Many techniques for robot localization rely on the assumption that both process and measurement noises are uncorrelated, white, and normally distributed. However, if this assumption does not hold, these techniques are no longer optimal and, in addition, the maximum estimation errors can be hardly kept under control. In this paper, this problem is addressed by means of a tailored extended H $_\infty$ filter (EHF) fusing odometry and gyroscope data with position and heading measurements based on quick response (QR) code landmark recognition. In particular, it is shown that, by properly tuning EHF parameters and by using an adaptive mechanism to avoid finite escape time phenomena, it is possible to achieve a higher localization accuracy than using other dynamic estimators, even if QR codes are detected sporadically. Also, the proposed approach ensures a good tradeoff in terms of computational burden, convergence time, and deployment complexity.

Synchrophasor estimation accuracy is a well-known critical issue in systems for smart grid monitoring and control. This paper deals with an in-depth analysis of the effect of both steady-state and dynamic disturbances on single-cycle and multicycle windowed discrete Fourier transform (DFT)-based synchrophasor estimators. Unlike other qualitative or simulation-based results found in the literature, this work provides two accurate and easy-to-use analytical expressions that can be used to determine the worst case range of variation of the total vector error (TVE) due to off-nominal frequency deviations. In such conditions, estimation accuracy is limited by two factors, i.e., the infiltration caused by the input signal image frequency and the scalloping loss associated with the spectrum main lobe of the chosen window. Starting from the aforementioned general analysis, a new two-term window minimizing the detrimental effects of image frequency tone is proposed. The accuracy of the related DFT-based synchrophasor estimator is evaluated under both static and dynamic conditions, which is the most interesting scenario for future smart grids. Moreover, the effect of waveform frequency measurement uncertainty on scalloping loss compensation is quantified. Several simulation results (including the effects of noise, harmonic distortion, and amplitude and phase modulation) confirm that the proposed window can significantly improve the accuracy achievable with a simple single-cycle DFT estimator. Indeed, TVE values much smaller than 1% can be achieved even in the worst case conditions reported in the standard IEEE C37.118.1-2011, when the frequency waveform deviations are within ±4% of the nominal value. In addition, the proposed solution could be useful to improve the performance of more complex dynamic phasor estimators, e.g., those in which the first- and second-order terms of the phasor Taylor series expansion result from the differences of consecutive DFT-based phasor estimates.

Next-generation phasor measurement units (PMUs) are expected to play a key role for monitoring the behavior of future smart grids. While most of the PMUs used nowadays in transmission networks rely on static phasor models, more sophisticated representations and stricter accuracy requirements are needed to track amplitude, phase, and frequency changes of power waveforms in strongly dynamic scenarios as those expected in future distribution systems. In this paper, a discrete Fourier transform (DFT)-based algorithm based on a dynamic phasor model (referred to as interpolated dynamic DFT-based synchrophasor estimator) is used to estimate not only amplitude and phase of the collected waveforms, but also their frequency and rate of change of frequency. The performances of the proposed method are evaluated through multiple simulations in different steady-state and transient conditions described in the Standard IEEE C37.118.1-2011.

This paper presents the application of the Synthetic Aperture Radar (SAR) localization method for indoor positioning of UHF-RFID tags when the robot-mounted reader antenna moves along multiple trajectories. By properly combining the phase data associated to a set of multiple paths, the whole length of the combined synthetic apertures enlarges and then the localization accuracy may improve. Besides, during consecutive inventory rounds, several tag position estimates are available and they can be profitably combined to minimize the localization uncertainty. Different combination approaches are investigated to determine the best choice to improve the localization performance. The method capabilities are discussed through a numerical analysis, by considering different configurations of the multiple apertures and different measurement uncertainty sources. Finally, the proposed localization method is validated through an experimental analysis carried out with commercial RFID hardware and a robotic wheeled walker, in an indoor scenario, by employing different types of tags. The knowledge of the reader/robot trajectory required by the SAR method is here achieved with an optical system.

The real-time high-accuracy measurement of waveform phasors is one of the many open challenges that need to be addressed in future smart grids. In this paper, the accuracy of four recently proposed synchrophasor estimators is analyzed and compared with the well-known one-cycle discrete Fourier transform estimator under the effect of static frequency offsets, amplitude modulation, phase modulation, harmonic distortion, and wideband noise. Two of the considered techniques track the phasor variations through finite-difference equations that estimate the first- and second-order derivatives of the phasor itself. The other two methods are instead based on a least squares estimation of the coefficients of the phasor Taylor's series expansion. The analysis reported in this paper covers the main scenarios described in the Standard IEEE C37.118.1-2011. In particular, the influence of different signal parameters on the total vector error (TVE) values is quantified and used to determine the maximum TVE increments associated with distinct parameters and the corresponding upper bounds.

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.