Change search
Refine search result
1 - 20 of 20
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Alhashimi, Anas
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Hostettler, Roland
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    An Improvement in the Observation Model for Monte Carlo Localization2014In: Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics: Vienna, Austria, 1-3, September, 2014, SciTePress, 2014, p. 498-505Chapter in book (Refereed)
    Abstract [en]

    Accurate and robust mobile robot localization is very important in many robot applications. Monte Carlo localization (MCL) is one of the robust probabilistic solutions to robot localization problems. The sensor model used in MCL directly influence the accuracy and robustness of the pose estimation process. The classical beam models assumes independent noise in each individual measurement beam at the same scan. In practice, the noise in adjacent beams maybe largely correlated. This will result in peaks in the likelihood measurement function. These peaks leads to incorrect particles distribution in the MCL. In this research, an adaptive sub-sampling of the measurements is proposed to reduce the peaks in the likelihood function. The sampling is based on the complete scan analysis. The specified measurement is accepted or not based on the relative distance to other points in the 2D point cloud. The proposed technique has been implemented in ROS and stage simulator. The result shows that selecting suitable value of distance between accepted scans can improve the localization error and reduce the required computations effectively.

  • 2.
    Birk, Wolfgang
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Hostettler, Roland
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Bardov, Vladimir
    Obertov, Dmitrii
    Vehicle Speed Determination (Patent pending)Patent (Other (popular science, discussion, etc.))
  • 3.
    Birk, Wolfgang
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Hostettler, Roland
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    Lundberg Nordenvaad, Magnus
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Eliasson, Jens
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Gylling, Arne
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Mäkitaavola, Henrik
    Project: iRoad2011Other (Other (popular science, discussion, etc.))
  • 4.
    Hostettler, Roland
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    A Two Filter Particle Smoother for Wiener State-Space Systems2015In: 2015 IEEE International Conference on Control Applications (CCA 2015): Sydney, Australia, September 21-23 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 412-417, article id 7320664Conference paper (Refereed)
    Abstract [en]

    In this article, a two filter particle smoothing algorithm for Wiener state-space systems is proposed. The smoother is obtained by exploiting the model structure. This leads to a suitable proposal density for the backward filter inherent in the problem instead of introducing an artificial one. Numerical examples are provided in order to illustrate the proposed algorithm's performance and to compare it to current state of the art smoothers from the literature. It is found that the proposed method yields comparable results with less computational complexity as backward simulation-based particle smoothing algorithms.

  • 5.
    Hostettler, Roland
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Traffic Monitoring using Road Side Sensors: Modeling and Estimation2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In many modern societies, different sensors have started to penetrate life in many new ways.Examples include personal devices for monitoring health and well-being, supervision and power distribution in the smart grid, or smart farming that takesweather and soil conditions into account to efficiently cultivate fields. In order to obtain the desired data, such sensor systems requirewell-designed signal processing algorithms that infer the parameters of interest from the measured quantities. In this thesis, algorithms for traffic monitoring using road side sensors are proposed. Thesensors considered are a combination of an accelerometer measuring road surfacewaves and a magnetometer measuring magnetic disturbances, both caused by vehicles passing the sensors.The research problems addressed are: (1) the feasibility of using road surface waves fortraffic monitoring, (2) the modeling of road surface waves, and (3) combining the measurements of the accelerometer and the magnetometer.These three problems are addressed in the six research papers composing this thesis. First, it is shown that it is indeed viable to exploit road surface waves for estimating vehicle parameters and research challenges are identified by analyzing a first field test. Based on these conclusions, it is shown how to model waves in pavements using system identification and a semi-parametric wave propagation model. Furthermore, an efficient algorithm for estimating the driving direction using magnetometers only is proposed and evaluated. Finally, it is shown how to combine the two sensors. First, an iterative particle smootherbased system identification algorithm is used to jointly estimate the vehicle trajectory as wellas unknown parameters in the system model. Second, a multi-rate particle filter is proposed where unknown parameters are treated through marginalization.Based on the work in this thesis, future research directions are proposed. These include the improvement of some of the models to address problems encountered in the trajectory estimation and tracking algorithms aswell as further development of the estimation methods to make them more efficient and take prior information and constraints into account.

  • 6.
    Hostettler, Roland
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Vehicle parameter estimation using road surface vibrations2012Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Traffic safety is a big concern of many modern societies. Every year, car accidents cause many injuries and fatalities. This has aected many car manufacturers and governments equally. While governments try to reduce the number of accidents by educating drivers or imposing regulations, car manufacturers have successfully incorporated diverse safety functions such as seat belts, anti-lock breaking systems, or airbags in their vehicles. Recent advances in communication technologies have given rise to new approaches for advancing vehicular safety even more: having vehicles and the road infrastructure communicating with each other will enable new safety systems that can also take the behavior of other road users into consideration. The information provided by the infrastructure stems from roadside sensors that continuously measure traffic and track vehicles. Parameters of interest are, among others, vehicle class or vehicle speed.Clearly, many sensors for estimating these parameters exist. However, these are often too limited, too expensive for maintenance, or not developed well enough in order to be deployed in large scale. For example, vision-based systems can provide very comprehensive information, as long as the line of sight is not obstructed and enough computational resources are available. On the other hand, miniaturized sensors are becoming more and more popular in conjunction with wireless sensor networks. This approach is also put forth in this work. The aim of the thesis is to examine the potential of using accelerometers mounted on the road surface for estimating parameters of vehicles passing the sensor.In the four research papers composing this thesis, it is shown that this novel approach is viable. First, the feasibility is analyzed based on measurements of real traffic and research challenges are identied. The two rst applications are derived from that: vehicle detection and wheelbase estimation, where the latter can only be achieved under the knowledge of a vehicle's speed. Then, the underlying mechanisms, namely wave propagation in the road is examined in a system identication framework. It is found that Lamb waves, that is, waves in a thin plate, are predominant and a model for describing this is proposed. Finally, an Extended Kalman Filter for vehicle tracking based on a moving constant force and the wave propagation model is proposed. As a result, estimations of the vehicle velocity and wheelbase are automatically obtained.It is indicated that future research should further rene the physical model of the source (vehicle) as well as the wave propagation and also improve the proposed Kalman Filter. Furthermore, there is still a lot of potential in exploiting other features of the measured signal.

  • 7.
    Hostettler, Roland
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Analysis of the adaptive threshold vehicle detection algorithm applied to traffic vibrations2011In: Proceedings of the 18th IFAC World Congress, 2011, IFAC, International Federation of Automatic Control , 2011, p. 2150-2155Conference paper (Refereed)
    Abstract [en]

    This paper discusses and analyzes the performance of the Adaptive Threshold Detection Algorithm for vehicle detection based on road traffic vibrations. The algorithm, originally developed for magnetometer- and microphone-based vehicle detection, is adapted for the usage with seismic signals and then analyzed in a statistical framework. It is found that the algorithm can be applied to this kind of signals and promising results are obtained in simulations and tests on measurement data. Further testing using real traffic data is required in order to obtain more significant results.

  • 8.
    Hostettler, Roland
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Analysis of the adaptive threshold vehicle detection algorithm applied to traffic vibrations2011In: Proceedings of the 18th IFAC World Congress, 2011, IFAC, International Federation of Automatic Control , 2011, p. 2150-2155Conference paper (Refereed)
    Abstract [en]

    This paper discusses and analyzes the performance of the Adaptive Threshold Detection Algorithm for vehicle detection based on road traffic vibrations. The algorithm, originally developed for magnetometer- and microphone-based vehicle detection, is adapted for the usage with seismic signals and then analyzed in a statistical framework. It is found that the algorithm can be applied to this kind of signals and promising results are obtained in simulations and tests on measurement data. Further testing using real traffic data is required in order to obtain more significant results.

  • 9.
    Hostettler, Roland
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lundberg Nordenvaad, Magnus
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Maximum Likelihood Estimation of the Non-Parametric FRF for Pulse-Like Excitations2016In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 61, no 8, p. 2276-2281Article in journal (Refereed)
    Abstract [en]

    This technical note introduces the closed form maximum likelihood estimator for estimating the coefficients of the non-parametric frequency response function from system identification experiments. It is assumed that the experiments consist of repeated pulse excitations and that both the excitation and system response are measured which leads to an error-in-variables setting. Monte Carlo simulations indicate that the estimator achieves efficiency at low signal-to-noise ratios with only few measurements. Comparison with the least-squares estimator shows that better, unbiased results are obtained.

  • 10.
    Hostettler, Roland
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nordenvaad, Magnus Lundberg
    Extended Kalman filter for vehicle tracking using road surface vibration measurements2013In: IEEE 51st Annual Conference on Decision and Control: CDC 2012, Piscataway, NJ: IEEE Communications Society, 2013Conference paper (Refereed)
    Abstract [en]

    This paper addresses a novel method for vehicle tracking using an extended Kalman filter and measurements of road surface vibrations from a single accelerometer. First, a measurement model for vibrations caused by vehicular road traffic is developed. Then the identifiability of the involved parameters is analyzed. Finally, the measurement model is combined with a constant speed motion model and the Kalman filter is derived. Simulation and measurement results indicate that the approach is feasible and show where further development is needed.

  • 11.
    Hostettler, Roland
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Birk, Wolfgang
    Nordenvaad, Magnus Lundberg
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Feasibility of road vibrations-based vehicle property sensing2010In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 4, no 4, p. 356-364Article in journal (Refereed)
    Abstract [en]

    This article discusses a novel approach to vehicle property sensing based on traffic-induced road surface vibrations and investigates the feasibility of this approach. Road surface vibrations from real-life experiments are acquired using three-axis accelerometers and the data are analysed. Based on the assessment of the data, a first coarse scheme for axle detection of passing vehicles is developed. The scheme is then evaluated using measurement data from a highway with moderate traffic intensity but diverse traffic. It is found that the proposed approach is feasible and the estimation scheme yields promising results. Furthermore, delimitations, encountered problems and identified research challenges are discussed and future research directions are given.

  • 12.
    Hostettler, Roland
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nordenvaad, Magnus Lundberg
    Joint Vehicle Trajectory and Model Parameter Estimation using Road Side Sensors2015In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 15, no 9, p. 5075-5086Article in journal (Refereed)
    Abstract [en]

    This article shows how a particle smoother based system identification method can be applied for estimating the trajectory of road vehicles. As sensors, a combination of an accelerometer measuring the road surface vibrations and a magnetometer measuring magnetic disturbances mounted on the side of the road are considered. First, sensor models describing the measurements of the two sensors are introduced. It is shown that these depend on unknown, static parameters that have to be considered in the estimation. Second, the sensor models are combined with a two-dimensional constant velocity motion model. Third, the system identification algorithm is introduced which iteratively runs a Rao-Blackwellized particle smoother to estimate the vehicle trajectory followed by an expectation-maximization step to estimate the parameters. Finally, the method is applied to both simulation and measurement data. It is found that the method works well in general and some issues when real data is considered are identified as future work.

  • 13. Hostettler, Roland
    et al.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Nordenvaad, Magnus Lundberg
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Surface mounted vehicle property sensing for cooperative vehicle infrastructure systems2009In: 16th World Congress and Exhibition on Intelligent Transport Systems 2009: 16th ITS World Congress ; Stockholm, Sweden, 21 - 25 September 2009, Red Hook: Curran Associates, Inc., 2009Conference paper (Refereed)
    Abstract [en]

    This paper presents first results for vehicle detection and vehicle property estimation based on the assessment of traffic induced vibrations in the road surface. A surface mounted 3D accelerometer device is used to register the vibrations in the surface. Acquired data from experiments on roads are used to design methods that are able to detect vehicle passages, estimate the number of axles of a vehicle and also deduce the wheel-base for passenger cars. Evaluation of the methods indicate that the accelerometer based approach is feasible and should be further developed in order to deduce vehicle properties like vehicle speed and distance to sensing device from one device. Moreover, results for the vehicle detection on real-life traffic data from the E4 in northern Sweden are summarized.

  • 14.
    Hostettler, Roland
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Djurić, Petar
    Department of Electrical and Computer Engineering, Stony Brook University.
    Vehicle Tracking Based on Fusion of Magnetometer and Accelerometer Sensor Measurements with Particle Filtering2015In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 64, no 11, p. 4917-4928Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a method for vehicle tracking on roadways using measurements of magnetometers and accelerometers. The measurements are used to build a low-cost, low-complexity vehicle tracking sensor platform for highway traffic monitoring. First, the problem is formulated by introducing the process model for the motion of the vehicle on the road and two measurement models: one for each of the sensors. Second, it is shown how the measurements of the sensors can be fused using particle filtering. The standard sampling importance resampling (SIR) particle filter is extended for processing of multirate sensor measurements and models that employ unknown static parameters. The latter are treated by Rao–Blackwellization. The performance of the method is demonstrated by computer simulations. It is found that it is feasible to fuse the two sensors for vehicle tracking and that the proposed multirate particle filter performs better than particle filters that process only measurements of one of the sensors. The main contribution of this paper is the novel approach of fusing the measurements of road-mounted magnetometers and accelerometers for vehicle tracking and traffic monitoring.

  • 15.
    Hostettler, Roland
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nordenvaad, Magnus Lundberg
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A system identification approach to modeling of wave propagation in pavements2012In: 16th IFAC Symposium on System Identification, IFAC, International Federation of Automatic Control , 2012, p. 292-297Conference paper (Refereed)
    Abstract [en]

    In this paper, modeling of the pavement as a wave propagation medium and estimation of the corresponding model parameters is approached from a system identification perspective. A model based on the physical background is proposed and the corresponding parameters are then estimated from measurement data. In order to achieve the latter, two estimators are proposed, their performance evaluated, and then applied to the measurement data. It is found that the proposed methods are applicable and the results show that different eigenmodes of the structure are excited.

  • 16.
    Hostettler, Roland
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nordenvaad, Magnus Lundberg
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    The pavement as a waveguide: modeling, system identification, and parameter estimation2014In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 63, no 8, p. 2052-2063Article in journal (Refereed)
    Abstract [en]

    This paper presents modeling of wave propagation in pavements from a system identification point of view. First, a model based on the physical structure is derived. Second, experiment design and evaluation are discussed and maximum-likelihood estimators for estimating the model parameters are introduced, assuming an error-in-variables setting. Finally, the proposed methods are applied to measurement data from two experiments under varying environmental conditions. It is found that the proposed methods can be used to estimate the dispersion curves of the considered waveguide and the results can be used for further analysis

  • 17.
    Kleyko, Denis
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hostettler, Roland
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Distance- Spanning Technology.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Comparison of Machine Learning Techniques for Vehicle Classification using Road Side Sensors2015In: Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems: Las Palmas, 15-18 Sept. 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 572-577, article id 7313192Conference paper (Refereed)
    Abstract [en]

    The main contribution of this paper is a comparison of different machine learning algorithms for vehicle classification according to the "Nordic system for intelligent classification of vehicles" standard using measurements of road surface vibrations and magnetic field disturbances caused by vehicles. The algorithms considered are logistic regression, neural networks, and support vector machines. They are evaluated on a large dataset, consisting of 3074 samples and hence, a good estimate of the actual classification rate is obtained. The results show that for the considered classification problem logistic regression is the best choice with an overall classification rate of 93.4%.

  • 18.
    Riliskis, Laurynas
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Osipov, Evgeny
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hostettler, Roland
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Mäkitaavola, Henrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Eliasson, Jens
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Enabling remote controlled road surface networks for enhanced ITS2011Conference paper (Refereed)
    Abstract [en]

    Intelligent Transportation Systems (ITS) will, in the future, play a key role to improve transportation efficiency and safety. However, cost-benefit of deploying traditional ITS is retarded by expensive equipment, infrastructure, installation and maintenance. The demo presents a replica of a real world experimental ITS application using recently proposed Road Surface Network architecture. The demonstrated "intelligent roundabout'' application is intended to warn and inform drivers about an upcoming roundabout and to prevent driving straight into collision. We show a lab prototype system consisting of: an authentic sensor node platform enabled for car detection, secure multihop communications and the running light application, a base station with system control center.

  • 19.
    Wahlström, Niklas
    et al.
    Linköpings universitet.
    Hostettler, Roland
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Fredrik
    Linköpings universitet.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Classification of driving direction in traffic surveillance using magnetometers2014In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 15, no 4, p. 1405-1418Article in journal (Refereed)
    Abstract [en]

    Traffic monitoring using low-cost two-axis magnetometers is considered. Although detection of metallic vehicles is rather easy, detecting the driving direction is more challenging. We propose a simple algorithm based on a nonlinear transformation of the measurements, which is simple to implement in embedded hardware. A theoretical justification is provided, and the statistical properties of the test statistic are presented in closed form. The method is compared with the standard likelihood ratio test on both simulated data and real data from field tests, where very high detection rates are reported, despite the presence of sensor saturation, measurement noise, and near-field effects of the magnetic field.

  • 20.
    Wahlström, Niklas
    et al.
    Linköping University.
    Hostettler, Roland
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Fredrik
    Linköping University.
    Birk, Wolfgang
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Rapid classification of vehicle heading direction with two-axis magnetometer2012In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing : ICASSP: 25-30 March 2012, Piscataway, NJ: IEEE Communications Society, 2012, p. 3385-3388Conference paper (Refereed)
    Abstract [en]

    We present an approach for computing the heading direction of a vehicle by processing measurements from a 2-axis magnetometer rapidly. The proposed method relies on a non-linear transformation of the measurement data comprising only two inner products. Deterministic analysis of the signal model shows how the heading direction is contained in the signal and the proposed estimator is analyzed in terms of its statistical properties. Experimental verification indicates that good performance is achieved under the presence of saturation, measurement noise, and near field effects.

1 - 20 of 20
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf