| 1. |
- Bergström, Andreas, 1978-
(författare)
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Timing-Based Localization using Multipath Information
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)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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| 2. |
- Boström-Rost, Per, 1988-, et al.
(författare)
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Optimal Range and Beamwidth for Radar Tracking of Maneuvering Targets Using Nearly Constant Velocity Filters
- 2020
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Ingår i: Proceedings of 2020 IEEE Aerospace Conference. - 9781728127347 - 9781728127354
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Konferensbidrag (refereegranskat)abstract
- For a given radar system on an unmanned air vehicle, this work proposes a method to find the optimal tracking rangeand the optimal beamwidth for tracking a maneuvering target. An inappropriate optimal range or beamwidth is indicative ofthe need for a redesign of the radar system. An extended Kalman filter (EKF) is employed to estimate the state of the target using measurements of the range and bearing from the sensor to the target. The proposed method makes use of an alpha-beta filter to predict the expected tracking performanceof the EKF. Using an assumption of the maximum acceleration of the target, the optimal tracking range (or beamwidth) is determined as the one that minimizes the maximum mean squared error (MMSE) of the position estimates while satisfying a user-defined constraint on the probability of losing track of the target.The applicability of the design method is verified using Monte Carlo simulations.
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| 3. |
- Boström-Rost, Per, 1988-, et al.
(författare)
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PMBM Filter With Partially Grid-Based Birth Model With Applications in Sensor Management
- 2022
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Ingår i: IEEE Transactions on Aerospace and Electronic Systems. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0018-9251 .- 1557-9603 .- 2371-9877. ; 58:1, s. 530-540
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Tidskriftsartikel (refereegranskat)abstract
- This article introduces a Poisson multi-Bernoulli mixture (PMBM) filter in which the intensities of target birth and undetected targets are grid-based. A simplified version of the Rao-Blackwellized point mass filter is used to predict the intensity of undetected targets and to initialize tracks of targets detected for the first time. The grid approximation can efficiently represents intensities with abrupt changes with relatively few grid points compared to the number of Gaussian components needed in conventional PMBM implementations. This is beneficial in scenarios where the sensors field of view is limited. The proposed method is illustrated in a sensor management setting, where trajectories of sensors with limited fields of view are controlled to search for and track the targets in a region of interest.
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| 4. |
- Boström-Rost, Per, 1988-, et al.
(författare)
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Sensor management for search and track using the Poisson multi-Bernoulli mixture filter
- 2021
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Ingår i: 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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Tidskriftsartikel (refereegranskat)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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| 5. |
- Boström-Rost, Per, 1988-
(författare)
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Sensor Management for Target Tracking Applications
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Many practical applications, such as search and rescue operations and environmental monitoring, involve the use of mobile sensor platforms. The workload of the sensor operators is becoming overwhelming, as both the number of sensors and their complexity are increasing. This thesis addresses the problem of automating sensor systems to support the operators. This is often referred to as sensor management. By planning trajectories for the sensor platforms and exploiting sensor characteristics, the accuracy of the resulting state estimates can be improved. The considered sensor management problems are formulated in the framework of stochastic optimal control, where prior knowledge, sensor models, and environment models can be incorporated. The core challenge lies in making decisions based on the predicted utility of future measurements.In the special case of linear Gaussian measurement and motion models, the estimation performance is independent of the actual measurements. This reduces the problem of computing sensing trajectories to a deterministic optimal control problem, for which standard numerical optimization techniques can be applied. A theorem is formulated that makes it possible to reformulate a class of nonconvex optimization problems with matrix-valued variables as convex optimization problems. This theorem is then used to prove that globally optimal sensing trajectories can be computed using off-the-shelf optimization tools. As in many other fields, nonlinearities make sensor management problems more complicated. Two approaches are derived to handle the randomness inherent in the nonlinear problem of tracking a maneuvering target using a mobile range-bearing sensor with limited field of view. The first approach uses deterministic sampling to predict several candidates of future target trajectories that are taken into account when planning the sensing trajectory. This significantly increases the tracking performance compared to a conventional approach that neglects the uncertainty in the future target trajectory. The second approach is a method to find the optimal range between the sensor and the target. Given the size of the sensor's field of view and an assumption of the maximum acceleration of the target, the optimal range is determined as the one that minimizes the tracking error while satisfying a user-defined constraint on the probability of losing track of the target. While optimization for tracking of a single target may be difficult, planning for jointly maintaining track of discovered targets and searching for yet undetected targets is even more challenging. Conventional approaches are typically based on a traditional tracking method with separate handling of undetected targets. Here, it is shown that the Poisson multi-Bernoulli mixture (PMBM) filter provides a theoretical foundation for a unified search and track method, as it not only provides state estimates of discovered targets, but also maintains an explicit representation of where undetected targets may be located. Furthermore, in an effort to decrease the computational complexity, a version of the PMBM filter which uses a grid-based intensity to represent undetected targets is derived.
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| 6. |
- Jonas, Nordlöf, 1987-, et al.
(författare)
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Belief Space Planning using Landmark Density Information
- 2020
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Ingår i: Proceedings of the 23rd International Conference on Information Fusion (FUSION). - : IEEE. - 9780578647098 - 9781728168302
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Konferensbidrag (refereegranskat)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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| 7. |
- Jonas, Nordlöf, 1987-, et al.
(författare)
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LiDAR-Landmark Modeling for Belief-Space Planning using Aerial Forest Data
- 2022
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Ingår i: Proceedings of the 25th International Conference on Information Fusion (FUSION). - : IEEE. - 9781737749721 - 9781665489416
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Konferensbidrag (refereegranskat)abstract
- A belief-space planning problem for GNSS-denied areas is studied, where knowledge about the landmark density is used as prior, instead of explicit landmark positions. To get accurate predictions of the future information gained from observations, the probability of detecting landmarks needs to be taken into account in addition to the probability of the existence of landmarks. Furthermore, these probabilities need to be calculated from prior data without knowledge of explicit landmarks. It is shown in this paper how the landmark detection probabilities can be generated for a ground-to-ground LiDAR sensor and integrated in the path-planning problem. Moreover, it is also shown how prior information can be generated for a forest scenario. Lastly, the approach is evaluated in a simulated environment using a real landmark detector applied to a simulated point cloud. Compared to previous approaches, an informative path planner, integrating the proposed approximation, is able to reduce the platform pose uncertainty. This is achieved using only prior aerial data of the environment.
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| 8. |
- Nordlöf, Jonas, 1987-, et al.
(författare)
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Improved Virtual Landmark Approximation for Belief-Space Planning
- 2021
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Ingår i: Proceedings of 2021 IEEE 24th International Conference on Information Fusion (FUSION). - : IEEE. - 9781737749714 - 9781665414272 ; , s. 813-820
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Konferensbidrag (refereegranskat)abstract
- A belief-space planning problem for GNSS-denied areas is studied where the location and number of landmarks available are unknown when performing the planning. To be able to plan an informative path in this situation, an algorithm using virtual landmarks to position the platform during the planning phase is studied. The virtual landmarks are selected to capture the expected information available in different regions of the map, based on the beforehand known landmark density. The main contribution of this work is a better approximation of the obtained information from the virtual landmarks and a theoretical study of the properties of the approximation. Furthermore, the proposed approximation, in itself and its use in a path planner, is investigated with successful results.
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| 9. |
- Nordlöf, Jonas, 1987-
(författare)
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On Landmark Densities in Minimum-Uncertainty Motion Planning
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Accurate self-positioning of autonomous mobile platforms is important when performing tasks such as target tracking, reconnaissance and resupply missions. Without access to an existing positioning infrastructure, such as Global Navigation Satellite Systems (GNSS), the platform instead needs to rely on its own sensors to obtain an accurate position estimate. This can be achieved by detecting and tracking landmarks in the environment using techniques such as simultaneous localization and mapping (SLAM). However, landmark-based SLAM approaches do not perform well in areas without landmarks or when the landmarks do not provide enough information about the environment. It is therefore desirable to estimate and minimize the position uncertainty while planning how to perform the task. A complicating factor is that the landmarks used in SLAM are not known at the time of planning.In this thesis, it is shown that by integrating SLAM and path planning, paths can be computed that are favorable, from a localization point of view, during motion execution. In particular, it is investigated how prior knowledge of landmark distributions, or densities, can be used to predict the information gained from a region. This is done without explicit knowledge of landmark positions. This prediction is then integrated into the path-planning problem.The first contribution is the introduction of virtual landmarks which represent the expected information in unexplored regions during planning. Two approaches to construct the virtual landmarks that capture the expected information available, based on the beforehand known landmark density, are given. The first approach can be used with any sensor configuration while the second one uses properties of range-bearing sensors, such as LiDAR sensors, to improve the quality of the approximation.The second contribution is a methodology for generating landmark densities from prior data for a forest scenario. These densities were generated from publicly available aerial data used in the Swedish forest industry.The third contribution is an approach to compute the probability of detecting pole-based landmarks in LiDAR point clouds. The approach uses properties of the sensor, the landmark detector, and the probability of occlusion from other landmarks in order to model the detection probability. The model accuracy has been validated in simulations where a real landmark detector and simulated Li-DAR point clouds have been used in a forest scenario.The final contribution is a position-uncertainty aware path-planning approach. This approach utilizes virtual landmarks, the landmark densities, and the land-mark detection probabilities, to produce paths which are advantageous from a positioning point of view. The approach is shown to reduce the platform position uncertainty in several different simulated scenarios without prior knowledge of explicit landmark positions. The computed position uncertainty is shown to be relatively comparable to the uncertainty obtained when executing the path. Furthermore, the generated paths show characteristics that make sense from an application point of view.
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| 10. |
- Forsling, Robin, 1988-, et al.
(författare)
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Communication Efficient Decentralized Track Fusion Using Selective Information Extraction
- 2020
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Ingår i: Proceedings of the 23rd International Conference on Information Fusion (FUSION). - : Institute of Electrical and Electronics Engineers (IEEE). - 9780578647098 - 9781728168302
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Konferensbidrag (refereegranskat)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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