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The problem addressed in this paper is the optimal allocation of a CPU to a number of software control tasks. Each task is used to implement a feedback controller for a linear and time invariant system and is activated with a fixed period. On every periodic activation, the task executes a job, which collects the output of the system, and produces the control values after executing for a random computation time. If a job's duration exceeds a deadline, then the job is cancelled and the control values are not updated. The systems to be controlled are affected by process noise. Therefore the performance of each control loop can be evaluated through the steady-state covariance of the system's state, which depends on the probability with which the task implementing the controller drops its jobs. We show that by making a proper choice for the scheduling algorithm, this probability can be straightforwardly computed as a function of the scheduling parameters. This observation enables the construction of a very efficient procedure for finding the scheduling parameters that attain the optimal tradeoff between the performance of the different control loops. [ABSTRACT FROM AUTHOR]

The phase of the signals backscattered by the ultrahigh frequency radio-frequency identification (UHF-RFID) tags is generally more insensitive to multipath propagation than the received signal strength indicator (RSSI). However, signal phase measurements are inherently ambiguous and could be further affected by the unknown phase offsets added by the transponders. As a result, the localization of an agent by using only signal phase measurements looks infeasible. In this article, it is shown instead that the design of a dynamic position estimator (e.g., a Kalman filter) based only on the signal phase measurement is actually possible. To this end, the necessary conditions to ensure the theoretical local nonlinear observability are first demonstrated. However, a system that is locally observable guarantees the convergence of the localization algorithm only if the actual initial agent position is approximately known a priori. Therefore, the second part of the analysis covers the global observability, which ensures convergence starting from any initial condition in the state space. It is important to emphasize that complete observability holds only in theory. In fact, measurement uncertainty may greatly affect position estimation convergence. The validity of the analysis and the practicality of this localization approach are further confirmed by numerical simulations based on an unscented Kalman filter (UKF). [ABSTRACT FROM AUTHOR]

Indoor localization and position tracking are essential to support applications and services for ambient-assisted living. While the problem of indoor localization is still open and already quite complex per se, in large public places, additional issues of cost, accuracy, and scalability arise. In this paper, the position estimation and tracking technique developed within the project devices for assisted living (DALi) is described, analyzed through simulations, and finally validated by means of a variety of experiments on the field. The goal of the DALi project is to design a robotic wheeled walker guiding people with psychomotor problems. Indeed, people with motor or cognitive impairments are often afraid of moving in large and crowded environments (e.g., because they could lose the sense of direction). In order to mitigate this problem, the position tracking approach described in this paper is based on multisensor data fusion and it is conceived to assure a good tradeoff between target accuracy, level of confidence, and deployment costs. Quite interestingly, the same approach could be used for indoor automated guided vehicles and robotics. [ABSTRACT FROM PUBLISHER]

Industrial networks for distributed monitoring, control, and automation purposes require high-accuracy clock synchronization in topologies including long chains of cascaded nodes. Unfortunately, accuracy typically degrades as the number of devices and the distance from the synchronization reference node (i.e., the master or grandmaster) grows, because of the accumulation of multiple uncertainty contributions. To mitigate this problem, the so-called transparent clocks are used in some synchronization protocols, such as the precision transparent clock protocol used in PROFINET IO isochronous real time networks and the precision time protocol version 2, standardized as IEEE 1588–2008. In this paper, an optimal servo-clock in the mean square sense is proposed. The controller relies on both a Kalman filter that estimates the clock state difference with respect to the master and a static-state feedback assuring mean square stability even under the effect of significant fluctuations of the synchronization period. Several multiparametric simulation results in a case study based on the features of PROFINET IO devices confirm that excellent performance can be achieved with the proposed approach. [ABSTRACT FROM PUBLISHER]

Indoor localization and tracking of moving human targets is a task of recognized importance and difficulty. In this paper, we describe a position measurement technique based on the fusion of various sensor data collected using a wearable embedded platform. Since the accumulated measurement uncertainty affecting inertial data (especially due to the on-board accelerometer) usually makes the measured position values drift away quickly, a heuristic approach is used to keep velocity estimation uncertainty in the order of a few percent. As a result, unlike other solutions proposed in the literature, localization accuracy is good when the wearable platform is worn at the waist. Unbounded uncertainty growth is prevented by injecting the position values collected at a very low rate from the nodes of an external fixed infrastructure (e.g., based on cameras) into an extended Kalman filter. If the adjustment rate is in the order of several seconds and if such corrections are performed only when the user is detected to be in movement, the infrastructure remains idle most of time with evident benefits in terms of scalability. In fact, multiple platforms could work simultaneously in the same environment without saturating the communication channels. [ABSTRACT FROM PUBLISHER]

The increasing diffusion of mobile and portable devices provided with wireless connectivity makes the problem of distance measurement based on radio-frequency technologies increasingly important for the development of next-generation nomadic applications. In this paper, the performance limitations of two classic wireless ranging techniques based on received signal strength (RSS) and two-way time-of-flight (ToF) measurements, respectively, are analyzed and compared in detail. On the basis of this study, a data fusion algorithm is proposed to combine both techniques in order to improve ranging accuracy. The algorithm has been implemented and tested on the field using a dedicated embedded prototype made with commercial off-the-shelf components. Several experimental results prove that the combination of both techniques can significantly reduce measurement uncertainty. The results obtained with the developed prototype are not accurate enough for fine-grained position tracking in Ambient Assisted Living applications. However, the platform can be successfully used for reliable indoor zoning, e.g., for omnidirectional and adjustable hazard proximity detection. Most importantly, the proposed solution is absolutely general, and it is quite simple and light from the computational point of view. Accuracy could be further improved by using a more isotropic antenna and by integrating the ToF measurement technique at the lowest possible level on the same radio chip used for communication. Usually, this feature is not available in typical low-cost short-range wireless modules, e.g., for wireless sensor networks. Thus, the results of this research suggest that combining RSS with ToF measurements could be a viable solution for chip manufacturers interested in adding ranging capabilities to their radio modules. [ABSTRACT FROM PUBLISHER]

In the context of smart grids, Distribution Systems State Estimation (DSSE) is notoriously problematic because of the scarcity of available measurement points and the lack of real-time information on loads. The scarcity of measurement data influences on the effectiveness and applicability of dynamic estimators like the Kalman filters. However, if an Extended Kalman Filter (EKF) resulting from the linearization of the power flow equations is complemented by an ancillary prior least-squares estimation of the weekly active and reactive power injection variations at all buses, significant performance improvements can be achieved. Extensive simulation results obtained assuming to deploy an increasing number of next-generation smart meters and Phasor Measurement Units (PMUs) show that not only the proposed approach is generally more accurate and precise than the classic Weighted Least Squares (WLS) estimator (chosen as a benchmark algorithm), but it is also less sensitive to both the number and the metrological features of the PMUs. Thus, low-uncertainty state estimates can be obtained even though fewer and cheaper measurement devices are used. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed target tracking solution using a team of Unmanned Aerial Vehicles (UAVs) equipped with low-cost visual sensors capable of measuring targets bearing information only. The team of UAVs moves along circular orbits and uses consensus–based distributed Kalman Filtering to identify the position of the target. We show that the centre of the orbit eventually converges to the target position using theoretical arguments and extensive simulation data. By using the same approach, we can solve the rendezvous problem: the team first scans an area in search of a target; as soon as one of the UAVs spots, the other components converge on the target position. [ABSTRACT FROM AUTHOR]

Detecting and tracking the position of multiple targets indoors is a challenging measurement problem due to the inherent difficulty to cluster correctly the sensor data associated to a given target and to track the position of each cluster with adequate accuracy. This problem is critical especially in rooms filled with fixed or moving objects hampering target detection and whenever the paths of different targets cross one another. In this paper, a robust Multiple Targets Tracking (MTT) algorithm exploiting the clouds of points collected from a mmWave-FMCW radar is presented. The proposed solution consists of four main steps. First, the possible outliers of a raw radar data set are removed using a neural network model. Next, the cleaned-up radar data are clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Then, a Kalman Filter (KF) is used to track the position of the centroid of each cluster. Finally, a Structured Branching Multiple Hypothesis Testing (SBMHT) algorithm is applied and updated over reasonably short time intervals to decide which detected tracks are supposed to be confirmed and which ones instead should be discarded. The proposed MTT technique was validated experimentally using the data sets collected from a 60-GHz TI IWR6843 radar platform. The reported results show that the developed algorithm, if properly tuned, is faster and returns more accurate results than other MTT techniques. In particular, the percentage of detection errors is negligible and the planar positioning accuracy is within about 30 cm with 90% probability when up to five targets move freely within the same room. [Display omitted] • A Multiple Targets Tracking (MTT) algorithm exploiting indoor radar data is proposed. • Robust outlier removal and clustering based on a Neural Network and DBSCAN. • A Structured Branching Multiple Hypothesis Testing (SBMHT) algorithm used to confirm/discard tracks. • Planar positioning accuracy (with a 60-GHz radar) is within ±30 cm when 5 people move in the room. • Probability of detection errors and processing latency lower than other MTT algorithms. [ABSTRACT FROM AUTHOR]