian nonparametric modeling for trajectory learning in sport analytics.The main contribution of this thesis is to propose sequential Monte Carlo methods, namely particle filtering and smoothing, for a novel indoor positioning framework based on proximity reports. The experiment results have been further compared with theoretical bounds derived for this proximity based positioning system. To improve the performance, Bayesian non-parametric modeling, namely Gaussian process, has been applied to better indicate the radio propagation conditions. Then, the position estimates obtained sequentially using filtering and smoothing are further compared with a static solution, which is known as fingerprinting.Moreover, we propose a trajectory learning framework for flow estimation in sport analytics based on Gaussian processes. To mitigate the computation deficiency of Gaussian process, a grid-based on-line algorithm has been adopted for real-time applications. The resulting trajectory modeling for individual athlete can be used for many purposes, such as performance prediction and analysis, health condition monitoring, etc. Furthermore, we aim at modeling the flow of groups of athletes, which could be potentially used for flow pattern recognition, strategy planning, etc.
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| 8. |
- Amirijoo, Mehdi, et al.
(författare)
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On Self-Optimization of the Random Access Procedure in 3G Long Term Evolution
- 2009
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Ingår i: Proceedings of the 11th IFIP/IEEE International Symposium on Integrated Network Management. - : IEEE. - 9781424439232 - 9781424439249
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Konferensbidrag (refereegranskat)abstract
- Operationally efficient radio networks typically feature a high degree of self-organization. This means less planning efforts and manual intervention, and a potential for better radio resource utilization when network elements adapts its operation to the observed local conditions. The focus in this paper is selfoptimization of the random access channel (RACH) in the 3G Long Term Evolution (LTE). A comprehensive tutorial about the RACH procedure is provided to span the complexity of the selfoptimization. Moreover, the paper addresses RACH key performance metrics and appropriate modeling of the various steps and components of the procedure. Finally, some coupling between parameters and key performance metrics as well as selfoptimization examples are presented together with a feasibility discussion. The main ambition with this workshop paper is to present and define a relevant set of self-optimization problems, rather than to provide a complete solution.
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| 9. |
- Amirijoo, Mehdi, et al.
(författare)
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Towards Random Access Channel Self-Tuning in LTE
- 2009
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Ingår i: Proceedings of the 69th IEEE Vehicular Technology Conference. - 9781424425174 ; , s. 1-5
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Konferensbidrag (refereegranskat)abstract
- Future radio access networks are expected to show a high degree of self-organization. This paper addresses self-tuning of the random access channel (RACH) in the 3G Long Term Evolution (LTE). The feasibility of self-tuning is investigated by means of simulation, where the coupling between several parameters and the performance of RACH is provided. The conclusion of the simulations is that RACH self-tuning is indeed possible given that UE assisted measurements are available for the self-tuning mechanism.
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| 10. |
- Axehill, Daniel, et al.
(författare)
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A Low-Complexity High-Performance Preprocessing Algorithm for Multiuser Detection using Gold Sequences
- 2008
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Ingår i: IEEE Transactions on Signal Processing. - Linköping : IEEE Signal Processing Society. - 1053-587X .- 1941-0476. ; 56:9, s. 4377-4385
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Tidskriftsartikel (refereegranskat)abstract
- The optimum multiuser detection problem can be formulated as a maximum likelihood problem, which yields a binary quadratic programming problem to be solved. Generally this problem is NP-hard and is therefore hard to solve in real time. In this paper, a preprocessing algorithm is presented which makes it possible to detect some or all users optimally for a low computational cost if signature sequences with low cross correlation, e.g., Gold sequences, are used. The algorithm can be interpreted as, e.g., an adaptive tradeoff between parallel interference cancellation and successive interference cancellation. Simulations show that the preprocessing algorithm is able to optimally compute more than 94,% of the bits in the problem when the users are time-synchronous, even though the system is heavily loaded and affected by noise. Any remaining bits, not computed by the preprocessing algorithm, can either be computed by a suboptimal detector or an optimal detector. Simulations of the time-synchronous case show that if a suboptimal detector is chosen, the bit error rate (BER) rate is significantly reduced compared with using the suboptimal detector alone.
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