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Reliability of any electrical system heavily depends on the accuracy of the model used for representing the integrated device components. The design of renewable power sources and energy management systems using ultracapacitor demands adoption of precise mathematical representation of ultracapacitor dynamics. An appropriate model of ultracapacitor not only allows an accurate assessment of its charge/discharge characteristics but also provides a precise estimation of energy storage and power delivery capabilities. However, due to the lack in the usability of data/information provided by the manufacturer, conventional calculus-based ultracapacitor modeling and energy estimation approach often leads to its inaccurate and inefficient utility. In this regard, this paper has focused on fractional calculus-based analytical formulation for power, energy, and efficiency measurement of ultracapacitor and has deployed an upgraded model of the ultracapacitor, which in turn provides a better estimation of its energy efficiency. The work presented in this paper has been supported by extensive experimentation with the ultracapacitor under different input excitations, considering the dependence of energy storage (state of energy) and power delivery capacity of ultracapacitor on the charge/discharge profile. Furthermore, the hybrid optimization-based analysis recommends the requirement and challenges for the design and development of optimal charging/discharging of ultracapacitors for achieving higher energy efficiency. [ABSTRACT FROM AUTHOR]

Time-of-flight (TOF) measures are valuable in the estimation of displacements, distances, and moving fluids’ speed, e.g., the wind. Of the different types of methods that measure wind speed, the ultrasound is highlighted. As part of the calculation required to acquire the wind speed, one must find the time that an ultrasonic wave takes to travel from one particular place to another. This time is known as TOF. The biggest challenge in measuring TOF is to determine the correct moment when the ultrasonic wave arrived at the reception because it will be immersed in noise and its envelope probably will be corrupted by the actions of the wind. The methodology proposed by this paper presents a combined algorithm between an extended Kalman Filter (EKF) and a zero-crossing detector (ZCD) technique. The EKF method is responsible for the noise elimination stage and estimation of the real ultrasound’s envelope, while the ZCD detects the correct TOF of the estimated signal. This paper also presents the flaws and solutions found for each technique. It was shown that for speeds up to 75 km/h, the coefficient that was found to be $R^{2}\,\,=0.99977$. [ABSTRACT FROM AUTHOR]

This paper presents a modified version of the sine wave histogram test for analog-to-digital converter (ADC) characterization. To accurately estimate the transition levels, the input signal has to fulfill strict conditions, which are coherent sampling and uniform distribution of the phases. Noncoherent sampling of the sine wave leads to systematic errors in the transition level estimation, while the nonuniform distribution of the phases increases the variance of the results using the original sine wave histogram test method. The source of the errors is the improper modeling of the histogram of the measured signal. This paper shows that the true histogram of the measured sine wave can be reconstructed by estimating the phases of the sampled signal. It is shown that integrating this information into the estimation of the transition levels can significantly decrease the effect of noncoherent sampling, thus the ADC can be characterized accurately even if the conditions are not fulfilled. The proposed method is verified on both simulated and real measurement data. [ABSTRACT FROM AUTHOR]

A novel and practical combined weighted (COM-W) method for target localization based on time difference of arrival (TDOA) is proposed in this paper. For 2-D situations with more than three nodes, the main idea of the proposed method is to obtain multiple preliminary localization results corresponding to the different three-node combinations first, and then use the weighted averaging technique based on the Cramér–Rao lower bound (CRLB) to obtain the final estimation. Considering target localization with a set of fixed-positioning nodes employing TDOA measurements as an example, the performance of the proposed method is evaluated by both simulations and acoustic indoor positioning experiments. The experimental results show that the COM-W method is a closed-form and computationally efficient method. It has an excellent positioning performance under both narrow and square geometric arrangements, specifically in the central cross-shaped area and in the vicinity of the nodes. Compared to the three classic weighted linear least squares (WLS)-based positioning methods, the proposed COM-W method is less sensitive to the measurement noise and the NLOS environment. The proposed method has an important application value for the realization of a TDOA-based indoor target positioning system. [ABSTRACT FROM AUTHOR]

As an important part of prognostics and health management, accurate remaining useful life (RUL) prediction for lithium (Li)-ion batteries can provide helpful reference for when to maintain the batteries in advance. This paper presents a novel method to predict the RUL of Li-ion batteries. This method is based on the framework of improved particle learning (PL). The PL framework can prevent particle degeneracy by resampling state particles first with considering the current measurement information and then propagating them. Meanwhile, PL is improved by adjusting the number of particles at each iteration adaptively to reduce the running time of the algorithm, which makes it suitable for online application. Furthermore, the kernel smoothing algorithm is fused into PL to keep the variance of parameter particles invariant during recursive propagation with the battery prediction model. This entire method is referred to as PLKS in this paper. The model can then be updated by the proposed method when new measurements are obtained. Future capacities are iteratively predicted with the updated prediction model until the predefined threshold value is triggered. The RUL is calculated according to these predicted capacities and the predefined threshold value. A series of case studies that demonstrate the proposed method is presented in the experiment. [ABSTRACT FROM AUTHOR]

This paper proposes models for the reference phasor in the context of phasor measurement unit calibration, under conditions of step discontinuities in magnitude and phase, along with methods to estimate the model parameters. Two parametric mathematical models, which are proposed to represent these signals, are fitted to signal samples via an iterative numerical method. The proposed approach does not require any time adjustment of the analysis window to skip the discontinuity. The estimated parameters can be used to calculate the frequency, the magnitude, and the phase during magnitude or phase steps. During a window with the step transient, we propose reference phasors with an appropriate definition. Moreover, we quantify the errors of the phasor-related estimates, for both computer-simulated cases and laboratory measurements. [ABSTRACT FROM AUTHOR]

The Wiener–Hammerstein (W–H) system is a popular and easy to understand class of Volterra nonlinear dynamical system. It consists of a static nonlinearity positioned between two dynamical subsystems. The main identification challenge resides in separating two linear filters as a rational form of poles and zeros. According to previous studies, an initial guess of the separated dynamics is made by browsing through all possible combinations of pole–zero. Then, based on the root-mean-square error (RMSE), good partitions are selected and later all parameters are optimized mutually to determine the best model. As the RMSE before and after optimization behaves erratically, this paper proposes the use of the Spearman correlation to select good models for optimizing. The proposed technique avoids the estimation of the local nonlinearity for each partition and optimizes only few good models. Thus, a massive speedup in processing time is achieved without any prior knowledge about the system. The theoretical verification complies with the simulation study and the measurement analysis. [ABSTRACT FROM AUTHOR]

An electrochemical impedance spectrum (EIS) is considered to be one of the key indicators to monitor the health status of lithium-ion batteries. Experimental procedures to measure the EIS of a battery are offline and require manual intervention. So, in order to monitor the state of health of a battery in real time, online methods for EIS estimation would be very useful. This paper presents an approach for estimation of the EIS of lithium-ion batteries based on a fractional-order equivalent circuit model (FOECM) which can be implemented online. First, the parameters of the fractional-order model are determined using recursive least-squares technique in conjunction with a fractional-order state variable filter based on current and voltage measurements. The parameters obtained are then used to generate the estimated EIS of the battery under different aging conditions. Thereafter, a regression model is obtained based on the estimated EIS spectrum which can represent the degradation trend of the battery in terms of its internal resistance growth. Finally, the obtained regression model is used in the particle filtering framework to predict the remaining useful life (RUL) of the battery quite satisfactorily as compared to the RUL obtained based on the measured EIS data. Moreover, in order to justify the proposed RUL estimation method based on FOECM, comparative analyses with respect to other FOECM-based regression models and an integer order model have also been carried out. [ABSTRACT FROM AUTHOR]

The significance of measurement data transfer over unreliable channel has emerged in the last decade, due to the spread of sensor networks and the idea of Internet of things. This paper investigates the behavior of the fast Fourier transform (FFT)-based power spectral density (PSD) estimation in the case of data loss. There are different methods available to estimate the PSD, but the hegemony of the FFT is beyond dispute, especially in real-time applications. This paper investigates the behavior of the PSD estimator in the case of different data loss models, and then offers some simple solutions on how the data loss can be handled in PSD estimation, when only moderate computing resources are available. The efficiency of the proposed method is demonstrated by the simulation and measurement results. [ABSTRACT FROM AUTHOR]

Phasor measurement units (PMUs) are playing an increasingly important role in wide-area monitoring and the control of power systems. PMUs allow synchronous real-time measurements of voltage, phase angle, and frequency from multiple remote locations in the grid, enabled by their ability to align to global positioning system (GPS) clocks. Given that this ability is vulnerable to GPS spoofing attacks, which have been confirmed easy to launch, in this paper, we propose a distributed real-time wide-area oscillation estimation approach that is robust to GPS spoofing on PMUs and their associated phasor data concentrators. The approach employs the idea of checking update consistency with histories and across distributed nodes and can tolerate up to one third of compromised nodes. It can be implemented in a completely decentralized architecture and in a completely asynchronous way. The effectiveness of the approach is confirmed by numerical simulations of the IEEE 68-bus power system models. [ABSTRACT FROM AUTHOR]

With advances in computing power, stereo vision has become an essential part of navigation applications. However, there may be instances wherein insufficient image data precludes the estimation of navigation parameters. Earlier, a novel vision-based velocity estimation method was developed by the authors, which suffered from the aforementioned drawback. In this paper, the vision-based navigation method has been integrated with a unique low-cost reduced inertial sensor system to bridge the navigation gap using one gyroscope and two accelerometers along with the inputs from wheel speed sensors. The integrated system is based on the extended Kalman filter and was tested on three trajectories with the introduction of vision data gaps. The system showed promising results for autonomous land vehicle applications. [ABSTRACT FROM PUBLISHER]

Missing data are an important issue in large-scale low-cost wireless sensor networks. In these measurement applications, sensor failure, data transmission errors, and data removal for saving transmission power are the main causes for the missing data. This paper studies the impact of missing data on the measurement of the time-variant frequency response func- tion (TV-FRF) of linear time-variant systems. A nonparametric procedure for estimating the missing data and the TV-FRF is proposed. The theory is illustrated by simulations and experiments. [ABSTRACT FROM PUBLISHER]

The navigation performance of the rotational inertial navigation system (RINS) could be greatly improved by rotating the inertial measurement unit with gimbals, and self-calibration for error parameters could be achieved in RINS as well. However, accelerometer nonlinearity errors need to be considered and calibrated to further improve the navigation accuracy of RINS, especially in large dynamic applications. In this paper, a self-calibration method is proposed for accelerometer nonlinearity errors in triaxis RINS. Accelerometer nonlinearity errors and other errors are calibrated through optimal estimation with velocity and position error measurements. In order to guarantee that all errors are observable during calibration, some rotation scheme design principles are proposed, which are different from traditional observability analysis methods and could provide instructions for rotation scheme design directly. The effectiveness of the self-calibration method is proved by both simulation and experiment. The accelerometer nonlinearity errors could be accurately calibrated with the proposed method, while other error parameters reach higher calibration accuracy. Furthermore, experiment results from a long-term vehicle navigation show that velocity and position accuracy of the triaxis RINS have improved significantly after compensation with the self-calibration results, fully illustrating the significance of the proposed self-calibration method in improving the navigation performance of RINS. [ABSTRACT FROM AUTHOR]

This paper proposes an inertial and magnetic sensing-based parallel orientation Kalman filter (KF), which is composed of two separate KFs: one for attitude estimation with an external acceleration model as presented in author’s previous work and the other for heading estimation with a magnetic disturbance model. The novelty of the proposed attitude-heading KF, compared to other model-based KFs, is that the disturbance models are implemented by inserting them into the measurement equations without state-augmentation. In this case, there is no increase in the matrix size of the corresponding KF structure, which is advantageous as the computational cost is approximately halved when compared with the state-augmentation type approaches. The proposed KF has been experimentally verified in magnetically disturbed conditions. The proposed method has advantages over the KF with state-augmentation in the calculation efficiency and ease of use. [ABSTRACT FROM AUTHOR]

Localization of the hazardous gas source plays an important role in the protection of public security, since it can save a lot of time for subsequent rescue works. For gas source localization (GSL), a large number of gas sensor nodes can be rapidly deployed to construct a wireless sensor network (WSN) and cover the whole concerned area. Although least-squares (LS) methods can solve the problem of GSL in WSNs regardless of the distribution of measurement noises, centralized LS methods are not power efficient and robust since they require the gathering and processing of large-scale measurements on a central node. In this paper, we propose a novel distributed method for GSL in WSNs, which is performed on a sequence of sensor nodes successively. Each sensor node in the sequence conducts an individual estimation and a convex combination. The individual estimation is inspired by the LS formulation of the problem of GSL in WSNs. The proposed method is fully distributed and computationally efficient, and it does not rely on the absolute location of the sensor nodes. Extensive simulation results and a set of experimental results demonstrate that the success rate and localization accuracy of the proposed method are generally higher than those of the trust-region-reflective method. [ABSTRACT FROM PUBLISHER]

Due to the external acceleration interference/magnetic disturbance, the inertial/magnetic measurements are usually fused with visual data for drift-free orientation estimation, which plays an important role in a wide variety of applications, ranging from virtual reality, robot, and computer vision to biomotion analysis and navigation. However, in order to perform data fusion, alignment calibration must be performed in advance to determine the difference between the sensor coordinate system and the camera coordinate system. Since orientation estimation performance of the inertial/magnetic sensor unit is immune to the selection of the inertial/magnetic sensor frame original point, we therefore ignore the translational difference by assuming the sensor and camera coordinate systems sharing the same original point and focus on the rotational alignment difference only in this paper. By exploiting the intrinsic restrictions among the coordinate transformations, the rotational alignment calibration problem is formulated by a simplified hand–eye equation $AX=XB$ ( $A$ , $X$ , and $B$ are all rotation matrices). A two-step iterative algorithm is then proposed to solve such simplified hand-eye calibration task. Detailed laboratory validation has been performed and the good experimental results have illustrated the effectiveness of the proposed alignment calibration method. [ABSTRACT FROM PUBLISHER]

In this paper a nonparametric time-domain estimation method of linear time-varying systems from measured noisy data is presented. The challenge with time-varying systems is that the time-varying two-dimensional (2-D) impulse response functions (IRFs) are not uniquely determined from a single set of input and output signals as in the case of linear time-invariant systems. Due to this nonuniqueness, the number of possible solutions is growing quadratically with the number of samples. To decrease the degrees of freedom, user-defined (adjustable) constraints will be imposed. In this particular case, it is imposed that the entire 2-D IRF is smooth. This is implemented by a 2-D kernel-based regularization. This regularization is applied over the system time (the direction of the impulse responses) and simultaneously over the global time (representing the system memory). To illustrate the efficiency of the proposed method, it is demonstrated on a measurement example as a conceptual instrument for measuring and analyzing time-varying systems. [ABSTRACT FROM AUTHOR]

This paper presents an air-gap torque (AGT)-based method for efficiency estimation of induction motors. A new concept of stator resistance that includes the mechanical losses effect is proposed. This new stator resistance is estimated through a particle swarm optimization approach based on the stator flux equations and minimization of torque error at the rated operation point. Then, the obtained stator resistance is used in the AGT equations to estimate the shaft torque and then the efficiency. Moreover, the rotor speed is estimated using induction motor current signature analysis. Thus, the proposed methodology for induction motor efficiency estimation relies only on line currents, line voltages, and nameplate data, being appropriate for in-service applications. Finally, the simulation and experimental results are presented to validate the proposed method at different load conditions. [ABSTRACT FROM PUBLISHER]

An accurate noninvasive estimation of mean arterial pressure (MAP) is of great importance in the evaluation of circulatory function and prognosis of some cardiovascular diseases. This paper proposes a novel oscillometric MAP estimation method based on the dependence of pulse transit time (PTT) on cuff pressure (CP). The PTT computed as the time interval between the electrocardiogram (ECG) R-peaks and the maximum slope points on the oscillometric pulses is mathematically modeled by considering the cuff-arm-artery system and the blood flow dynamics. It is then analytically shown that MAP can be approximated as the CP at which the PTT is maximum. Based on our theoretical findings, a new method of MAP estimation from simultaneous ECG and oscillometric blood pressure measurements is proposed. Our proposed method is validated with a pilot study in which 150 recordings from 10 subjects are analyzed. The reference MAP is computed from the systolic and diastolic pressures measured by the Food and Drug Administration-approved Omron HEM-790IT monitor using three different formulas given in the literature. The performance of our proposed method is compared with the maximum amplitude and zero-crossing methods in terms of mean error (ME), mean absolute error, and standard deviation of error (SDE). It is found that our proposed method achieves improvements of more than 20% in SDE compared with the maximum amplitude method and more than 50% in ME compared with the zero-crossing method. [ABSTRACT FROM PUBLISHER]

This paper derives a novel initial alignment method for the strapdown inertial navigation system (SINS), which transforms the attitude alignment into an attitude estimation problem. The process model of the proposed initial alignment method by attitude estimation is established by decomposition of the attitude matrix. The measurement model is constructed based on a generalized velocity integration formula that can integrate the inertial measurements over certain fixed time intervals. The contributions of the work presented here are twofold. First, the attitude estimation-based structure enables the proposed method to estimate the gyroscope biases other than the attitude quaternion, which is celebrated for the low-cost SINS. The second is the application of the proposed generalized velocity integration formula to attenuate the accumulated errors in vector observations caused by the traditional velocity integration formula. Experimental road tests are performed with a low-cost SINS, which validate the efficacy of the proposed method. [ABSTRACT FROM PUBLISHER]

Accurate and precise navigation solution can be obtained by integrating multiple sensors such as global navigation satellite system (GNSS), vision sensor, and inertial navigation system (INS). However, accuracy of position solutions under GNSS-challenged environment occasionally degrades due to poor distributions of GNSS satellites and feature points from vision sensors. This paper proposes a selective integration method, which improves positioning accuracy under GNSS-challenged environments when applied to the multiple navigation sensors such as GNSS, a vision sensor, and INS. A performance index is introduced to recognize poor environments where navigation errors increase when measurements are added. The weighted least squares method was applied to derive the performance index, which measures the goodness of geometrical distributions of the satellites and feature points. It was also used to predict the position errors and the effects of the integration, and as a criterion to select the navigation sensors to be integrated. The feasibility of the proposed method was verified through a simulation and an experimental test. The performance index was examined by checking its correlation with the positional error covariance, and the performance of the selective navigation was verified by comparing its solution with the reference position. The results show that the selective integration of multiple sensors improves the positioning accuracy compared with nonselective integration when applied under GNSS-challenged environments. It is especially effective when satellites and feature points are posed in certain directions and have poor geometry. [ABSTRACT FROM PUBLISHER]

Oscillometry is the most common measurement method used in electronic blood pressure (BP) monitors. However, most of the existing oscillometric algorithms employ empirical ratios on the oscillometric waveform envelope (OMWE) to determine the systolic pressure (SP) and diastolic pressure (DP). As these algorithms do not consider the cardiovascular system parameters that may vary due to health conditions or age, the pressure estimates are not always reliable. In this paper, we develop a new mathematical model for the OMWE by incorporating an existing model of the cuff-arm-artery system. [ABSTRACT FROM PUBLISHER]