| 1. |
- Bengtsson, Mats, et al.
(författare)
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Developing and Implementing a Program Interfacing Project Course in Electrical Engineering
- 2012
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Konferensbidrag (refereegranskat)abstract
- In this paper, we describe the ideas behind a second-year Design-Build course in Electrical Engineering. Electrical Engineering is a theoretical subject, and in such it is difficult to maintain the theoretical level in project courses introduced too early in the program, especially when core subjects like electromagnetic field theory are involved. This issue is addressed and we also describe our approach for the assessment of the students. We also discuss the different goals that were set up prior to the course from a program perspective; how we reasoned when designing the course, the assessment structure, and the output once the course was implemented
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| 2. |
- Bossér, Daniel, et al.
(författare)
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A Statistically Motivated Likelihood for Track-Before-Detect
- 2022
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Ingår i: 2022 IEEE INTERNATIONAL CONFERENCE ON MULTISENSOR FUSION AND INTEGRATION FOR INTELLIGENT SYSTEMS (MFI). - : IEEE. - 9781665460262 - 9781665460279
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Konferensbidrag (refereegranskat)abstract
- A theoretically sound likelihood function for passive sonar surveillance using a hydrophone array is presented. The likelihood is derived from first order principles along with the assumption that the source signal can be approximated as white Gaussian noise within the considered frequency band. The resulting likelihood is a nonlinear function of the delay-and-sum beamformer response and signal-to-noise ratio (SNR). Evaluation of the proposed likelihood function is done by using it in a Bernoulli filter based track-before-detect (TkBD) framework. As a reference, the same TkBD framework, but with another beamforming response based likelihood, is used. Results from Monte-Carlo simulations of two bearings-only tracking scenarios are presented. The results show that the TkBD framework with the proposed likelihood yields an approx. 10 seconds faster target detection for a target at an SNR of -27 dB, and a lower bearing tracking error. Compared to a classical detect-and-track target tracker, the TkBD framework with the proposed likelihood yields 4 dB to 5 dB detection gain.
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| 3. |
- Bossér, Daniel, et al.
(författare)
-
A Statistically Motivated Likelihood for Track-Before-Detect
- 2022
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Ingår i: 2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). - : IEEE. - 9781665460279 - 9781665460262
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Konferensbidrag (refereegranskat)abstract
- A theoretically sound likelihood function for passive sonar surveillance using a hydrophone array is presented. The likelihood is derived from first order principles along with the assumption that the source signal can be approximated as white Gaussian noise within the considered frequency band. The resulting likelihood is a nonlinear function of the delay-and-sum beamformer response and signal-to-noise ratio (SNR).Evaluation of the proposed likelihood function is done by using it in a Bernoulli filter based track-before-detect (TkBD) framework. As a reference, the same TkBD framework, but with another beamforming response based likelihood, is used. Results from Monte-Carlo simulations of two bearings-only tracking scenarios are presented. The results show that the TkBD framework with the proposed likelihood yields an approx. 10 seconds faster target detection for a target at an SNR of -27 dB, and a lower bearing tracking error. Compared to a classical detect-and-track target tracker, the TkBD framework with the proposed likelihood yields 4 dB to 5 dB detection gain.
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| 4. |
- Bossér, Daniel, et al.
(författare)
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Underwater Environment Modeling for Passive Sonar Track-Before-Detect
- 2023
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Ingår i: OCEANS 2023 - LIMERICK. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350332278 - 9798350332261
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Konferensbidrag (refereegranskat)abstract
- Underwater surveillance using passive sonar and track-before-detect technology requires accurate models of the tracked signal and the background noise. However, in an underwater environment, the signal channel is time-varying and prior knowledge about the spatial distribution of the background noise is unavailable. In this paper, an autoregressive model that captures a time-varying signal level caused by multi-path propagation is presented. In addition, a multi-source model is proposed to describe spatially distributed background noise. The models are used in a Bernoulli filter track-before-detect framework and evaluated using both simulated and sea trial data. The simulations demonstrate clear improvements in terms of target loss and improved ability to discern the target from the noisy background. An evaluation of the track-before-detect algorithm on the sea trial data indicates a performance gain when incorporating the proposed models in underwater surveillance and tracking problems.
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| 5. |
- Carlsson, Håkan, et al.
(författare)
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Inertial Navigation Using an Inertial Sensor Array
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- We present a comprehensive framework for fusing measurements from multiple and generally placed accelerometers and gyroscopes to perform inertial navigation. Using the angular acceleration provided by the accelerometer array, we show that the numerical integration of the orientation can be done with second-order accuracy, which is more accurate compared to the traditional first-order accuracy that can be achieved when only using the gyroscopes. Since orientation errors are the most significant error source in inertial navigation, improving the orientation estimation reduces the overall navigation error. The practical performance benefit depends on prior knowledge of the inertial sensor array, and therefore we present four different state-space models using different underlying assumptions regarding the orientation modeling. The models are evaluated using a Lie Group Extended Kalman filter through simulations and real-world experiments. We also show how individual accelerometer biases are unobservable and can be replaced by a six-dimensional bias term whose dimension is fixed and independent of the number of accelerometers.
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| 6. |
- Carlsson, Håkan, et al.
(författare)
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On-The-Fly Geometric Calibration of Inertial Sensor Arrays
- 2017
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Ingår i: Proceedings 2017 international conference on indoor positioning and indoor navigation (IPIN). - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- We present a maximum likelihood estimator for estimating the positions of accelerometers in an inertial sensor array. This method simultaneously estimates the positions of the accelerometers and the motion dynamics of the inertial sensor array and, therefore, does not require a predefined motion sequence nor any external equipment. Using an iterative block coordinate descent optimization strategy, the calibration problem can be solved with a complexity that is linear in the number of time samples. The proposed method is evaluated by Monte-Carlo simulations of an inertial sensor array built out of 32 inertial measurement units. The simulation results show that, if the array experiences sufficient dynamics, the position error is inversely proportional to the number of time samples used in the calibration sequence. Further, results show that for the considered array geometry and motion dynamics in the order of 2000 degrees/s and 2000 degrees/s(2), the positions of the accelerometers can be estimated with an accuracy in the order of 10(-6) m using only 1000 time samples. This enables fast on-the-fly calibration of the geometric errors in an inertial sensor array by simply twisting it by hand for a few seconds.
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| 7. |
- Carlsson, Håkan, et al.
(författare)
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Quantifying the Uncertainty of the Relative Geometry in Inertial Sensors Arrays
- 2021
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Ingår i: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 21:17, s. 19362-19373
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Tidskriftsartikel (refereegranskat)abstract
- We present an algorithm to estimate and quantify the uncertainty of the accelerometers' relative geometry in an inertial sensor array. We formulate the calibration problem as a Bayesian estimation problem and propose an algorithm that samples the accelerometer positions' posterior distribution using Markov chain Monte Carlo. By identifying linear substructures of the measurement model, the unknown linear motion parameters are analytically marginalized, and the remaining non-linear motion parameters are numerically marginalized. The numerical marginalization occurs in a low dimensional space where the gyroscopes give information about the motion. This combination of information from gyroscopes and analytical marginalization allows the user to make no assumptions of the motion before the calibration. It thus enables the user to estimate the accelerometer positions' relative geometry by simply exposing the array to arbitrary twisting motion. We show that the calibration algorithm gives good results on both simulated and experimental data, despite sampling a high dimensional space.
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| 8. |
- Carlsson, Håkan, et al.
(författare)
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Self-Calibration of Inertial Sensor Arrays
- 2021
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Ingår i: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 21:6, s. 8451-8463
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Tidskriftsartikel (refereegranskat)abstract
- A maximum likelihood estimator is presented for self-calibrating both accelerometers and gyroscopes in an inertial sensor array, including scale factors, misalignments, biases, and sensor positions. By simultaneous estimation of the calibration parameters and the motion dynamics of the array, external equipment is not required for the method. A computational efficient iterative optimization method is proposed where the calibration problem is divided into smaller subproblems. Further, an identifiability analysis of the calibration problem is presented. The analysis shows that it is sufficient to know the magnitude of the local gravity vector and the average scale factor gain of the gyroscopes, and that the array is exposed to two types of motions for the calibration problem to be well defined. The proposed estimator is evaluated by real-world experiments and by Monte Carlo simulations. The results show that the parameters can be consistently estimated and that the calibration significantly improves the accuracy of the motion estimation. This enables on-the-fly calibration of small inertial sensors arrays by simply twisting them by hand.
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| 9. |
- Das, Sandipan
(författare)
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State estimation with auto-calibrated sensor setup
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Localization and mapping is one of the key aspects of driving autonomously in unstructured environments. Often such vehicles are equipped with multiple sensor modalities to create a 360o sensing coverage and add redundancy to handle sensor dropout scenarios. As the vehicles operate in underground mining and dense urban environments the Global navigation satellite system (GNSS) is often unreliable. Hence, to create a robust localization system different sensor modalities like camera, lidar and IMU are used along with a GNSS solution. The system must handle sensor dropouts and work in real-time (~15 Hz), so that there is enough computation budget left for other tasks like planning and control. Additionally, precise localization is also needed to map the environment, which may be later used for re-localization of the autonomous vehicles as well. Finally, for all of these to work seamlessly, accurate calibration of the sensors is of utmost importance.In this PhD thesis, first, a robust system for state estimation that fuses measurements from multiple lidars and inertial sensors with GNSS data is presented. State estimation was performed in real-time, which produced robust motion estimates in a global frame by fusing lidar and IMU signals with GNSS components using a factor graph framework. The proposed method handled signal loss with a novel synchronization and fusion mechanism. To validate the approach extensive tests were carried out on data collected using Scania test vehicles (5 sequences for a total of ~ 7 Km). An average improvement of 61% in relative translation and 42% rotational error compared to a state-of-the-art estimator fusing a single lidar/inertial sensor pair is reported. Since precise calibration is needed for the localization and mapping tasks, in this thesis, methods for real-time calibration of the sensor setup is proposed. First, a method is proposed to calibrate sensors with non-overlapping field-of-view. The calibration quality is verified by mapping known features in the environment. Nevertheless, the verification process was not real-time and no observability analysis was performed which could give us an indicator of the analytical traceability of the trajectory required for motion-based online calibration. Hence, a new method is proposed where calibration and verification were performed in real-time by matching estimated sensor poses in real-time with observability analysis. Both of these methods relied on estimating the sensor poses using the state estimator developed in our earlier works. However, state estimators have inherent drifts and they are computationally intensive as well. Thus, another novel method is developed where the sensors could be calibrated in real-time without the need for any state estimation.
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| 10. |
- De Angelis, Alessio, et al.
(författare)
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Indoor positioning by ultra wide band radio aided inertial navigation
- 2009
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Ingår i: 19th IMEKO World Congress 2009. - 9781615675937 ; , s. 450-455
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Konferensbidrag (refereegranskat)abstract
- In this paper, a research activity aimed at developing an indoor positioning system is presented. The realized system prototype uses sensor fusion techniques to combine information from two sources: a local Ultra-Wideband (UWB) radio based distance-measuring system infrastructure and an Inertial Navigation System (INS). The UWB system provides a measure of distance between two transceivers by measuring the time-of-flight of pulses. Its principle of operation is briefly described, together with the main features of its architecture. Furthermore, the main characteristics of the INS and of the Extended Kalman Filter information fusion approach are presented. Finally, some experimental results are provided, relative to static and dynamic position measurements.
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