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This review aims at assessing the opportunities and challenges of creating and using digital twins for process industrial systems over their life-cycle in the context of estimation and control. The scope is, therefore, to provide a survey on mechanisms to generate models for process industrial systems using machine learning (purely data-driven) and automated equation-based modeling. In particular, we consider learning, validation, and updating of large-scale (i.e., plant-wide or plant-stage but not component-wide) equation-based process models. These aspects are discussed in relation to typical application cases for the digital twins creating value for users both on the operational and planning level for process industrial systems. These application cases are also connected to the needed technologies and the maturity of those as given by the state of the art. Combining all aspects, a way forward to enable the automatic generation and updating of digital twins is proposed, outlining the required research and development activities. The paper is the outcome of the research project AutoTwin-PRE funded by Strategic Innovation Program PiiA within the Swedish Innovation Agency VINNOVA and the academic version of an industry report prior published by PiiA.

Publisher Copyright: © 2022 International Society of Information Fusion. This paper is concerned with discrete time Kalman-type filtering with state transition and measurement noises that may be non-additive or non-linearly transformed. More specifically, we extend the iterative estimation algorithm Posterior Linearization Filter (PLF) for estimation with this kind of noises. The approach solves the prediction and update step simultaneously, which allows to use the PLF iterations to improve the estimation in the non-linear state transition model. The proposed algorithm also produces single step fixed-lag smoothing estimates. We show in examples how the proposed approach can be used with non-Gaussian state transition noises and non-linearly transformed state transition noises.

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Received signal strength based device-free localization applications utilize a model that relates the measurements to position of the wireless sensors and person, and the underlying inverse problem is solved either using an imaging method or a nonlinear Bayesian filter. In this paper, it is shown that the Bayesian filters nearly reach the posterior Cramér-Rao bound and they are superior with respect to imaging approaches in terms of localization accuracy because the measurements are directly related to position of the person. However, Bayesian filters are known to suffer from divergence issues and in this paper, the problem is addressed by introducing a novel Bayesian filter. The developed filter augments the measurement model of a Bayesian filter with position estimates from an imaging approach. This bounds the filter's measurement residuals by the position errors of the imaging approach and as an outcome, the developed filter has robustness of an imaging method and tracking accuracy of a Bayesian filter. The filter is demonstrated to achieve a localization error of 0.11 \text{ m}0.11m in a 75 \; \text{m}^275m2 open indoor deployment and an error of 0.29 \text{ m}0.29m in a 82 \; \text{m}^282m2 apartment experiment, decreasing the localization error by 30-48 percent with respect to a state-of-the-art imaging method.

This paper is concerned with sigma-point methods for filtering in nonlinear systems, where the process and measurement noise are heavy tailed and enter the system nonadditively. The problem is approached within the framework of assumed density filtering and the necessary statistics are approximated using sigma-point methods developed for Student’s t-distribution. This leads to UKF/CKF-type of filters for Student’s t-distribution. Four different sigma-point methods are considered that compute exact expectations of polynomials for orders up to 3, 5, 7, and 9, respectively. The resulting algorithms are evaluated in a simulation example and real data from a pedestrian dead-reckoning experiment. In the simulation experiment the nonlinear Student’s t filters are found to be faster in suppressing large errors in the state estimates in comparison to the UKF when filtering in nonlinear Gaussian systems with outliers in process and measurement noise. In the pedestrian dead-reckoning experiment the sigma-point Student’s t filter was found to yield better loop closure and path length estimates as well as significantly improved robustness towards extreme accelerometer measurement spikes.

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. Godkänd; 2014; 20140411 (rolhos); Nedanstående person kommer att disputera för avläggande av teknologie doktorsexamen. Namn: Roland Hostettler Ämne: Reglerteknik/Automatic Control Avhandling: Traffic Monitoring using Road Side Sensors: Modeling and Estimation Opponent: Professor Thomas Schön, Avd för systemteknik, Institutionen för informationsteknologi, Uppsala universitet, Ordförande: Biträdande professor Wolfgang Birk, Avd för system och interaktion, Institutionen för system- och rymdteknik, Luleå tekniska universitet Tid: Onsdag den 18 juni 2014, kl 09.00 Plats: A1545, Luleå tekniska universitet

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. Godkänd; 2012; 20120404 (rolhos); LICENTIATSEMINARIUM Ämnesområde: Reglerteknik/Automatic Control Examinator: Bitr professor Wolfgang Birk, Institutionen för system- och rymdteknik, Luleå tekniska universitet Diskutant: Tekn dr Johan Casselgren, Institutionen för teknikvetenskap och matematik, Luleå tekniska universitet Tid: Onsdag den 16 maj 2012 kl 10.00 Plats: A1545, Luleå tekniska universitet

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. Godkänd; 2011; Bibliografisk uppgift: Publicerat i 8th European Conference on Wireless Sensor Networks (EWSN 2011) IEEE; 20110107 (lauril) iRoad

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. Godkänd; 2009; 20091013 (wolfgang)

In nowadays road traffic, road user safety is a main concern for vehicle manufacturers and road traffic administrations. New technologies enable complex and save vehicles and modern building techniques are the key to high quality roads. Intelligent road infrastructure systems are available, e.g. for adaptively routing traffic or surveillance of crucial traffic junctions. However, such systems are relatively sluggish in providing assistance to drivers of individual vehicles anywhere along the road. Within the framework of the iRoad project, this thesis focuses on the exploitation of road surface vibrations for vehicle parameter estimation. Measured road vibrations are analyzed and phenomena are identified. Algorithms for vehicle detection and axle estimation based on the findings are derived and their performance is evaluated and discussed. It is found that a vehicle detection of over 99% can be achieved by using the proposed algorithm, independent of the vehicle speed. Axle parameter estimation yields reasonable results for the number of axles and their relative positions as well as transferred energy. In combination with another method to determine the vehicle speed, the wheelbase can be calculated. Validerat; 20101217 (root)