Estimate Of Location Research Articles

A semi-parametric bootstrap-based best linear unbiased estimator of location under symmetry

In this note we provide a novel semi-parametric best linear unbiased estimator (BLUE) of location and its corresponding variance estimator under the assumption the random variate is generated from a symmetric location-scale family of distributions. The approach follows in a two-stage fashion and is based on the exact bootstrap estimate of the covariance matrix of the order statistic. We generalize our approach to add a robustness component in order to derive a trimmed BLUE of location under a semi-parametric symmetry assumption.

Heuristic Global Optimization Algorithm for Estimating a Radio Source Location by a Passive Radar System

The methods for generating estimator for radio source location based on digital processing of signals received at various points in space are one of the main areas of multi-position radar systems’ research. Nowadays the above-mentioned methods that can provide the highest accuracy among the others are subject of interest. So, the mean square error usually serves as a measure of accuracy, which allows formulating a convenient, for mathematical transformations, quality criteria and synthesizing the algorithms. The traditional estimation algorithms have a multi-stage character, and they are based on the formation of optimal estimators for time and phase delays of the signals and their subsequent conversion to the source coordinates. The research has the modern approaches of the development of new positioning algorithms to guarantee the achievement of the minimum mean square error and do not create excessive computing load.

Lifting the secrets of stardust

The cosmic origin of the heaviest elements in the periodic table remains a mystery. Estimates of the physical locations of element-producing events within small galaxies that formed in the early Universe are now providing new clues.

Terrain matching localization for hybrid underwater vehicle in the Challenger Deep of the Mariana Trench

The maximum ocean depth so far reported is about 11 000 m, and is located in the Mariana Trench in the Western Pacific Ocean. The hybrid unmanned underwater vehicle, Haidou, is developed to perform scientific survey at the deepest parts of the Earth oceans. For vehicles working at the full-ocean depth, acoustic positioning is the most effective and popular method. The 11 000 m class acoustic positioning system is relatively massive and complex, and it requires specialized research vessels equipped with compatible acoustic instruments. As a compact testbed platform, it is impractical for Haidou to carry an LBL/USBL beacon with its large volume and weight. During the descent to about 11 000 m, horizontal drift could not be eliminated because of the hydrodynamics and uncertain ocean currents in the sea trials. The maximum depth recorded by Haidou is 10 905 m, and determining the precise location of the deepest point is challenging. With the bathymetric map produced by a multibeam sonar, the terrain contour matching (TERCOM) method is adopted for terrain matching localization. TERCOM is stable in providing an accurate position because of its insensitivity to the initial position errors. The final matching results show the best estimate of location in the reference terrain map.

WAVELET TRANSFORM BASED FAULT LOCATION ESTIMATOR FOR STATCOM COMPENSATED LINES

Voltage Source Converter (VSC) based Flexible AC Transmission system (FACTS) controllers have several advantages like modular construction, better performance, facility to add an energy source at the DC bus. Static Synchronous Compensator (STATCOM) is a VSC based shunt connected controller employed to have better voltage regulation at critical buses. A STATCOM with energy storage device facilitates operation in any of the four quadrants and hence present methods for fault location estimation may not be accurate and reliable. This paper puts forward a new, simple and dependable wavelet transform based fault location estimator for transmission lines deploying STATCOM integrating energy storage device. It is shown that the recommended estimation methodology is very precise and is least affected by operating mode of STATCOM and fault resistance.

Estimation of Indoor Location Through Magnetic Field Data: An Approach Based On Convolutional Neural Networks

Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (i.e., wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set.

Cortical bone mapping improves finite element strain prediction accuracy at the proximal femur.

Despite evidence of the biomechanical role of cortical bone, current state of the art finite element models of the proximal femur built from clinical CT data lack a subject-specific representation of the bone cortex. Our main research hypothesis is that the subject-specific modelling of cortical bone layer from CT images, through a deconvolution procedure known as Cortical Bone Mapping (CBM, validated for cortical thickness and density estimates) can improve the accuracy of CT-based FE models of the proximal femur, currently limited by partial volume artefacts. Our secondary hypothesis is that a careful choice of cortical-specific density-elasticity relationship may improve model accuracy. We therefore: (i) implemented a procedure to include subject-specific CBM estimates of both cortical thickness and density in CT-based FE models. (ii) defined alternative models that included CBM estimates and featured a cortical-specific or an independently optimised density-elasticity relationship. (iii) tested our hypotheses in terms of elastic strain estimates and failure load and location prediction, by comparing with a published cohort of 14 femurs, where strain and strength in stance and fall loading configuration were experimentally measured, and estimated through reference FE models that did not explicitly model the cortical compartment. Our findings support the main hypothesis: an explicit modelling of the proximal femur cortical bone layer including CBM estimates of cortical bone thickness and density increased the FE strains prediction, mostly by reducing peak errors (average error reduced by 30%, maximum error and 95th percentile of error distribution halved) and especially when focusing on the femoral neck locations (all error metrics at least halved). We instead rejected the secondary hypothesis: changes in cortical density-elasticity relationship could not improve validation performances. From these improved baseline strain estimates, further work is needed to achieve accurate strength predictions, as models incorporating cortical thickness and density produced worse estimates of failure load and equivalent estimates of failure location when compared to reference models. In summary, we recommend including local estimates of cortical thickness and density in FE models to estimate bone strains in physiological conditions, and especially when designing exercise studies to promote bone strength.

Analogues on the Sphere of the Affine-Equivariant Spatial Median

Robust estimation of location for data on the unit sphere is an important problem in directional statistics even though the influence functions of the sample mean direction and other location estimators are bounded. A significant limitation of previous literature on this topic is that robust estimators and procedures have been developed under the assumption that the underlying population is rotationally symmetric. This assumption often does not hold with real data and in these cases there is a needless loss of efficiency in the estimator. In this article, we propose two estimators for spherical data, both of which are analogous to the affine-equivariant spatial median in Euclidean space. The influence functions of the new location estimators are obtained under a new semiparametric elliptical symmetry model on the sphere and are shown to be standardized bias robust in the highly concentrated case; the influence function of the companion scatter matrix is also obtained. An iterative algorithm that computes both estimators is described. Asymptotic results, including consistency and asymptotic normality, are also derived for the location estimators that result from applying a fixed number of steps in this algorithm. Numerical studies demonstrate that both location estimators may be expected to perform well in practice in terms of efficiency and robustness. A brief example application from the geophysics literature is also provided. Supplementary materials for this article are available online.

Exploiting stilbene’s scintillation anisotropy for neutron source localization

A technique for neutron source localization that exploits the scintillation anisotropy of stilbene is presented. The light output anisotropy of stilbene and constraints on the direction of recorded scatter events imposed by a threshold were used to estimate the direction of a 252Cf neutron source relative to the crystal axes of a stilbene crystal. The neutron source location was determined via triangulation using source directions estimated from multiple detectors. Two measurements that illustrate the efficacy of the technique are presented. The first measurement was designed with a favorable geometry for triangulation, which resulted in low uncertainties in the estimate of the neutron source location. The second measurement was designed with both favorable and unfavorable geometries for triangulation, which exhibited low and high uncertainties in the estimates of neutron source location, respectively. Pair-wise combinations of three detectors were used to estimate the source location for both measurements. The neutron source was localized with errors of 5.0, 8.9, and 3.9 cm for the first measurement with source detector-distance of 89, 90, and 88 cm for the three detectors. The neutron source was localized with errors of 2.8, 14.2, 6.2 cm for the second measurement with source detector-distances of 233, 123, and 130 cm.

Measurement based threat aware drone base station deployment

Unmanned aerial vehicles are gaining importance with many civilian and military applications. Especially the surveillance, search/rescue, and military operations may have to be carried out in extremely constrained environments. In such scenarios, drone base stations DBSs have to provide communication services to the people at the ground. The ground users may have no access to the global positioning system GPS ; therefore, their locations have to be estimated using alternative techniques. Besides there may be threats in the environment, such as shooters. In this work, we address the problem of optimal DBS deployment under the aforementioned constraints. We propose a novel DBS deployment algorithm that uses estimated positions of ground users and threats. The proposed algorithm is based on receiver signal strength-based maximum likelihood estimate of user locations and K-means clustering supported heuristic that takes into account the positions of threats. Numerical results show that proposed algorithm performs close to the computation intensive near-optimal algorithm and strikes a good trade-off between the number of unserved users and the probability of DBSs not being hit.

Projection-domain metal artifact correction using a dual layer detector.

Metal artifact remains a challenge in cone-beam CT images. Many two-pass metal artifact reduction methods have been proposed, which work fairly well, but are limited when the metal is outside the scan field-of-view (FOV) or when the metal is moving during the scan. In the former, even reconstructing with a larger FOV does not guarantee a good estimate of metal location in the projections; and in the latter, the metal location in each projection is difficult to identify due to motion. Furthermore, two-pass methods increase the total reconstruction time. In this study, a projection-based metal detection and correction method with a dual layer detector is investigated. The dual layer detector provides dual energy images with perfect temporal and spatial registration in each projection, which aid in the identification of metal. A simple phantom with metal wires (copper) and a needle (steel) is used to evaluate the projection-based metal artifact reduction method from a dual layer scan and compared with that of a single layer scan. Preliminary results showed enhanced ability to identify metal regions, leading to substantially reduced metal artifact in reconstructed images. In summary, an effective single-pass, projection-domain method using a dual layer detector has been demonstrated, and it is expected to be robust against truncation and motion.

Color-Based Image Segmentation by Means of a Robust Intuitionistic Fuzzy C-means Algorithm

To yield well-suited image segmentation results, conventional clustering algorithms depend on customized hand-crafted features as well as an appropriate initialization process. This latter aspect is a mandatory pre-requisite for convergence of the algorithm, in other words, its efficiency impacts the quality of the result. In this work, we introduce an Intuitionistic Fuzzy C-Means clustering algorithm enhanced by means of Robust Statistics, which develops an outstanding image segmentation based on a basic feature such as the color information, and it requires a reduced iteration number to converge. The non-parametric Lorentzian Redescending M-estimator is used both at initialization and iterative stages of the clustering algorithm; since, it behaves such as a robust location estimator when the centroid vector is computed, and as a weighting when the membership matrix is updated. With the fusion of both techniques, we can guarantee that the introduced clustering algorithm can efficiently develop the task of segmentation of color images and pattern recognition processes. The robustness and effectiveness of this proposal is verified by experiments on the natural color images BSDS500 dataset, as well as a simulated dataset corrupted with atypical data.

Antarctic Topographic Realizations and Geostatistical Modeling Used to Map Subglacial Lakes

Antarctic subglacial lakes can play an important role in ice sheet dynamics, biology, geology, and oceanography, but it is difficult to definitively constrain their character and locations. Subglacial lake locations are related to factors including heat flux, ice surface slope, ice thickness, and bed topography, though these relationships are not fully quantified. Bed topography is particularly important for determining where water flows and accumulates, but digital elevation models of the ice sheet bed rely on interpolation and are unrealistically smooth, biasing estimates of subglacial lake location and surface area. To address this issue, we use geostatistical methods to simulate realistically rough bed topography. We use our simulated topography to predict subglacial lake distribution across the continent using a binomial logistic regression, which uses physical parameters and known lake locations to calculate the probabilities of lake occurrences. Our results suggest that topography models interpolated without appropriate geostatistics overestimate subglacial lake surface area and that total lake surface area is lower than previously predicted. We find that radar‐detected lakes are more likely to occur in the interior of East Antarctica, while altimetry‐detected (active) lakes are expected to be found in West Antarctica and near the grounding line. We observe that radar‐detected lakes have a high correlation with heat flux and ice thickness, while active lakes are associated with higher ice velocity.

Semiparametric inference with missing data: Robustness to outliers and model misspecification

Classical semiparametric inference with missing outcome data is not robust to contamination of the observed data and a single observation can have arbitrarily large influence on estimation of a parameter of interest. This sensitivity is exacerbated when inverse probability weighting methods are used, which may overweight contaminated observations. Inverse probability weighted, double robust and outcome regression estimators of location and scale parameters are introduced, which are robust to contamination in the sense that their influence function is bounded. Asymptotic properties are deduced and finite sample behaviour studied. Simulated experiments show that contamination can be more serious a threat to the quality of inference than model misspecification. An interesting aspect of the results is that the auxiliary outcome model used to adjust for ignorable missingness by some of the estimators, is also useful to protect against contamination. Both adjustment to ignorable missingness and protection against contamination are achieved through weighting schemes. A case study illustrates how the resulting weights can be studied to gain insights on how the two different weighting schemes interact.

Variability and bias between magnetoencephalography systems in localization of the primary visual cortex

ObjectivesWhen using MEG for pre-surgical mapping it is critically important that reliable estimates of functional locations, such as the primary visual cortex (V1) can be provided. Several different models of MEG systems exist, each with varying software and hardware configurations, and it is not currently known how the system type contributes to variability in V1 localization. Patients and MethodsIn this study, participants underwent MEG sessions using two different systems (Vector View and CTF) during which they were presented with a repeating grating stimulus to the lower-left visual quadrant to generate a visual evoked field (VEF). The location, amplitude and latency of the VEF source was compared between systems for each participant. ResultsNo significant differences were found in latency and amplitude between systems, however, a significant bias in the latero-medial position of the localization was present. The median inter-system Euclidian distance between V1 localization across participants was 10.5 mm. ConclusionsOverall, our results indicate that mapping of V1 can be reliably reproduced within approximately one centimetre by different MEG systems. SignificanceThis result provides knowledge of the useful limits on the reliability of localization which can be taken into consideration in clinical practice.

On Moments of Lindley Distribution Based on Generalized Order Statistics

In this paper, we have deduced the exact and explicit expressions for single and product moments of Lindley distribution based on generalized order statistics in terms of Gauss hypergeometric function and Kampé de Fériet series. These results include the exact expression for the single and product moments of order statistics, progressive Type II censoring, record values Pfeifer’s record value and sequential order statistics from Lindley distribution. Further, means and variances of Lindley distribution based on order statistics, progressive type II censored order statistics and for generalized order statistics have been computed. We have also calculated the best linear unbiased estimators for location and scale parameters of Lindley distribution utilizing these results. Finally, a real data application is given.

Robust Confidence Intervals for PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina.

In this article, robust confidence intervals for PM2.5 (particles with size less than or equal to ) concentration measurements performed in La Carolina Park, Quito, Ecuador, have been built. Different techniques have been applied for the construction of the confidence intervals, and routes around the park and through the middle of it have been used to build the confidence intervals and classify this urban park in accordance with categories established by the Quito air quality index. These intervals have been based on the following estimators: the mean and standard deviation, median and median absolute deviation, median and semi interquartile range, -trimmed mean and Winsorized standard error of order , location and scale estimators based on the Andrew’s wave, biweight location and scale estimators, and estimators based on the bootstrap- method. The results of the classification of the park and its surrounding streets showed that, in terms of air pollution by PM2.5, the park is not at caution levels. The results of the classification of the routes that were followed through the park and its surrounding streets showed that, in terms of air pollution by PM2.5, these routes are at either desirable, acceptable or caution levels. Therefore, this urban park is actually removing or attenuating unwanted PM2.5 concentration measurements.

Nanomachine Localization in a Diffusive Molecular Communication System

The information-bearing molecules are used for communication among nanomachines in a diffusion-based molecular communication (DMC) system. The location of the nanomachine is needed for improving the performance of a DMC system. In the literature, only the distance between a transmitter nanomachine (TN) and a receiver nanomachine is estimated. In contrast, an iterative maximum likelihood estimate of the nanomachine location is computed herein. Notably, both the signal-dependent noise and intersymbol interference (ISI) effects are considered for nanomachine localization. Further, the nanomachine localization is performed, when the locations of the receiver nanomachine are both known and unknown. Additionally, the condition for the unique localizability of the TN and the bound of the proposed estimator are obtained. The results illustrate the effectiveness of the proposed method.

A passive energy-based method for footstep impact localization, using an underfloor accelerometer sensor network with Kalman filtering

An underfloor accelerometer sensor network can be used to track occupants in an indoor environment using measurements of floor vibration induced by occupant footsteps. To achieve occupant tracking, each footstep impact location must first be estimated. This paper proposes a new energy-based algorithm for footstep impact localization. Compared to existing energy-based algorithms, the new algorithm achieves higher localization accuracy and removes a previously required calibration step (removal of the need to estimate floor-dependent parameters). Furthermore, the algorithm uses a much smaller data sampling rate compared to time of flight/arrival localization methods, which greatly reduces data and data-processing time. The new algorithm is a two-step location estimator: the first step is a coarse location estimate, with the second step as a fine location search through a nonlinear minimization problem. The performance of the proposed algorithm is evaluated using a single occupant walking experiment on an instrumented floor inside an operational smart building. This paper also demonstrates that higher localization accuracy is obtained using an additional Kalman filtering scheme.

Investigation of finite-sample properties of robust location and scale estimators

When the experimental data set is contaminated, we usually employ robust alternatives to common location and scale estimators such as the sample median and Hodges-Lehmann estimators for location and the sample median absolute deviation and Shamos estimators for scale. It is well known that these estimators have high positive asymptotic breakdown points and are Fisher-consistent as the sample size tends to infinity. To the best of our knowledge, the finite-sample properties of these estimators, depending on the sample size, have not well been studied in the literature. In this paper, we fill this gap by providing their closed-form finite-sample breakdown points and calculating the unbiasing factors and relative efficiencies of the robust estimators through the extensive Monte Carlo simulations up to the sample size 100. The numerical study shows that the unbiasing factor improves the finite-sample performance significantly. In addition, we provide the predicted values for the unbiasing factors obtained by using the least squares method which can be used for the case of sample size more than 100.