| 1. |
- Kasebzadeh, Parinaz, 1985-
(author)
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Parameter Estimation for Mobile Positioning Applications
- 2017
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Licentiate thesis (other academic/artistic)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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| 2. |
- Bergström, Andreas, 1978-
(author)
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Timing-Based Localization using Multipath Information
- 2020
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Licentiate thesis (other academic/artistic)abstract
- The measurements of radio signals are commonly used for localization purposes where the goal is to determine the spatial position of one or multiple objects. In realistic scenarios, any transmitted radio signal will be affected by the environment through reflections, diffraction at edges and corners etc. This causes a phenomenon known as multipath propagation, by which multiple instances of the transmitted signal having traversed different paths are heard by the receiver. These are known as Multi-Path Components (MPCs). The direct path (DP) between transmitter and receiver may also be occluded, causing what is referred to as non-Line-of-Sight (non-LOS) conditions. As a consequence of these effects, the estimated position of the object(s) may often be erroneous.This thesis focuses on how to achieve better localization accuracy by accounting for the above-mentioned multipath propagation and non-LOS effects. It is proposed how to mitigate these in the context of positioning based on estimation of the DP between transmitter and receiver. It is also proposed how to constructively utilize the additional information about the environment which they implicitly provide. This is all done in a framework wherein a given signal model and a map of the surroundings are used to build a mathematical model of the radio environment, from which the resulting MPCs are estimated.First, methods to mitigate the adverse effects of multipath propagation and non-LOS conditions for positioning based on estimation of the DP between transmitter and receiver are presented. This is initially done by using robust statistical measurement error models based on aggregated error statistics, where significant improvements are obtained without the need to provide detailed received signal information. The gains are seen to be even larger with up-to-date real-time information based on the estimated MPCs.Second, the association of the estimated MPCs with the signal paths predicted by the environmental model is addressed. This leads to a combinatorial problem which is approached with tools from multi-target tracking theory. A rich radio environment in terms of many MPCs gives better localization accuracy but causes the problem size to grow large—something which can be remedied by excluding less probable paths. Simulations indicate that in such environments, the single best association hypothesis may be a reasonable approximation which avoids the calculation of a vast number of possible hypotheses. Accounting for erroneous measurements is crucial but may have drawbacks if no such are occurring.Finally, theoretical localization performance bounds when utilizing all or a subset of the available MPCs are derived. A rich radio environment allows for good positioning accuracy using only a few transmitters/receivers, assuming that these are used in the localization process. In contrast, in a less rich environment where basically only the DP/LOS components are measurable, more transmitters/receivers and/or the combination of downlink and uplink measurements are required to achieve the same accuracy. The receiver’s capability of distinguishing between multiple MPCs arriving approximately at the same time also affects the localization accuracy.
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| 3. |
- Boström-Rost, Per, 1988-, et al.
(author)
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Sensor management for search and track using the Poisson multi-Bernoulli mixture filter
- 2021
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In: IEEE Transactions on Aerospace and Electronic Systems. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0018-9251 .- 1557-9603. ; 57:5, s. 2771-2783
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Journal article (peer-reviewed)abstract
- A sensor management method for joint multi-target search and track problems is proposed, where a single user-defined parameter allows for a trade-off between the two objectives. The multi-target density is propagated using the Poisson multi-Bernoulli mixture filter, which eliminates the need for a separate handling of undiscovered targets and provides the theoretical foundation for a unified search and track method. Monte Carlo simulations of two scenarios are used to evaluate the performance of the proposed method.
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| 4. |
- Jonas, Nordlöf, 1987-, et al.
(author)
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Belief Space Planning using Landmark Density Information
- 2020
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In: Proceedings of the 23rd International Conference on Information Fusion (FUSION). - : IEEE. - 9780578647098 - 9781728168302
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Conference paper (peer-reviewed)abstract
- An approach for belief space planning is presented, where knowledge about the landmark density is used as prior, instead of explicit landmark positions.Having detailed maps of landmark positions in a previously unvisited environment is considered unlikely in practice. Instead, it is argued that landmark densities should be used, as they could be estimated from other sources, such as ordinary maps or aerial imagery.It is shown that it is possible to use virtual landmarks to approximate the landmark density to solve the presented problem. This approximation is also shown to give small errors during evaluation.The approach is tested in a simulated environment, in conjunction with an extended information filter (EIF), where the computed path is shown to be superior compared to other alternative paths used as benchmarks.
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| 5. |
- Veibäck, Clas, 1985-
(author)
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Tracking the Wanders of Nature
- 2018
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Doctoral thesis (other academic/artistic)abstract
- Target tracking is a mature topic with over half a century of mainly military and aviation research. The field has lately expanded into a range of civilian applications due to the development of cheap sensors and improved computational power. With the rise of new applications, new challenges emerge, and with better hardware there is an opportunity to employ more elaborated algorithms.There are five main contributions to the field of target tracking in this thesis. Contributions I-IV concern the development of non-conventional models for target tracking and the resulting estimation methods. Contribution V concerns a reformulation for improved performance. To show the functionality and applicability of the contributions, all proposed methods are applied to and verified on experimental data related to tracking of animals or other objects in nature.In Contribution I, sparse Gaussian processes are proposed to model behaviours of targets that are caused by influences from the environment, such as wind or obstacles. The influences are learned online as a part of the state estimation using an extended Kalman filter. The method is also adapted to handle time-varying influences and to identify dynamic systems. It is shown to improve accuracy over the nearly constant velocity and acceleration models in simulation. The method is also evaluated in a sea ice tracking application using data from a radar on Svalbard.In Contribution II, a state-space model is derived that incorporates observations with uncertain timestamps. An example of such observations could be traces left by a target. Estimation accuracy is shown to be better than the alternative of disregarding the observation. The position of an orienteering sprinter is improved using the control points as additional observations.In Contribution III, targets that are confined to a certain space, such as animals in captivity, are modelled to avoid collision with the boundaries by turning. The proposed model forces the predictions to remain inside the confined space compared to conventional models that may suffer from infeasible predictions. In particular the model improves robustness against occlusions. The model is successfully used to track dolphins in a dolphinarium as they swim in a basin with occluded sections.In Contribution IV, an extension to the jump Markov model is proposed that incorporates observations of the mode that are state-independent. Normally, the mode is estimated by comparing actual and predicted observations of the state. However, sensor signals may provide additional information directly dependent on the mode. Such information from a video recorded by biologists is used to estimate take-off times and directions of birds captured in circular cages. The method is shown to compare well with a more time-consuming manual method.In Contribution V, a reformulation of the labelled multi-Bernoulli filter is used to exploit a structure of the algorithm to attain a more efficient implementation.Modern target tracking algorithms are often very demanding, so sound approximations and clever implementations are needed to obtain reasonable computational performance. The filter is integrated in a full framework for tracking sea ice, from pre-processing to presentation of results.
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| 6. |
- Zhao, Yuxin, 1986-, et al.
(author)
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Cramér–Rao Bounds for Filtering Based on Gaussian Process State-Space Models
- 2019
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In: 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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Journal article (peer-reviewed)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. |
- Forsling, Robin, 1988-, et al.
(author)
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Communication Efficient Decentralized Track Fusion Using Selective Information Extraction
- 2020
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In: Proceedings of the 23rd International Conference on Information Fusion (FUSION). - : Institute of Electrical and Electronics Engineers (IEEE). - 9780578647098 - 9781728168302
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Conference paper (peer-reviewed)abstract
- We consider a decentralized sensor network of multiple nodes with limited communication capability where the cross-correlations between local estimates are unknown. To reduce the bandwidth the individual nodes determine which subset of local information is the most valuable from a global perspective. Three information selection methods (ISM) are derived. The proposed ISM require no other information than the communicated estimates. The simulation evaluation shows that by using the proposed ISM it is possible to determine which subset of local information is globally most valuable such that both reduced bandwidth and high performance are achieved.
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| 8. |
- Forsling, Robin, 1988-, et al.
(author)
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Consistent Distributed Track Fusion Under Communication Constraints
- 2019
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In: Proceedings of the 22nd International Conference on Information Fusion (FUSION). - : IEEE. - 9780996452786 - 9781728118406
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Conference paper (peer-reviewed)abstract
- This paper addresses the problem of retrieving consistentestimates in a distributed network where the communication between the nodes is constrained such that only the diagonal elements of the covariance matrix are allowed to be exchanged. Several methods are developed for preserving and/or recovering consistency under the constraints imposed by the communication protocol. The proposed methods are used in conjunction with the covariance intersection method and the estimation performance is evaluated based on information usage and consistency. The results show that among the proposed methods, consistency can be preserved equally well at the transmitting node as at the receiving node.
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| 9. |
- Ho, Du, 1988-, et al.
(author)
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A sensor-to-sensor model-based change detection approach for quadcopters
- 2020
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In: Proceedings of the 21st IFAC World Congress, 2020. - : Elsevier. ; , s. 712-717
-
Conference paper (peer-reviewed)abstract
- This paper addresses the problem of change detection for a quadcopter in the presence ofwind disturbances. Different aspects of the quadcopter dynamics and various flight conditions have beeninvestigated. First, the wind is modeled using the Dryden wind model as a sum of a low-frequent and aturbulent part. Since the closed-loop control can compensate for system changes and disturbances andthe effect of the wind disturbance is significant, the residuals obtained from a standard simulation modelcan be misleading. Instead, a sensor-to-sensor submodel of the quadcopter is selected to detect a changein the payload using the Instrumental Variables (IV) cost function. It is shown that the mass variationcan be detected using the IV cost function in different flight scenarios.
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| 10. |
- Kasebzadeh, Parinaz, 1985-
(author)
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Learning Human Gait
- 2019
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Doctoral thesis (other academic/artistic)abstract
- Pedestrian navigation in body-worn devices is usually based on global navigation satellite systems (GNSS), which is a sufficient solution in most outdoor applications. Pedestrian navigation indoors is much more challenging. Further, GNSS does not provide any specific information about the gait style or how the device is carried. This thesis presents three contributions for how to learn human gait parameters for improved dead-reckoning indoors, and to classify the gait style and how the device is carried, all supported with extensive test data.The first contribution of this thesis is a novel approach to support pedestrian navigation in situations when GNSS is not available. A novel filtering approach, based on a multi-rate Kalman filter bank, is employed to learn the human gait parameters when GNSS is available using data from an inertial measurement unit (IMU). In a typical indoor-outdoor navigation application, the gait parameters are learned outdoors and then used to improve the pedestrian navigation indoors using dead-reckoning methods. The performance of the proposed method is evaluated with both simulated and experimental data.Secondly, an approach for estimating a unique gait signature from the inertial measurements provided by IMU-equipped handheld devices is proposed. The gait signatures, defined as one full cycle of the human gait, are obtained for multiple human motion modes and device carrying poses. Then, a parametric model of each signature, using Fourier series expansion, is computed. This provides a low-dimensional feature vector that can be used in medical diagnosis of certain physical or neurological diseases, or for a generic classification service outlined below.The third contribution concerns joint motion mode and device pose classification using the set of features described above. The features are extracted from the received IMU gait measurement and the computed gait signature. A classification framework is presented which includes standard classifiers, e.g. Gaussian process and neural network, with an additional smoothing stage based on hidden Markov model.There seems to be a lack of publicly available data sets in these kind of applications. The extensive datasets developed in this work, primarily for performance evaluation, have been documented and published separately. In the largest dataset, several users with four body-worn devices and 17 body-mounted IMUs performed a large number of repetitive experiments, with special attention to get well annotated data with ground truth position, motion mode and device pose.
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