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This article addresses the opportunistic use of satellite downlink attenuation measurements for the site-specific estimation of rainfall rate. In particular, this article proposes a regression model characterized by the introduction of correction elements extracted from the frequency-domain analysis of the attenuation time series, and the use of a simple linear regression function with a small modification, which allows to using a negative intercept with the goal of minimizing the maximum error (large underestimation/overestimation). The performance, assessed using attenuation measurements of a satellite downlink in Q/V band shows that the proposed model allows to estimate the rainfall rate with low average error and low maximum error with respect to a more standard ITU model, so as to offer more reliable estimations. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating the mean of a symmetric log-concave distribution under the constraint that only a single bit per sample from this distribution is available to the estimator. We study the mean squared error as a function of the sample size (and hence the number of bits). We consider three settings: first, a centralized setting, where an encoder may release $n$ bits given a sample of size $n$ , and for which there is no asymptotic penalty for quantization; second, an adaptive setting in which each bit is a function of the current observation and previously recorded bits, where we show that the optimal relative efficiency compared to the sample mean is precisely the efficiency of the median; lastly, we show that in a distributed setting where each bit is only a function of a local sample, no estimator can achieve optimal efficiency uniformly over the parameter space. We additionally complement our results in the adaptive setting by showing that one round of adaptivity is sufficient to achieve optimal mean-square error. [ABSTRACT FROM AUTHOR]

The stochastic Richardson extrapolation-based numerical error quantification (StREEQ) technique has been applied to accomplish a code verification for the cavity systemgenerated electromagnetic pulse (SGEMP) simulation code LASER-SGEMP. Here, the grid convergence index method estimates the discretization error caused by the finite difference, while the bootstrap sampling demonstrates the sampling error arising from the inherent stochastic noise. The electric field waveforms obtained by the LASER-SGEMP code agree well with the high precision solution from a reference. According to the StREEQ method, the convergence value of the electric field at 1.8 ns is calculated to be 0.955 V/m and the numerical error characterized by the 95% confidence interval is [0.948, 0.964] V/m, while the high precision solution is 0.961 V/m. Therefore, the code verification has supported the accuracy of the LASER-SGEMP code, and the StREEQ technique is proved to be capable of estimating the numerical error of the cavity SGEMP simulation. [ABSTRACT FROM AUTHOR]

Due to the unbalanced three-phase currents and the torque ripples and speed fluctuations to the drive caused by the current sampling errors of three-phase current sensors and corresponding signal processing circuits, the estimation on current sampling errors is of great importance to eliminate these adverse effects. The observer-based methods, however, present two defects: first, they could increase the computational burden to the drives, thus limiting their practical applications; second, their high dependence on the system speed or position feedback information might degrade the estimation accuracy. To solve these problems, this article proposed a fixed-point sampling method of lower branch flows and three phase-currents, which adopts the relations between the measured currents, rather than any complex algorithm, to estimate the current sampling errors. To coordinate with the proposed strategy, a fixed-point sampling scheme was accordingly rolled out. Compared with the observer-based methods, as a fixed-point one, much applicable to actual digital controllers, it could reduce the computational burden of the drive. Meanwhile, its effectiveness was verified by experimental results on a 1.5-kW IPMSM prototype, which showed accurate estimated current sampling errors and the effectively eliminated torque ripples and speed fluctuations. [ABSTRACT FROM AUTHOR]

We show through case studies that it is easier to estimate the fundamental limits of data processing than to construct the explicit algorithms to achieve those limits. Focusing on binary classification, data compression, and prediction under logarithmic loss, we show that in the finite space setting, when it is possible to construct an estimator of the limits with vanishing error with $n$ samples, it may require at least $n\ln n$ samples to construct an explicit algorithm to achieve the limits. [ABSTRACT FROM AUTHOR]

This correspondence paper aims at addressing the estimation of carrier frequency offset (CFO) for the uplink orthogonal frequency-division multiple access systems. Since the CFOs of the signals from different active users are sparsely distributed in the frequency domain, a sparse Bayesian learning (SBL) is tailored to determine the CFO in this paper, ending up with the SBL assisted CFO (SBL-CFO) estimator. In particular, the CFO estimation problem is first formulated as a sparse nonnegative least squares (S-NNLS) problem. Meanwhile, background noise and sampling errors are mitigated utilizing a selection matrix and a whitening filter, respectively. This enables us to exploit the SBL with nonnegative Laplace prior (SBL-NLP) to solve the S-NNLS problem. Furthermore, in order to make the convergence of the SBL-NLP algorithm faster and its estimation more accurate, the hyperprior inherent in the SBL-NLP algorithm is initialized by the traditional SBL with nonnegative Gaussian prior. Simulation results show that our proposed SBL-CFO estimator significantly outperforms the state-of-the-art estimators in terms of estimation accuracy, especially when the CFOs and the number of active users are large. [ABSTRACT FROM AUTHOR]

The interpolation based on prediction-error filter (PEF) is one of the most effective approaches recently proposed for seismic data reconstruction. However, the number of effective regression equations for estimating the filter coefficients will be much less when missing many seismic traces, which makes the estimated filter coefficients inaccurate or even impossible to be estimated. To improve the accuracy of filter coefficients, in this paper, we design a multiscale and multidirectional PEF, in which the number of effective regression equations can be increased much more, and use it to seismic data reconstruction. First, we estimate the adaptive different directional PEFs using the known data in different scales. The known data can be regularly sampled with randomly or regularly missing, or even both of them. Then, we interpolate missing seismic traces using estimated PEF and the sparse known traces. The regularization in least-squares inversion controls the adaptivity of multiscale multidirectional PEF. The use of more effective regression equations in inversion makes the filter coefficients more accurate. In addition, the multiscale filter can conveniently deal with the case of the simultaneous existence of randomly and regularly missing, while the conventional methods have to be treated separately for randomly and regularly missing. The applicability and effectiveness of the proposed method are examined by synthetic and field data examples. [ABSTRACT FROM AUTHOR]

Interval analysis procedures are used to estimate the parameters of an experimental chemical process under conditions of noise and uncertainty in the probabilistic characteristics of the measurement errors for a small measurement sample. Interval analysis makes it possible to describe exactly the set of admissible values of the estimated parameters needed for correct organization of the technological process through a correct choice of its parameters. Approximate estimates of the parameters are obtained by formal application of a statistical approach and it is shown that in this case the standard statistical approach yields essentially meaningless estimates for the parameters of the process studied here. [ABSTRACT FROM AUTHOR]

Abstract Diffusion processes are commonplace in many scientific disciplines, as they describe a broad range of physical phenomenon. Consider a diffusion process observed through linear sensors with additive white noise. We derive the optimal placement of these sensors for estimating this process, where optimality is defined in terms of the mean squared estimation error (MSE) of the state given past observations. We consider two cases. First, we assume the sensors to be orthogonal. We show in this case that the minimum MSE is related to the nuclear norm of the system matrix of the process. Second, we remove the orthogonality constraint and show that the MSE is related to the Schatten p -norm of the system matrix of the process and the optimal sensors are proportional its matrix cube root. We present simulation results illustrating the fact that the gain afforded by optimizing the choice of sensors depends on the ratio p ∕ n , where n is the dimension of the system and p the dimension of the Wiener processes driving it, and this gain is in general large, especially when p ∕ n is small. [ABSTRACT FROM AUTHOR]

Fundamental relations between information and estimation have been established in the literature for the continuous time Gaussian and Poisson channels. In this paper, we demonstrate that such relations hold for a much larger family of continuous-time channels. We introduce the family of semi-martingale channels where the channel output is a semi-martingale stochastic process, and the channel input modulates the characteristics of the semi-martingale. For these channels, which includes as a special case the continuous time Gaussian and Poisson models, we establish new representations relating the mutual information between the channel input and output to an optimal causal filtering loss, thereby unifying and considerably extending results from the Gaussian and Poisson settings. Extensions to the setting of mismatched estimation are also presented where the relative entropy between the laws governing the output of the channel under two different input distributions is equal to the cumulative difference between the estimation loss incurred by using the mismatched and optimal causal filters, respectively. The main tool underlying these results is the Doob–Meyer decomposition of a class of sub-martingales. The results in this paper can be viewed as the continuous-time analogues of recent generalizations for relations between information and estimation for discrete-time Lévy channels. [ABSTRACT FROM AUTHOR]

This letter proposes a structured approach for accurate estimation of diode reverse-recovery characteristics using datasheet specifications. Considering a conductance model of a diode, the entire reverse-recovery process is divided into four operating modes. The state equations for each mode are derived taking the circuit nonidealities, viz., device capacitance and parasitic inductances, into account. Analytical expressions for reverse-recovery charge, reverse-recovery time, and diode current are derived and numerically solved to estimate the diode conductance directly from manufacturer's data. An empirical model is proposed to characterize the effects of temperature variation on the diode conductance. Numerical simulation and experimental results are presented for antiparallel diodes of three MOSFETs having different current and voltage ratings and also for a discrete p-n diode at different junction temperatures. The maximum estimation error with the proposed approach is 5.1% for peak reverse-recovery current and less than 8% for reverse-recovery time, peak voltage, and energy loss. [ABSTRACT FROM AUTHOR]

Sensor-based online process monitoring has extensive applications, such as in manufacturing and service industries. In real environments, though, sensor data are often contaminated with noise, leading to severe challenges in accurate data analysis. In the existing literature, noise is generally modeled as Gaussian to analyze sensor data for various applications, for example in fault detection and diagnostics. However, in some applications, such as due to challenging field conditions, sensor data may be disturbed by high levels of outliers such that the Gaussian assumption of sensor noise is inadequate, thus leading to large estimation errors. This paper focuses on online classification applications. A robust sparse representation classification method is proposed, which considers non-Gaussian noise, and thus can effectively analyze sensor data with higher levels of outliers. Case studies were completed, based on both numerically simulated sensor data and actual wearable sensor data from occupational manual material handling process monitoring. The proposed classification method could effectively analyze sensor data with non-Gaussian noise, and outperformed commonly used methods in the literature. Thus, this new method may be advantageous for solving classification problems in challenging field conditions, to address the difficulties of high levels of sensor outliers. [ABSTRACT FROM AUTHOR]

This paper introduces a time-domain nondata-aided maximum-likelihood (ML) estimation of Doppler frequency for orthogonal frequency-division multiplexing (OFDM) over doubly selective fading channels with the Clarke–Jakes–Doppler spectrum. The proposed approach adopts the repetitive signal structure of OFDM, such as the cyclic prefix, which can be used for synchronization purpose. The originally derived ML estimation results in a high computational cost; therefore, after gaining insights into the log-likelihood function, we further design an algorithm with reduced complexity. The proposed algorithm only has a complexity $\sim O(QN_{d})$ , where $Q$ and $N_{d}$ denote the numbers of repetitive parts and samples in a repetitive part, respectively. In contrast to conventional works based on (data aided) channel estimates and/or assuming that channels are flat fading, the proposed method employs the cyclostationary feature of received signals and is not sensitive to channel estimation error and unknown channel phases. Furthermore, the Cramér–Rao lower bounds of the Doppler frequency estimate over nonline-of-sight (NLOS) and line-of-sight multipath fading channels are analyzed. Simulations confirm the advantages of the proposed estimator. [ABSTRACT FROM AUTHOR]

In this letter, we discuss cost optimization of sensor networks monitoring structurally full-rank systems under distributed observability constraint. Using structured systems theory, the problem is relaxed into two subproblems: first, sensing cost optimization; and second, networking cost optimization. Both problems are reformulated as combinatorial optimization problems. The sensing cost optimization is shown to have a polynomial-order solution. The networking cost optimization is shown to be NP-hard in general, but has a polynomial-order solution under specific conditions. A 2-approximation polynomial-order relaxation is provided for general networking cost optimization, which is applicable in large-scale system monitoring. [ABSTRACT FROM AUTHOR]

This paper is concerned with the distributed fusion estimation problem for time-varying stochastic uncertain systems with multiple asynchronous sampling sensors of different fading measurement rates. Stochastic uncertainties of multiplicative noises exist in the state and measurement equations. The phenomena of fading measurements of different sensors are depicted by a group of stochastic variables with known statistics. Different sensors have different sampling rates. Sampling period of each sensor is uniform and an integer multiple of the state update period. By transforming the multiplicative noises into the additive noises, local estimators (LEs) and estimation error cross-covariance matrices between any two LEs are derived at the state update points. A real-time distributed fusion estimator (DFE) is obtained by using the matrix-weighted fusion estimation algorithm in the linear unbiased minimum variance sense. The asymptotic stability and steady-state property are analyzed. A new conception of $\boldsymbol{{period}}$ steady state is presented and the $\boldsymbol{{period}}$ steady-state properties of the LEs, cross-covariance matrices, and DFE are proven. Though DFE has lower accuracy than the centralized fusion estimator, it has better robustness and flexibility since it has a parallel structure. To avoid the calculation of cross-covariance matrices, a sequential covariance intersection fusion estimator is given by using two-sensor covariance intersection fusion algorithm. It has lower accuracy but smaller computational cost than DFE, and better accuracy than LEs. Two examples are given to show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose an open-loop unequal-error-protection querying policy based on superposition coding for the noisy 20 questions problem. In this problem, a player wishes to successively refine an estimate of the value of a continuous random variable by posing binary queries and receiving noisy responses. When the queries are designed non-adaptively as a single block and the noisy responses are modeled as the output of a binary symmetric channel, the 20 questions problem can be mapped to an equivalent problem of channel coding with unequal error protection (UEP). A new non-adaptive querying strategy based on UEP superposition coding is introduced, whose estimation error decreases with an exponential rate of convergence that is significantly better than that of the UEP repetition coding introduced by Variani et al. (2015). With the proposed querying strategy, the rate of exponential decrease in the number of queries matches the rate of a closed-loop adaptive scheme, where queries are sequentially designed with the benefit of feedback. Furthermore, the achievable error exponent is significantly better than that of random block codes employing equal error protection. [ABSTRACT FROM AUTHOR]

Abstract: This study introduces the NMπ computer program designed for estimation of plant mating system and seed and pollen dispersal kernels. NMπ is a re‐implementation of the NM+ program and provides new features such as support for multicore processors, explicit treatment of dioecy, the possibility of incorporating uniparentally cytoplasmic markers, the possibility of assessing assortative mating due to phenotypic similarity and inference about offspring genealogies. The probability model of parentage (the neighbourhood model) accounts for missing data and genotyping errors, which can be estimated along with regular parameters of the mating system. The program has virtually no restrictions with respect to a number of individuals, markers or phenotypic characters. A console version of NMπ can be run under a wide variety of operating systems, including Windows, Linux or Mac OS. For Windows users, a graphical user interface is provided to facilitate operating the software. The program, user manual and example data are available on http://www.ukw.edu.pl/pracownicy/plik/igor_chybicki/3694/. [ABSTRACT FROM AUTHOR]

In this article, a new advantageous approach for damage identification of mechanical structures based on state projection estimation error (SP2E) is proposed. The theoretical foundation relies on output-only identification [ABSTRACT FROM AUTHOR]

To correctly insert a gastric tube into the stomach, the position of an attached permanent magnet is estimated by solving the inverse problem using measurement data obtained from magneto-impedance (MI) sensors placed outside the patient’s body. The ubiquity and power of tablet computers make them promising tools to easily confirm the position of the tip of the gastric tube. However, a single mobile device may be insufficient for actually solving the inverse problem. Therefore, we propose a distributed cooperative local access network system for the estimation of the position of a permanent magnet attached to the tip of a gastric tube. In addition, we evaluate whether ferromagnetic materials near a sensor affect the performance of the system. Experimental results show that the position estimation error is less than 10 mm, even when the tip is 150 mm from a MI sensor, and that the tip position can be displayed on a tablet computer every 0.25 s. These results suggest that the proposed method holds promise for hospital applications. [ABSTRACT FROM AUTHOR]

This paper proposes a novel blind carrier frequency offset (CFO) estimator, namely the sparse recovery assisted CFO (SR-CFO) estimator, for the uplink orthogonal frequency-division multiple access (OFDMA) systems. By exploiting the sparsity embedded in the OFDMA data, the CFO estimation is formulated as an optimization problem of sparse recovery with high-resolution. Meanwhile, in order to enhance the estimation accuracy of CFOs, background noise and sampling errors are mitigated by exploiting the structure of the noise covariances matrix in the transformed observation data, and the asymptotic distribution of the sampling errors. Furthermore, we propose an approach for deriving the regularization parameter used by the SR-CFO estimator, so as to control the tradeoff between the data fitting error and the sparsity of solution. The performance of the proposed SR-CFO estimator along with other four existing estimators is investigated and compared. Numerical results show that the proposed SR-CFO estimator is superior to the state-of-the-art estimators in terms of the estimation reliability. [ABSTRACT FROM PUBLISHER]

In this paper, we study the problem of velocity estimation in vehicular networks by exploiting the sparsity of vehicle velocity trajectory. First, we exploit the sparsity of vehicular velocity trajectories to reduce the beaconing load on the channel. To this end, we propose a sublayer that reduces the number of transmitted packets through a superframe. At each superframe, each vehicle transmits a few samples of its velocity as well as an encoded measurement of its past velocities. Sparse recovery of each velocity vector is performed at the receiver. We propose the use of repetitions at the medium access control (MAC) layer. Moreover, we extend our scheme into a streaming estimation system without the superframe concept. The streaming system uses a sliding window concept. We thoroughly study the estimation error of the streaming system, and we propose an algorithm to find the best solution over a streaming sliding window. The proposed superframe and streaming schemes are tested with real velocity traces collected in the city of Toronto to capture the performance in the city and highway conditions. Experiment results show that the proposed schemes significantly reduce the number of exchanged packets while preserving the velocity information with an excellent accuracy at the receiver. We also demonstrate how past velocity can be used to enhance vehicle localization accuracy. [ABSTRACT FROM PUBLISHER]

This paper introduces a method to monitor battery state of health (SOH) by estimating the number of cyclable Li-ions, a health-relevant electrochemical variable. SOH monitoring is critical to battery management in balancing the trade-off between maximizing system performance and minimizing battery degradation. The decrease of cyclable Li-ions indicates the effect on the SOH of degradation mechanisms that consume cyclable Li-ions. The unavailability of the number of cyclable Li-ions through non-invasive measurements makes its estimation necessary for in-situ SOH monitoring. In this paper, the extended Kalman filter (EKF) is used to estimate the number of cyclable Li-ions as an unknown battery parameter. The single particle model (SPM), a simplified battery electrochemical model, is used as the model in the EKF to achieve a computational complexity suitable for on-line estimation. Simulations are performed under typical electric vehicle current trajectories using an example parameter set for a hybrid-electric-vehicle battery. In the simulations, the battery is represented by the Doyle–Fuller–Newman (DFN) model, an electrochemical model with higher fidelity than the SPM. To comply with the practice, instead of using the same parameters as the DFN model in the SPM, parameterization of the SPM is performed before estimation of the number of cyclable Li-ions. The simulations show high estimation accuracy of the number of cyclable Li-ions using the EKF, even with the structural and parametric differences between the DFN model and the SPM, under both the ideal conditions and various non-ideal conditions (i.e., SOC estimation error, additional modeling error, and measurement noise). [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]

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]

An accurate battery model is the prerequisite for reliable state estimate of vanadium redox battery (VRB). As the battery model parameters are time varying with operating condition variation and battery aging, the common methods where model parameters are empirical or prescribed offline lacks accuracy and robustness. To address this issue, this paper proposes to use an online adaptive battery model to reproduce the VRB dynamics accurately. The model parameters are online identified with both the recursive least squares (RLS) and the extended Kalman filter (EKF). Performance comparison shows that the RLS is superior with respect to the modeling accuracy, convergence property, and computational complexity. Based on the online identified battery model, an adaptive peak power estimator which incorporates the constraints of voltage limit, SOC limit and design limit of current is proposed to fully exploit the potential of the VRB. Experiments are conducted on a lab-scale VRB system and the proposed peak power estimator is verified with a specifically designed “two-step verification” method. It is shown that different constraints dominate the allowable peak power at different stages of cycling. The influence of prediction time horizon selection on the peak power is also analyzed. [ABSTRACT FROM AUTHOR]

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]

A method is proposed for scheduling sensor accesses to the shared network in a networked control system. The proposed method determines the access order in which the sensors are granted medium access through minimization of the state estimation error covariance. Solving the problem by evaluating the error covariance for each possible ordered set of sensors is not practical for large systems. Therefore, a convex optimization problem is proposed, which yields approximate yet acceptable results. A state estimator is designed for the augmented system resulting from the incorporation of the optimally chosen communication sequence in the plant dynamics. A car suspension system simulation is conducted to test the proposed method. The results show promising improvement in the state estimation performance by reducing the estimation error norm compared to round-robin scheduling. [ABSTRACT FROM AUTHOR]

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]

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]

• Estimation error with model-based sampling can be related to samples properties. • A criterion can be derived from the expression of theoretical estimation errors. • Such a criterion can guide sampling sites selection to minimise estimation errors. • The framework demonstrates the interest in clustering approaches for sampling. Model sampling has proven to be an interesting approach to optimize the sampling of an agronomic variable of interest at the field level. The use of a model improves the quality of the estimates by making it possible to integrate the information provided by one or more auxiliary data. It has been shown that such an approach gives better estimations compared to more traditional approaches. Through a statistical work describing the properties of model sampling variance, this paper details how the different factors either related to sample characteristics or to the correlation between the auxiliary data and the variable of interest, affect estimation error. The resulting equations show that the use of samples with a mean close to the field mean and with a substantial dispersion reduces the estimation variance. On the basis of these statistical considerations, a variance criterion is defined to compare sample properties. The lower the value of the criterion of a sample, the lower the variance of the estimate and the expected errors. These theoretical insights were applied to real commercial vine fields in order to validate the demonstration. Nine vine fields were considered with the objective to provide the best yield estimation. High resolution vegetative index derived from airborne multispectral image was used to drive the sampling and the estimation. The theoretical considerations were verified on the nine fields; as the observed estimation errors correspond quite well to the values predicted by the equations. The selection of a large number of random samples from these fields confirms that samples associated with higher values of the chosen criterion result, on average, in larger yield estimation errors. Samples with the highest criterion values are associated with mean estimation errors up to two times larger than those of average samples. Random sampling is also compared to two target sampling approaches (Clustering based on quantiles or on k-means algorithm) commonly considered in the literature, whose characteristics improve the value of the proposed criterion. It is shown that these sampling strategies produce samples associated with criterion values up to 100 times smaller than random sampling. The use of these easy-to-implement methods thus guarantees to reduce the variance of the estimation and the estimation errors. [ABSTRACT FROM AUTHOR]

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 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]

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]

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]

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]

This paper studies the convergence of the estimation error process and the characterization of the corresponding invariant measure in distributed Kalman filtering for potentially unstable and large linear dynamic systems. A gossip network protocol termed modified gossip interactive Kalman filtering (M-GIKF) is proposed, where sensors exchange their filtered states (estimates and error covariances) and propagate their observations via intersensor communications of rate \bar \gamma ; \bar \gamma is defined as the averaged number of intersensor message passages per signal evolution epoch. The filtered states are interpreted as stochastic particles swapped through local interaction. This paper shows that the conditional estimation error covariance sequence at each sensor under M-GIKF evolves as a random Riccati equation (RRE) with Markov modulated switching. By formulating the RRE as a random dynamical system, it is shown that the network achieves weak consensus, i.e., the conditional estimation error covariance at a randomly selected sensor converges weakly (in distribution) to a unique invariant measure. Further, it is proved that as \bar \gamma \rightarrow \infty this invariant measure satisfies the large deviation (LD) upper and lower bounds, implying that this measure converges exponentially fast (in probability) to the Dirac measure \delta P^{*} , where P^* is the stable error covariance of the centralized (Kalman) filtering setup. The LD results answer a fundamental question on how to quantify the rate at which the distributed scheme approaches the centralized performance as the intersensor communication rate increases. [ABSTRACT FROM PUBLISHER]

In this paper, we study the distributed blind equalization of networked single-input multi-output (SIMO) systems. An indirect distributed equalization framework is presented, which estimates the transfer functions followed by the associated equalizers. Two distributed indirect equalization algorithms are proposed: one depends on multiple average consensus operations, and the other relies on the combination of innovation and one average consensus operation. The former generates an approximate equalizer for which the associated estimation error is determined by the number of average consensus operations, while the latter can provide an accurate equalizer estimation under some mild conditions. The proposed algorithms estimate the desired equalizer recursively and recover the source signal in real time. Furthermore, the distributed equalization under a time-varying topology is investigated as well. Convergence properties of the proposed algorithms are established and numerical simulations are carried out to show the performances of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

This paper establishes a nearly optimal algorithm for denoising a mixture of sinusoids from noisy equispaced samples. We derive our algorithm by viewing line spectral estimation as a sparse recovery problem with a continuous, infinite dictionary. We show how to compute the estimator via semidefinite programming and provide guarantees on its mean-squared error rate. We derive a complementary minimax lower bound on this estimation rate, demonstrating that our approach nearly achieves the best possible estimation error. Furthermore, we establish bounds on how well our estimator localizes the frequencies in the signal, showing that the localization error tends to zero as the number of samples grows. We verify our theoretical results in an array of numerical experiments, demonstrating that the semidefinite programming approach outperforms three classical spectral estimation techniques. [ABSTRACT FROM PUBLISHER]

Under classical survey sampling theory the errors mainly studied in the estimation are sampling errors. However, often non-sampling errors are more influential to the properties of the estimator than sampling errors. This is recognized by practitioners, researchers and many great works of literature regarding non-sampling errors have been published during last two decades, especially regarding non-response error which is one of the cornerstones of the non-sampling errors. The literature handles one kind of non-sampling error at a time, although in real surveys more than one non-sampling error is usually present.In this paper, two kinds of non-sampling errors are considered at the estimation stage: non-response and measurement error. An exponential ratio type estimator has been developed to estimate the population mean of the response variable in the presence of non-response and measurement errors. Theoretically and empirically, it has been shown that the proposed estimator is more efficient than usual unbiased estimator and other existing estimators. [ABSTRACT FROM AUTHOR]

Flow data is often visualized by animated particles inserted into a flow field. The velocity of a particle on the screen is typically linearly scaled by the velocities in the data. However, the perception of velocity magnitude in animated particles is not necessarily linear. We present a study on how different parameters affect relative motion perception. We have investigated the impact of four parameters. The parameters consist of speed multiplier, direction, contrast type and the global velocity scale. In addition, we investigated if multiple motion cues, and point distribution, affect the speed estimation. Several studies were executed to investigate the impact of each parameter. In the initial results, we noticed trends in scale and multiplier. Using the trends for the significant parameters, we designed a compensation model, which adjusts the particle speed to compensate for the effect of the parameters. We then performed a second study to investigate the performance of the compensation model. From the second study we detected a constant estimation error, which we adjusted for in the last study. In addition, we connect our work to established theories in psychophysics by comparing our model to a model based on Stevens’ Power Law. [ABSTRACT FROM AUTHOR]

Entropy makes it possible to measure the uncertainty about an information source from the distribution of its output symbols. It is known that the maximum Shannon's entropy of a discrete source of information is reached when its symbols follow a Uniform distribution. In cryptography, these sources have great applications since they allow for the highest security standards to be reached. In this work, the most effective estimator is selected to estimate entropy in short samples of bytes and bits with maximum entropy. For this, 18 estimators were compared. Results concerning the comparisons published in the literature between these estimators are discussed. The most suitable estimator is determined experimentally, based on its bias, the mean square error short samples of bytes and bits. [ABSTRACT FROM AUTHOR]