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Deadbeat control (DBC) is developed based on the system model, which is commonly used to achieve good control performance for a three-phase grid connected converter (GCC). Unbalanced grid voltage is a common phenomenon in power systems. In this study, an improved DBC based on current predictive correction (CPC_DBC) is used to control GCC under an unbalanced grid voltage. The main purposes of CPC_DBC are to reduce the adverse effects due to random sampling error to improve current control performance, and to enhance the interference rejection capability of the control system. Then, the characteristics of CPC_DBC and the conventional DBC (Con_DBC) are analyzed by transfer functions in the discrete time domain. Several common control objectives under an unbalanced grid voltage are used to demonstrate the superiority of CPC_DBC. Finally, the experimental results indicate that CPC_DBC can effectively reduce the adverse effects of random sampling error; the robustness of system due to the uncertain value of the filter inductance is improved as well. [ABSTRACT FROM PUBLISHER]

The knowledge of the number of tags is critical in many radio frequency identification (RFID) applications. This paper is concerned with the problem of estimating an RFID tag population size when the number of tags is much higher than the frame size. A novel estimation scheme called “Scalable Minimum Mean Square Error” (sMMSE) is proposed. The proposed estimator updates the frame size by considering two key parameters: One determines the limit of the slots occupancy at which the frame size should be increased, and the other one sets the frame size increase factor. A formal study is provided to adjust these parameters with the aim of lowering the estimation error while scaling to highly populated tag sets. Numerical results indicate that sMMSE significantly decreases the normalized estimation error and maintains a low estimation time compared to existing strategies. [ABSTRACT FROM AUTHOR]

Tightly coupled GNSS/INS has been widely approved as a promising substitute for standalone GNSS in urban areas navigation. However, due to the frequent GNSS signal outages, the filter used in GNSS/INS should be insensitive to the less informative observations. In this paper, a novel sigma-points update method is proposed to enhance the robustness of cubature Kalman filter (CKF) under the circumstance of unavailable observations. First, the problems of existing sampling-based filters are analyzed. Then, by transforming the posterior sigma-points error matrix from prediction phase of filtering to the posterior domain of update, the updated sigma-points are expected to capture the covariance more precisely than traditional sigma-points. Finally, an improved CKF (ICKF) is developed by embedding these points into the Bayesian estimation framework, and the upper bounds of error covariance matrices are analyzed theoretically. Signal outages with different durations are simulated and results demonstrate that ICKF outperforms state-of-the-art methods. [ABSTRACT FROM PUBLISHER]

The problem of modulating the value of a parameter onto a band-limited signal to be transmitted over a continuous-time, additive white Gaussian noise (AWGN) channel, and then estimating this parameter at the receiver, is considered. The performance is measured by the mean power- \alpha error (MP \alpha \text{E} ), which is defined as the worst case \alpha th-order moment of the absolute estimation error. The optimal exponential decay rate of the MP \alpha \textE as a function of the transmission time is investigated. Two upper (converse) bounds on the MP \alpha \textE exponent are derived, on the basis of known bounds for the AWGN channel of inputs with unlimited bandwidth. The bounds are computed for typical values of the error moment and the signal-to-noise ratio (SNR), and the SNR asymptotics of the different bounds are analyzed. The new bounds are compared with known converse and achievability bounds, which were derived from channel coding considerations. [ABSTRACT FROM AUTHOR]

We study the problem of identifying multiple rumor or infection sources in a network under the susceptible-infected model, and where these sources may start infection spreading at different times. We introduce the notion of an abstract estimator that, given the infection graph, assigns a higher value to each vertex in the graph it considers more likely to be a rumor source. This includes several of the single-source estimators developed in the literature. We introduce the concepts of a quasi-regular tree and a heavy center, which allows us to develop an algorithmic framework that transforms an abstract estimator into a two-source joint estimator, in which the infection graph can be thought of as covered by overlapping infection regions. We show that our algorithm converges to a local optimum of the estimation function if the underlying network is a quasi-regular tree. We further extend our algorithm to more than two sources, and heuristically to general graphs. Simulation results on both synthetic and real-world networks suggest that our algorithmic framework outperforms several existing multiple-source estimators, which typically assume that all sources start infection spreading at the same time. [ABSTRACT FROM PUBLISHER]

In this paper, we study the level set estimation of a spatial-temporally correlated random field by using a small number of spatially distributed sensors. The level sets of a random field are defined as regions where data values exceed a certain threshold. The identification of the boundaries of such sets is an important theoretical problem with a wide range of applications such as spectrum sensing, urban sensing, and environmental monitoring, etc. We propose a new active sparse sensing and inference scheme, which can achieve rapid and accurate extraction of level sets in a large random field by using a small number of data samples strategically and sparsely selected from the field. A Gaussian process (GP) prior model is used to capture the spatial–temporal correlations inherent in the random field. It is first shown that the optimal level set estimation can be achieved by performing a GP regression with all data samples and then thresholding the regression results. We then investigate the active sparse sensing scheme, where a central controller dynamically selects a small number of sensing locations according to the information revealed from past measurements, with the objective to minimize the expected level set estimation error probability. The expected estimation error probability is explicitly expressed as a function of the selected sensing locations, and the results are used to formulate the optimal sensing location selection problem as a combinatorial problem. Two low complexity greedy algorithms are developed by using analytical upper bounds of the expected estimation error probability. Both simulation and experiment results demonstrate that the greedy algorithms can achieve significant performance gains over baseline passive sensing algorithms and the GP upper confidence bound level set estimation algorithm. [ABSTRACT FROM PUBLISHER]

Switched systems theory is used to analyze the stability of image-based observers for three-dimensional localization of objects in a scene in the presence of intermittent measurements due to occlusions, feature tracking losses, or a limited camera field of view, for example. Generally, observers or filters that are exponentially stable under persistent measurement availability may have unbounded error growth under intermittent measurement loss, even while providing seemingly accurate state estimates. By constructing a framework that utilizes a state predictor during periods when measurements are not available, a class of image-based observers is shown to be exponentially convergent in the presence of intermittent measurements if an average dwell time, and a total unmeasurability time, condition is satisfied. The conditions are developed in a general form, applicable to any observer that is exponentially convergent assuming persistent visibility, and utilizes object motion knowledge to reduce the amount of time measurements must be available to maintain convergence guarantees. Based on the stability results, simulations are provided to show improved performance compared to a zero-order hold approach, where state estimates are held constant when measurements are not available. Experimental results are also included to verify the theoretical results, to demonstrate applicability of the developed observer and predictor design, and to compare against a typical approach using an extended Kalman filter. [ABSTRACT FROM PUBLISHER]

We consider deterministic parameter estimation and the situation where the probability density function (p.d.f.) parameterized by unknown deterministic parameters results from the marginalization of a joint p.d.f. depending on random variables as well. Unfortunately, in the general case, this marginalization is mathematically intractable, which prevents from using the known standard deterministic lower bounds (LBs) on the mean squared error (MSE). Actually the general case can be tackled by embedding the initial observation space in a hybrid one where any standard LB can be transformed into a modified one fitted to nonstandard deterministic estimation, at the expense of tightness however. Furthermore, these modified LBs (MLBs) appears to include the submatrix of hybrid LBs which is an LB for the deterministic parameters. Moreover, since in the nonstandard estimation, maximum likelihood estimators (MLEs) can be no longer derived, suboptimal nonstandard MLEs (NSMLEs) are proposed as being a substitute. We show that any standard LB on the MSE of MLEs has a nonstandard version lower bounding the MSE of NSMLEs. We provide an analysis of the relative performance of the NSMLEs, as well as a comparison with the MLBs for a large class of estimation problems. Last, the general approach introduced is exemplified, among other things, with a new look at the well-known Gaussian complex observation models. [ABSTRACT FROM PUBLISHER]

The disturbance voltage of voltage-source inverter operation exerts significant influences on the accuracy of motor parameter identification. In this paper, an accurate model is established for estimating the disturbance voltage that includes current harmonics and their derivative. The conclusion is drawn that the derivative of the d -axis current harmonics plays a major role in causing estimation error of the disturbance voltage. Based on this, two methods are proposed to reduce the estimation error of the disturbance voltage. One employs an iterative learning controller in the d-axis current control loop to suppress the harmonics of d-axis current, thus reducing their influence on the estimation of the disturbance voltage. The second estimates the disturbance voltage on the basis of the average value of the variables in q-axis equation of the motor, so that the estimation result is immune to current harmonics. Motor parameter identification accuracy is consequently improved by both. [ABSTRACT FROM AUTHOR]

Approximate adders are widely being advocated as a means to achieve performance gain in error resilient applications. In this paper, a generic methodology for analytical modeling of probability of occurrence of error and the Probability Mass Function (PMF) of error value in a selected class of approximate adders is presented, which can serve as performance metrics for the comparative analysis of various adders and their configurations. The proposed model is applicable to approximate adders that comprise of sub-adder units of uniform as well as non-uniform lengths. Using a systematic methodology, we derive closed form expressions for the probability of error for a number of state-of-the-art high-performance approximate adders. The probabilistic analysis is carried out for arbitrary input distributions. It can be used to study the dependence of error statistics in an adder’s output on its configuration and input distribution. Moreover, it is shown that by building upon the proposed error model, we can estimate the probability of error in circuits with multiple approximate adders. We also demonstrate that, using the proposed analysis, the comparative performance of different approximate adders can be correctly predicted in practical applications of image processing. [ABSTRACT FROM AUTHOR]

To obtain accurate estimation of the channel parameters for a fluctuant multipath channel, a novel channel estimation algorithm based on iteration is proposed. By compensating estimation error of the fast correlation estimation algorithm, an improved accurate estimation for channel parameters is realized. Theoretical conclusions of the relation between compensation times and estimation performance are presented. Simulations results show that the proposed algorithm can obtain a small estimation error, low computational complexity, and anti-multipath distortion. It can estimate the channel parameters efficiently in low signal-noise-ratios situations, and improve the performance of the fast correlation channel estimation algorithm greatly in high signal-noise-ratios situations. [ABSTRACT FROM AUTHOR]

This paper presents an equivalent winding model of a small and medium permanent-magnet synchronous motor. The model considers fill factor and void ratio. To verify the model, two winding samples are made. One is annular and adopts ordinary enameled wires. The other is circle but adopts self-adhesion wires. Temperature rises of the winding samples supplied with different current densities are tested. The calculated and tested results were compared. The equivalent winding model is also used for the temperature calculation of a 6-pole/9-slot permanent magnet synchronous motor. The influence of fill factor and void ratio on the temperature rise of the motor is investigated, as well as two overload short time states are compared. Comparing with ignoring the effect of the void ratio, the winding temperature estimation error reduces about 5 °C. Finally, winding temperature rises of a prototype are tested. The results can provide some references for choosing a reasonable current density and improving the accuracy of motor temperature estimation. [ABSTRACT FROM PUBLISHER]

We address multiple-input multiple-output (MIMO) communication employing spatial modulation (SM) with dual-polarized (DP) antennas. The proposed architecture adds the polarization dimension to the conventional SM mappings and offers performances, which are comparable to or under certain conditions even better than those of the uni-polarized systems while occupying half as much space. We consider the generalized spatially correlated Rayleigh and Rician fading channel models and present an average bit-error probability upper bounding framework for the proposed DP SM-MIMO system. The theoretical error analysis is also extended to the case where the channel coefficients are estimated with Gaussian estimation errors. This upper bounding method is also used to determine the conditions in which the dual-polarized SM is better than equivalent systems with uni-polarized antennas. Theoretical derivations are also validated by extensive simulations, both corroborating that SM combined with dual-polarization forms an attractive alternative not only for its improved multiplexing gains and space efficiency but also for performance gains over correlated channels. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of persistently covering an environment with a group of mobile robots. In contrast to traditional coverage, in our scenario the coverage level of the environment is always changing. For this reason, the robots have to continually move to maintain a desired coverage level. In this context, our contribution is a complete approach to the problem, including distributed estimation of the coverage and control of the motion of the robots. First, we present an algorithm that allows every robot to estimate the global coverage function only with local information. We pay special attention to the characterization of the algorithm, establishing bounds on the estimation error, and we demonstrate that the algorithm guarantees a perfect estimation in particular areas. Second, we introduce a new function to determine the possible improvement of the coverage at each point of the environment. Upon this metric, we build a motion control strategy that drives the robots to the points of the highest improvement while following the direction of the gradient of the function. Finally, we simulate the proposal to test its correctness and performance. [ABSTRACT FROM AUTHOR]

High frequency (HF) signal injection is an effective sensorless control scheme for interior permanent-magnet synchronous motor (IPMSM) drives to achieve low and zero speed operation. However, the audible noise produced by the injected HF signal is often very shrill and harsh to hear, which restricts the actual application. In order to reduce the audible noise, a novel pseudo-random HF square-wave voltage injection scheme is proposed in this paper. The HF voltages with two different frequencies are randomly injected into the estimated rotor reference frame cycle by cycle, and a corresponding signal demodulation method for extracting the rotor position information is presented. Based on the principle analysis of this random frequency injection scheme, the digital time-delay effect in HF signal is considered and a compensation method for signal demodulation is proposed, which is effective in reducing position estimation error. Then, the power spectra density (PSD) in fixed frequency and pseudo-random frequency injection schemes are compared both theoretically and experimentally. The distribution of HF voltage and current PSD is extended by using the proposed injection scheme. Finally, this sensorless control method is verified by simulation and experiment on a 2.2-kW IPMSM drive platform. [ABSTRACT FROM AUTHOR]

In most of compressed sensing (CS) contributions, the sparsity of interesting signals is usually assumed to be prior. It may incur performance degradation of CS in such environments, where the sparsity order of channel occupancy changes dynamically. Therefore, sparsity order estimation (SOE) is vital, so that the sampling rate can be adjusted adaptively to exploit fully CS techniques. This paper studies the SOE problem for wideband cognitive radio. To investigate this issue, at first, the theoretical expression of sparsity order of the considered signals is derived via random matrix theory. The theoretical analysis shows that the sparsity order can be expressed with the ratio, which consists of the trace of measured signals’ covariance matrix, compression sampling ratio, signal-to-noise ratio, and so on. And then, according to the theoretical expressions, a novel SOE approach is presented. In the proposed method, the trace of measured signals’ covariance matrix is first calculated, and second, the sparsity order is estimated based on the trace. Compared with the previous work, the proposed algorithm, besides the lower computational complexity, has some superior performance, such as better robustness, smaller SOE error, and so on. Theoretical analysis and simulation results demonstrate the performance of the proposed approach. [ABSTRACT FROM PUBLISHER]

Tag population estimation has recently attracted significant research attention due to its paramount importance on a variety of radio-frequency identification (RFID) applications. However, most, if not all, of the existing estimation mechanisms are proposed for the static case where tag population remains constant during the estimation process, thus leaving the more challenging dynamic case unaddressed, despite the fundamental importance of the latter case on both the theoretical analysis and the practical application. In order to bridge this gap, we devote this paper to designing a generic framework of stable and accurate tag population estimation schemes based on the Kalman filter for both the static and dynamic RFID systems. Technically, we first model the dynamics of RFID systems as discrete stochastic processes and leverage the techniques in the extended Kalman filter and cumulative sum control chart to estimate tag population for both the static and dynamic systems. By employing the Lyapunov drift analysis, we mathematically characterize the performance of the proposed framework in terms of estimation accuracy and convergence speed by deriving the closed-form conditions on the design parameters under which our scheme can stabilize around the real population size with bounded relative estimation error that tends to zero with exponential convergence rate. [ABSTRACT FROM PUBLISHER]

In this paper, we study the secure multiantenna transmission with artificial noise (AN) under imperfect channel state information (CSI) in the presence of spatially randomly distributed eavesdroppers. We derive the optimal solutions of the power allocation between the information signal and the AN for minimizing the secrecy outage probability (SOP) under a target secrecy rate and for maximizing the secrecy rate under an SOP constraint, respectively. Moreover, we provide an interesting insight that channel estimation error affects the optimal power allocation strategy in opposite ways for the two given objectives. When the estimation error increases, more power should be allocated to the information signal if we aim to decrease the rate-constrained SOP, whereas more power should be allocated to the AN if we aim to increase the SOP-constrained secrecy rate. [ABSTRACT FROM PUBLISHER]

We investigate a localization problem using time-difference-of-arrival measurements with unknown and bounded measurement errors. Different from most existing algorithms, we consider the minimization of the worst-case position estimation error to improve the robustness of the algorithm. The localization problem is formulated as a nonconvex optimization problem. We adopt semidefinite relaxation to relax the original problem into a convex optimization problem, which can be solved using existing semidefinite program solvers. Simulation results show that our proposed algorithm has lower worst-case position estimation error than other existing algorithms. [ABSTRACT FROM PUBLISHER]

Position and orientation system (POS) is widely applied to the various mapping sensors for airborne direct georeferencing. As the accuracy requirements vary in different mapping applications, the POS error analysis is important for the system design. In this paper, an analytical approach to the POS error analysis for ground alignment and constant-velocity flight is proposed. First, the simplified system model is obtained for the error analysis. Then, by applying the two-stage Kalman filter, the analytical relations between the attitude errors and estimation errors of the biases of the inertial sensors are derived. Finally, the experiment is carried out to confirm the effectiveness of the proposed method. The proposed method is not only effective and straightforward but also provides us some new insights into the POS error characteristics. [ABSTRACT FROM AUTHOR]

To localize the wireless sensor networks nodes, only the hop-based information (i.e., hops’ number, average hop size, and so on) has been, so far, exploited by range-free techniques, with poor-accuracy, however. In this paper, we show that localization accuracy may greatly benefit from joint exploitation, at no cost, of the information already provided by the forwarding nodes (i.e., relays) between each anchor (i.e., position aware) and sensor nodes pair. As such, we develop a novel range-free localization algorithm, derive its average location estimation error (LEE) in closed-form, and compare it in LEE performance with the best representative algorithms in the literature. We show that the proposed algorithm outperforms them in accuracy. In contrast to the latter, we further prove that it is able to achieve an LEE average and variance of about 0 when the number of sensors is large enough, thereby achieving an unprecedented accuracy performance among range-free techniques. [ABSTRACT FROM PUBLISHER]

With new generations of high-resolution imaging radars, the orientation of vehicles can be estimated without temporal filtering. This enables time-critical systems to respond even faster. Based on a large data set, this paper compares three generic algorithms for the orientation estimation of a vehicle. An experimental MIMO imaging radar is used to highlight the requirements of a robust algorithm. The well-known orientated bounding box and the so-called L-fit are adapted for radar measurements and compared with a brute-force approach. A quality function selects the best fitted model and is a key factor to minimize alignment errors. Moreover, the reliability of the estimation is evaluated with respect to the aspect angle, the distance to the target, and the number of sensors. An approach to estimate the reliability of the current orientation estimation is introduced. It is shown that the root mean square error of the orientation estimation is 9.77° and 38% smaller compared with the common algorithm. In 50% of the evaluated measurements the orientation estimation error is smaller than 3.73°. [ABSTRACT FROM AUTHOR]

In the direct sequence spread spectrum (DSSS) system, multiple-access interference mitigation is critical to improving the acquisition performance. Therefore, in this paper, a fast cross-correlation mitigation algorithm based on the minimum mean-square error criterion is proposed. The algorithm exploits the matched filter outputs; hence, it is applicable to existing DSSS receivers based on direct pseudorandom noise code correlation. Essentially, the proposed scheme is an adaptive filter that optimizes each individual uncertain code phase delay. The algorithm employs both the code sequence information of the desired signal and that of the multiple-access interference signal, which enables it to outperform the standard matched filter, conventional least-mean-squares algorithm, and the pulse compression repair method. Furthermore, the proposed algorithm decreases the computational load compared with the existing multistatic adaptive pulse compression method by reducing the stage-number of the adaptive filter; this results in a slight degradation in performance. Numerical results verify the validity of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

This paper provides new error bounds on consistent reconstruction methods for signals observed from quantized random projections. Those signal estimation techniques guarantee a perfect matching between the available quantized data and a new observation of the estimated signal under the same sensing model. Focusing on dithered uniform scalar quantization of resolution \delta >0 , we prove first that, given a Gaussian random frame of \mathbb R^{N} with M vectors, the worst-case \ell 2 -error of consistent signal reconstruction decays with high probability as O(({N}/{M})\log ({M}/{\sqrt {N}})) uniformly for all signals of the unit ball \mathbb B^N \subset \mathbb R^N . Up to a log factor, this matches a known lower bound in \Omega (N/M) and former empirical validations in O(N/M) . Equivalently, if M exceeds a minimal number of frame coefficients growing like , any vectors in \mathbb B^{N} with M identical quantized projections are at most \epsilon 0 apart with high probability. Second, in the context of quantized compressed sensing with M Gaussian random measurements and under the same scalar quantization scheme, consistent reconstructions of K -sparse signals of \mathbb R^{N} have a worst case error that decreases with high probability as O(({K})/({M})\log ({MN})/({\sqrt {K}^{3}}))$ uniformly for all such signals. Finally, we show that the proximity of vectors whose quantized random projections are only approximately consistent can still be bounded with high probability. A certain level of corruption is thus allowed in the quantization process, up to the appearance of a systematic bias in the reconstruction error of (almost) consistent signal estimates. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel method of estimating the Fourier transform (FT) of deterministic, continuous-time signals, from a finite number N of their samples taken from a fixed-length observation window. It uses alias-free hybrid-stratified sampling to probe the processed signal at a mixture of deterministic and random time instants. The FT estimator, specifically designed to work with this sampling scheme, is unbiased, consistent and fast converging. It is shown that if the processed signal has continuous third derivative, then the estimator’s rate of uniform convergence in mean square is N^-5. Therefore, in terms of frequency-independent upper bounds on the FT estimation error, the proposed approach significantly outperforms existing estimators that utilize alias-free sampling, such as total random, stratified sampling, and antithetical stratified whose rate of uniform convergence is N^-1. It is proven here that N^-1 is a guaranteed minimum rate for all stratified-sampling-based estimators satisfying four weak conditions formulated in this paper. Owing to the alias-free nature of the sampling scheme, no constraints are imposed on the spectral support of the processed signal or the frequency ranges for which the Fourier Transform is estimated. [ABSTRACT FROM AUTHOR]

The Earth Radiation Budget Experiment wide-field-of-view (WFOV) radiometers aboard the Earth Radiation Budget Satellite (ERBS) provided a 15-year record of high-quality measurements for research into the radiant energy balance of the Earth. Monthly mean maps of RSR and outgoing longwave radiation (OLR) are primary data products from these measurements. The ERBS orbit had an inclination of 57° so as to precess through all local times every 72 days. Because of limited temporal sampling, some regions were not measured sufficiently often by the WFOV radiometers to produce accurate radiation flux values for these maps. The temporal sampling of any one region is very irregular; therefore, it is necessary to consider each region in detail for each month. An analysis of the errors, which result from computing the average value of the albedo of a region over a day or month based on limited sampling, is presented. It is necessary to take into account synoptic variations and their time correlations and differences of the regions' diurnal cycle from that assumed by the time-averaging algorithms. An expression is derived for the variance of the error of the computed daily and monthly mean albedo. Temporal correlation and variability of the albedo field are specified a priori. This analysis has been used for quality assurance to evaluate the temporal sampling errors of monthly mean RSR maps computed from the measurements by the WFOV radiometers aboard the ERBS and to delete those values for which the error variance is excessive. [ABSTRACT FROM AUTHOR]

This paper presents the decentralized sampled-data fuzzy observer design techniques for nonlinear interconnected systems, which are assumed to be composed of fuzzy subsystems and unknown interconnections. To design the decentralized fuzzy observer, the estimation error dynamics is obtained, and the performance function is defined. Based on the estimation error dynamics and the performance function, the continuous-time decentralized fuzzy observer is first proposed to minimize the interconnection bound to attenuation degree ratio. In addition, the decentralized sampled-data fuzzy observers are designed by using the approximate discretization and the exact discrete-time design approaches, respectively. Each proposed observer design technique is formulated into the optimal problems with the linear matrix inequalities. Finally, several simulation examples show the validity and superiority of the proposed observer design techniques by comparing with the conventional techniques. [ABSTRACT FROM PUBLISHER]

This paper presents a wearable sensor approach to motion measurements of human lower limbs, in which subjects perform specified walking trials at self-administered speeds so that their level walking and stair ascent capacity can be effectively evaluated. After an initial sensor alignment with the reduced error, quaternion is used to represent 3-D orientation and an optimized gradient descent algorithm is deployed to calculate the quaternion derivative. Sensors on the shank offer additional information to accurately determine the instances of both swing and stance phases. The Denavit-Hartenberg convention is used to set up the kinematic chains when the foot stays stationary on the ground, producing state constraints to minimize the estimation error of knee position. The reliability of this system, from the measurement point of view, has been validated by means of the results obtained from a commercial motion tracking system, namely, Vicon, on healthy subjects. The step size error and the position estimation accuracy change are studied. The experimental results demonstrated that the extensively existed sensor misplacement and sensor drift problems can be well solved. The proposed self-contained and environment-independent system is capable of providing consistent tracking of human lower limbs without significant drift. [ABSTRACT FROM AUTHOR]

This paper concerns the uplink of multicell multiuser multiple-input multiple-output (MIMO) systems. To tackle the effect of pilot contamination that is generally viewed as a bottleneck in previous work, this paper presents a superimposed pilot (SP)-aided uplink channel estimation scheme and mathematically characterizes the impact that an SP has on the performance of such a very large MIMO system. It is shown that there are two types of interference components that do not vanish, even when the number of antennas $M$ grows to infinity. The first type, which is referred to as cross-contamination, is due to the correlation between the SP and data among different cells. The second type, which is referred to as self-contamination, is due to the dependence between channel estimation and estimation error. Cross-contamination is, in principle, similar to pilot contamination in a conventional pilot-based multicell MIMO system, whereas self-contamination is unique for the SP-aided scheme. Both theoretical and simulation results demonstrate that the SP-aided scheme can effectively reduce the estimation contamination by increasing the data frame size and, in turn, achieve a significant improvement over the spectral efficiency, in comparison with conventional pilot-based methods. [ABSTRACT FROM AUTHOR]

Contactless smart card systems have gained universal prevalence in modern metros. In addition to its original goal of ticketing, the large amount of transaction data collected by the smart card system can be utilized for many operational and management purposes. This paper investigates an important problem: how to extract spatiotemporal segmentation information of trips inside a metro system. More specifically, for a given trip, we want to answer several key questions: How long does it take for a passenger to walk from the station gantry to the station platform? How much time does he/she wait for the next train? How long does he/she spend on the train? How long does it take to transfer from one line to another? This segmentation information is important for many application scenarios such as travel time prediction, travel planning, and transportation scheduling. However, in reality, we only assume that only each trip's tap-in and tap-out time can be directly obtained; all other temporal endpoints of segments are unknown. This makes the research very challenging. To the best of our knowledge, we are the first to give a practical solution to this important problem. By analyzing the tap-in/tap-out event pattern, our intuition is to pinpoint some special passengers whose transaction data can be very helpful for segmentation. A novel methodology is proposed to extract spatiotemporal segmentation information: first, for nontransfer trips, by deriving the boarding time between the gantry and the platform, and then, for with-transfer trips, by deriving the transfer time. Evaluation studies are based on large-scale real-system data of the Shenzhen metro system, which is one of the largest metro systems in China and serves millions of passengers daily. Onsite investigations validate that our algorithm is accurate and that the average estimation error is only around 15%. [ABSTRACT FROM AUTHOR]

An electromagnetic wave propagation problem can be formulated according to a pair of complementary formulations, called the e -formulation and the h -formulation. The two formulations are linked to each other by Maxwell’s curl equations, and, in the continuous setting, they are perfectly equivalent in describing the wave propagation phenomenon. However, this is not true in the discrete setting, where the two formulations, in general, give different solutions. In the past decades, complementary formulations were widely studied for static problems and eddy-current problems, where they were exploited as error estimators for adaptive refinement schemes. Moreover, the so-called bilateral energy bounds arise for some problems whether theoretically or at least numerically. However, to the best of our knowledge, little attention has been given to complementarity in the wave propagation problems. In this paper, we propose an adaptive refinement scheme using the constitutive error as an estimator, and then, we investigate the behavior in terms of bilateral energy bounds. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) aims at estimating the direction of maximal variability of a high-dimensional data set. A natural question is: does this task become easier, and estimation more accurate, when we exploit additional knowledge on the principal vector? We study the case in which the principal vector is known to lie in the positive orthant. Similar constraints arise in a number of applications, ranging from the analysis of gene expression data to spike sorting in neural signal processing. In the unconstrained case, the estimation performances of PCA have been precisely characterized using the random matrix theory, under a statistical model known as the spiked model. It is known that the estimation error undergoes a phase transition as the signal-to-noise ratio crosses a certain threshold. Unfortunately, tools from the random matrix theory have no bearing on the constrained problem. Despite this challenge, we develop an analogous characterization in the constrained case, within a one-spike model. In particular: 1) we prove that the estimation error undergoes a similar phase transition, albeit at a different thresholds in signal-to-noise ratio that we determine exactly; 2) we prove that—unlike in the unconstrained case—the estimation error depends on the spike vector, and characterize the least favorable vectors; and 3) we show that a non-negative principal component can be approximately computed—under the spiked model—in nearly linear time. This despite the fact that the problem is non-convex and, in general, NP-hard to solve exactly. [ABSTRACT FROM AUTHOR]

This paper deals with area-based subpixel image registration under the rotation-isometric scaling-translation transformation hypothesis. Our approach is based on parametrical modeling of geometrically transformed textural image fragments and maximum-likelihood estimation of the transformation vector between them. Due to the parametrical approach based on the fractional Brownian motion modeling of the local fragments' texture, the proposed estimator \mboxML_\mathrmfBm (ML stands for “maximum likelihood” and fBm stands for “fractal Brownian motion”) has the ability to better adapt to real image texture content compared with other methods relying on universal similarity measures such as mutual information or normalized correlation. The main benefits are observed when assumptions underlying the fBm model are fully satisfied, e.g., for isotropic normally distributed textures with stationary increments. Experiments on both simulated and real images and for high and weak correlations between registered images show that the \mboxML_\mathrmfBm estimator offers significant improvement compared with other state-of-the-art methods. It reduces translation vector, rotation angle, and scaling factor estimation errors by a factor of about 1.75–2, and it decreases the probability of false match by up to five times. In addition, an accurate confidence interval for \mboxML_\mathrmfBm estimates can be obtained from the Cramér–Rao lower bound on rotation-scaling-translation parameter estimation error. This bound depends on texture roughness, noise level in reference and template images, correlation between these images, and geometrical transformation parameters. [ABSTRACT FROM AUTHOR]

In the square-wave voltage signal injection sensorless drive of the interior permanent-magnet synchronous machine, the injection voltage can be distorted by the inverter nonlinearity effects especially when the injection voltage is low. If that happens, a high-frequency (HF) current signal which contains the rotor position information could be also distorted, which directly leads to an error in the position estimation. This paper analyzes the effects of the inverter nonlinearity on injection voltage, on induced current ripple, and on the position estimation performance in sequence and proposes a voltage injection method to minimize the impact of the inverter nonlinearity by the regulation of the HF current ripple. By simulations and experiments, the performance of the proposed method has been verified. The experimental results show 19.7% reduction of position estimation error under 15% of rated voltage signal injection without increasing the current ripple at a certain operating condition. Moreover, the performance of position estimation is kept uniform regardless of the loading condition. [ABSTRACT FROM AUTHOR]

Pansharpening techniques aim at improving the spatial resolution of a multispectral data set (MS) by using a panchromatic image (Pan) acquired on the same scene with a greater spatial resolution and consequently a lower ground sample distance (GSD). Usually, a quantitative assessment of the fused products cannot be directly performed because of the lack of a reference MS data set with the same GSD of the Pan. A well-known solution is Wald's protocol: The original MS and Pan are spatially degraded, the reducing factor being the ratio between their GSDs. Pansharpening is then performed between the reduced MS and Pan data sets, and the fused products are compared with the original MS, which can be used as reference. In this protocol, fusion performances are assumed to be independent of the scale so that the results at the reduced scale are an estimation of those at the original resolution. This hypothesis can be more or less reliable, depending on the sensor and/or the scene content. The objective of this paper is to propose a new methodology to infer the unknown performances of a pansharpening method at full scale. For this purpose, multiple sets of fused images are computed at degraded scales by downsampling Pan and MS data sets by means of the sensor modulation transfer function. Multiscale quality/distortion measurements are fitted by linear and quadratic polynomials in order to extrapolate their full-scale values. Once the proposed protocol has been assessed in the presence of reference originals, the obtained results are extended to the case where the reference image is not available. [ABSTRACT FROM PUBLISHER]

In sparse target inference problems, it has been shown that significant gains can be achieved by adaptive sensing using convex criteria. We generalize this previous work on adaptive sensing to: a) include multiple classes of targets with different levels of importance and b) accommodate multiple sensor models. Optimization policies are developed to allocate a limited resource budget to simultaneously locate, classify and estimate a sparse number of targets embedded in a large space. Bounds on the performance of the proposed policies are derived by analyzing a baseline policy, which allocates resources uniformly across the scene, and an oracle policy which has a priori knowledge of the target locations/classes. These bounds quantify the potential benefit of adaptive sensing as a function of target frequency and importance. Numerical results indicate that the proposed policies perform close to the oracle bound when signal quality is sufficiently high. Moreover, the proposed policies improve on previous policies in terms of reducing estimation error, reducing misclassification probability, and increasing expected return. To account for sensors with different levels of agility, three sensor models are considered: global adaptive (GA), which can allocate different amounts of resource to each location in the space? global uniform (GU), which can allocate resources uniformly across the scene? and local adaptive (LA), which can allocate fixed units to a subset of locations. Policies that use a mixture of GU and LA sensors are shown to perform similarly to those that use GA sensors while being more easily implementable. [ABSTRACT FROM AUTHOR]

We show that the rate regions achieved by linear-feedback coding schemes over dual multi-antenna Gaussian multi-access channels (MACs) and broadcast channels (BCs) with independent noises coincide. By dual here we mean: •

the channel matrices of the MAC and the BC are transposes of each other and [ABSTRACT FROM PUBLISHER]

This letter presents a novel time-of-arrival (TOA) blind estimation technique for ultra wideband energy detection receivers with reduced complexity. The proposed method is blind in the sense that it does not exploit any information about channel or noise power. This new approach is based on a set of approximations of the exact likelihood function (ELF) of the observed energy. Even though these approximations achieve an important reduction of complexity, the shape of the new approximated function is accurate enough compared to the ELF. Application of a threshold to the differential of the approximated log-likelihood function completes the procedure. Simulations show that the performance of the proposed method in terms of the cumulative distribution function of the estimation error approaches that of a method based on the ELF and a genie-aided algorithm with perfect knowledge of the optimal threshold. [ABSTRACT FROM PUBLISHER]

This paper describes the sensitivity analysis concerning time-to-go estimation error for the class of optimal impact angle control guidance laws under a stationary or a slowly moving target. First, in this paper, the closed-form solutions of those guidance laws for a lag-free system are determined. Then, we derive the explicit expressions of the terminal errors (i.e., miss distance and impact angle error) in the presence of a constant positive time-to-go estimation error. Based on these results, the guidance parameters that can help reduce the sensitivity to time-to-go estimation error are identified. Additionally, the terminal performance of the presented optimal impact angle guidance laws is evaluated from the standpoint of robustness against the time-to-go estimation errors. Numbers of linear homing simulations are also performed to confirm the analysis results. [ABSTRACT FROM AUTHOR]

Attitude control is investigated for a rigid spacecraft using single gimbal control moment gyros (SGCMGs) as actuators. By taking spacecraft and SGCMGs as an integrated system, the combined dynamics of SGCMGs and spacecraft are fully considered. The singularity problem of the SGCMGs is converted into the problem of state constraints and input constraints, and by so doing, the actuation capacity of the SGCMGs is fully utilized. To ensure accurate real-time computation, the Gauss pseudospectral and sequential quadratic programming methods are used to solve the attitude path planning of the system. Considering the uncertainties of the external disturbances and the estimation error of the inertia matrix, feedback control of the system is designed by using nonlinear model predictive control. Finally, dual-mode model predictive control is used to ensure the stability of the system, and the H8 controller and the estimated hyperellipsoid region of attraction of the linearized system are given. Simulation results show good tracking performance and robustness of the designed controller. The modeling method and the control method given in the paper provide a solution to the singularity problem of the SGCMGs. [ABSTRACT FROM AUTHOR]

Zonal occupant level is of great practical interest for building energy saving under normal operations and for fast evacuation under emergency. Though there are many existing sensing systems to estimate this information, the problem is still challenging due to the privacy concerns, the random human movement, and the accumulative error. In this paper, we consider this important problem and focus on infrared beam systems that monitor the zonal arrival and departure events. We make the following contributions. First, a rule (i.e., Rule 1) based on the stay time is developed to reduce the accumulated estimation error in each zone. Second, a rule (i.e., Rule 2) is designed to coordinate the estimation among neighboring zones. A decentralized estimation method is then developed using these two rules. Third, the advantage of this method is demonstrated through simulation results and field tests. We hope this work brings insight to zonal occupant level estimation in buildings in more general situations. [ABSTRACT FROM AUTHOR]

The longitudinal motion control of automotive vehicles is heavily reliant on information about the time-varying tire road friction coefficient. In the presence of varying road roughness profiles, the effective vertical load on each wheel varies dynamically, influencing the tire friction. In this paper, we integrated the vertical and longitudinal dynamics of a quarter wheel to form an integrated nonlinear model. In the modeled dynamics, the time-varying random road profile and the tire friction are treated as unknown inputs. To estimate these unknown inputs and states simultaneously, a combination of nonlinear Lipschitz observer and modified super-twisting algorithm (STA) observer is developed. Under Lipschitz conditions for the nonlinear functions, the convergence of the estimation error is established. Simulation results performed with the high-fidelity vehicle simulation software CarSim demonstrate the effectiveness of the proposed scheme in the estimation of states and unknown inputs. [ABSTRACT FROM PUBLISHER]