Estimate Of Location Research Articles

High Resolution Direct Detection and Position Determination of Sources With Intermittent Emission

This paper presents our research findings on the direct detection and position determination (DDPD) problem, which is to estimate the positions and the total number of multiple sources with intermittent emission, using the received signals intercepted by a moving antenna array at multiple observation slots. We combine the direct position determination (DPD) concept and the Minimum Variance Distortionless Response (MVDR) criterion to solve the DDPD problem without the prior knowledge of the effective number of sources (the number of sources at every observation slot). We find out that the parallel combination of the MVDR spectrum in the cost function results in its own sensitivity to the missing emission, which makes the location estimator bias. Therefore, we use the K-means clustering algorithm to identify the sources with intermittent emission and their emitting slots adaptively, based on the MVDR spectrum values over all observation slots. Then, we use the spectrum values of the identified emitting slots to reconstruct the MVDR spectrum. Finally, we find the peaks of the reconstructed MVDR spectrum, which are corresponding to the source positions. The results of the simulations demonstrate that the proposed method gets asymptotic performance with the signal to noise ratio (SNR), both in the aspects of the root mean square error (RMSE) and the bias error, and high resolution as a generalized MVDR based method.

Analysis and Visualization of Deep Neural Networks in Device-Free Wi-Fi Indoor Localization

Device-free Wi-Fi indoor localization has received significant attention as a key enabling technology for many Internet of Things (IoT) applications. Machine learning-based location estimators, such as the deep neural network (DNN), carry proven potential in achieving high-precision localization performance by automatically learning discriminative features from the noisy wireless signal measurements. However, the inner workings of DNNs are not transparent and not adequately understood especially in the indoor localization application. In this paper, we provide quantitative and visual explanations for the DNN learning process as well as the critical features that DNN has learned during the process. Toward this end, we propose to use several visualization techniques, including: 1) dimensionality reduction visualization, to project the high-dimensional feature space to the 2D space to facilitate visualization and interpretation, and 2) visual analytics and information visualization, to quantify relative contributions of each feature with the proposed feature manipulation procedures. The results provide insightful views and plausible explanations of the DNN in device-free Wi-Fi indoor localization using channel state information (CSI) fingerprints.

On the robustness properties for maximum likelihood estimators of parameters in exponential power and generalized T distributions

Examining the robustness properties of maximum likelihood (ML) estimators of parameters in exponential power and generalized t distributions has been considered together. The well-known asymptotic properties of ML estimators of location, scale and added skewness parameters in these distributions are studied. The ML estimators for location, scale and scale variant (skewness) parameters are represented as an iterative reweighting algorithm (IRA) to compute the estimates of these parameters simultaneously. The artificial data are generated to examine performance of IRA for ML estimators of parameters simultaneously. We make a comparison between these two distributions to test the fitting performance on real data sets. The goodness of fit test and information criteria approve that robustness and fitting performance should be considered together as a key for modeling issue to have the best information from real data sets.

Inferring cosmic string tension through the neural network prediction of string locations in CMB maps

In previous work, we constructed a convolutional neural network used to estimate the location of cosmic strings in simulated cosmic microwave background temperature anisotropy maps. We derived a connection between the estimates of cosmic string locations by this neural network and the posterior probability distribution of the cosmic string tension $G\mu$. Here, we significantly improve the calculation of the posterior distribution of the string tension $G\mu$. We also improve our previous plain convolutional neural network by using residual networks. We apply our new neural network and posterior calculation method to maps from the same simulation used in our previous work and quantify the improvement.

Inline ECE measurements for NTM control on ASDEX Upgrade

The successful use of a tokamak for generating fusion power requires an active control of magnetic instabilities, such as neoclassical tearing modes (NTMs). Commonly, the NTM location is determined using electron cyclotron emission (ECE) and this is used to apply electron cyclotron heating (ECH) on the NTM location. In this paper, an inline ECE set-up at ASDEX Upgrade is presented in which ECE is measured and ECH is applied via the same path. First results are presented and a means to interpret the measurement data is given. Amplitude and phase with respect to a reference magnetic signal are calculated. Based on the amplitude and phase, the time of mode crossing is determined and shown to compare well with real-time estimates of the mode crossing time. The ECH launcher and flux surface geometries at ASDEX Upgrade, which are optimized for current drive by a beam path that is tangential to the flux surface near deposition, make it difficult to identify the mode crossing without inline ECE launcher movement. Therefore, NTM control based on inline ECE requires launcher movement to determine and maintain a reliable estimate of the NTM location.

Organ-mounted robot localization via function approximation.

Organ-mounted robots adhere to the surface of a mobile organ as a platform for minimally invasive interventions, providing passive compensation of physiological motion. This approach is beneficial during surgery on the beating heart. Accurate localization in such applications requires accounting for the heartbeat and respiratory motion. Previous work has described methods for modeling quasi-periodic motion of a point and registering to a static preoperative map. The existing techniques, while accurate, require several respiratory cycles to converge. This paper presents a general localization technique for this application, involving function approximation using radial basis function (RBF) interpolation. In an experiment in the porcine model in vivo, the technique yields mean localization accuracy of 1.25mm with a 95% confidence interval of 0.22mm. The RBF approximation provides accurate estimates of robot location instantaneously.

Uncertainty Quantification in Robust Inference for Irregularly Spaced Spatial Data Using Block Bootstrap

This paper deals with uncertainty quantification (UQ) for a class of robust estimators of population parameters of a stationary, multivariate random field that is observed at a finite number of locations s1,…, sn, generated by a stochastic design. The class of robust estimators considered here is given by the so-called M-estimators that in particular include robust estimators of location, scale, linear regression parameters, as well as the maximum likelihood and pseudo maximum likelihood estimators, among others. Finding practically useful UQ measures, both in terms of standard errors of the point estimators as well as interval estimation for the parameters is a difficult problem due to presence of inhomogeneous dependence among irregularly spaced spatial observations. Exact and asymptotic variances of such estimators have a complicated form that depends on the autocovariance function of the random field, the spatial sampling density, and also on the relative rate of growth of the sample size versus the volume of the sampling region. Similar complex interactions of these factors are also present in the sampling distributions of these estimators which makes exact calibration of confidence intervals impractical. Here it is shown that a version of the spatial block bootstrap can be used to produce valid UQ measures, both in terms of estimation of the standard error as well as interval estimation. A key advantage of the proposed method is that it provides valid approximations in very general settings without requiring any explicit adjustments for spatial sampling structures and without requiring explicit estimation of the covariance function and of the spatial sampling density.

Analysis of the Phase Transition Zone Location in the Flow Section of the PGU-450T Steam Turbine During Various Modes of Operation

An estimate of the phase transition zone location in the flow section of the T-150-7.7 steam turbine operating as part of a 450 MW two-circuit combined cycle gas turbine unit during various modes of operation is provided. Stages were identified, in which stress corrosion cracking of the disks, as well as corrosion fatigue of the rotating blades are most likely to appear.

PMU-Based Event Localization Technique for Wide-Area Power System

This paper presents an online event location estimating process in a wide-area power system using the phasor measurement unit (PMU). For real-world applications of PMU-based event location estimation (ELE), it is necessary to consider the insufficiency of PMU data caused by a lack of PMUs, communication failure, PMU malfunction, etc., because insufficient PMU data cannot guarantee event observability over the entire network. To ensure possible event locations in a real-world system, an estimation method is developed by combining offline zonal analysis and an online ELE algorithm. The K-mean clustering method is utilized for electrical-bus clustering, which enables us to recognize separate electrical zones (EZs). Then, we propose a two-stage ELE algorithm by estimating the event EZ and localizing the specified event section within the estimated EZ. Proposed ELE algorithm is applied to a real-world PMU data collected from southwestern part of the United States. Finally, successful results from a real-world PMU data application are presented at the end of this paper.

On designing CUSUM charts using ratio-type estimators for monitoring the location of normal processes

Control chart is one of the important tools in statistical process control (SPC) that plays a significant role in monitoring and identifying disturbances of any production process. The Shewhart, cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) are commonly used control charts for detecting process shifts. CUSUM and EWMA chart are more sensitive in detecting smaller shifts whereas the typical Shewhart chart is known to be only sensitive to large process shifts. The present study incorporates ratio-type estimators based on auxiliary information in CUSUM structure as a substitute of simple mean estimator for monitoring process location. These estimators are more efficient than the simple mean estimator in the presence of high correlation between the study and the auxiliary variables. Average run length (ARL), standard deviation of run length (SDRL) and extra quadratic loss (EQL) are used to measure the performances of the proposed charts. The performance comparison of the proposed chart with the existing CUSUM, CUSUM-FIR and many other control charts are made by using out-of-control ARL. The comparison revealed the superiority of the suggested charts over the other existing charts. An illustrative example is also provided for the performance evaluation of the proposed charts.

Peri-personal space as a prior in coupling visual and proprioceptive signals

It has been suggested that the integration of multiple body-related sources of information within the peri-personal space (PPS) scaffolds body ownership. However, a normative computational framework detailing the functional role of PPS is still missing. Here we cast PPS as a visuo-proprioceptive Bayesian inference problem whereby objects we see in our environment are more likely to engender sensations as they come near to the body. We propose that PPS is the reflection of such an increased a priori probability of visuo-proprioceptive coupling that surrounds the body. To test this prediction, we immersed participants in a highly realistic virtual reality (VR) simulation of their right arm and surrounding environment. We asked participants to perform target-directed reaches toward visual, proprioceptive, and visuo-proprioceptive targets while visually displaying their reaching arm (body visible condition) or not (body invisible condition). Reach end-points are analyzed in light of the coupling prior framework, where the extension of PPS is taken to be represented by the spatial dispersion of the coupling prior between visual and proprioceptive estimates of arm location. Results demonstrate that if the body is not visible, the spatial dispersion of the visuo-proprioceptive coupling relaxes, whereas the strength of coupling remains stable. By demonstrating a distance-dependent alteration in visual and proprioceptive localization attractive pull toward one another (stronger pull at small spatial discrepancies) when the body is rendered invisible – an effect that is well accounted for by the visuo-proprioceptive coupling prior – the results suggest that the visible body grounds visuo-proprioceptive coupling preferentially in the near vs. far space.

LOCALIZATION ALGORITHM FOR MULTILATERATION SYSTEMS

At present, multilateration systems are becoming increasingly important in air traffic control. This is due to their significant advantages in compare with secondary surveillance radar complexes. This article solves the problem of synthesizing an algorithm for object location estimation for multilateration system operating in passive mode. The synthesized algorithm is a combination of a procedure, the result of which is a rough estimate of the observed object coordinates, and an iterative algorithm specifying the resulting solution. The rough estimate is the result of solving a linear system of equations. The iterative refinement procedure is based on the linearization of the observational equations and does not require a large number of iterations. The paper provides a comparative statistical analysis of the proposed algorithm and the known Bancroft algorithm. For an objective analysis of two algorithms, the paper derives the Cramer-Rao boundary for the correlation matrix of estimates of the observed object coordinates, which makes it possible to determine the potential accuracy of the solution of the problem. It is shown that both algorithms allow obtaining estimates, the accuracy of which is close to the potentially achievable accuracy of the object location estimate. In contrast to the Bancroft algorithm, the rough estimate of the object location is unambiguous. This virtue reduces the total amount of computations during the algorithm implementation and reduces the probability of anomalous errors.

Testing the performance of earthquake early warning system in northern India

The main goal of present study is to test the functionality of an earthquake early warning (EEW) system (a life-saving tool), in India using synthesized data and recorded earthquake data from Taiwan. In recent time, India set up an EEW system in the central seismic gap along the Himalayan Belt, consisting of about 100 low-cost P-Alert instruments. The area, where these instruments are installed, is highly sensitive to the seismic risk with the potential of strong, major and great earthquakes. In the absence of recorded data from the Himalayas required for analysis of such system, we take advantage of recorded waveforms from Taiwan, to test the EEW system. We selected Taiwanese stations in good accordance with the Indian sensor network, to have a best fit in terms of inter station spacing. Finally, the recorded waveforms are passed through Earthworm software using tankplayer module. The system performs very well in terms of earthquake detection, P-wave picking, earthquake magnitude and location (using previously estimated regressions). Pd algorithm has been tested where the peak amplitude of vertical displacement is used for estimating magnitudes using previously regressed empirical relationship data. For the earthquakes located between Main Boundary Thrust and Main Central Thrust along with a matching instrumentation window, a good estimate of location, as well as magnitude is observed. The approach based on Pd for estimating magnitude works perfectly as compared to $$\tau_{\text{c}}$$ approach, which is more sensitive to signal-to-noise ratio. To make it more region specific, we generated synthetic seismograms from the epicenters of historical Chamoli (1999) and Uttarkashi (1991) earthquakes at EEW stations in India and checked the functionality of EEW. While placing these earthquakes within the instrumentation window, a good approximation of earthquake location and magnitude is obtained by passing these generated waveforms. The parameters used to judge the performance of EEW system included the time taken by the system in issuing warning after the confirmation of the occurrence of damaging earthquake and the lead time (time interval between the issuing of warning and arrival of damaging earthquake ground motion at a particular location). High lead times have been obtained for the plainer regions including thickly populated regions of Gangetic plains, such as Delhi National Capital Region according to the distance from the epicenter, which are the main target of EEW system.

Modeling AUV Localization Error in a Long Baseline Acoustic Positioning System

This paper develops a linearized Bayesian inversion algorithm for high-precision localization of an autonomous underwater vehicle (AUV) on a test range using time difference of arrival acoustic data. The Bayesian approach allows the uncertainties of several factors, including precise receiver locations and bias and lateral variability of the water-column sound-speed profile, to be included in the inversion as unknown parameters with varying levels of prior information. This provides better estimates of these parameters (and hence AUV locations), and includes the effect of their uncertainties in the uncertainty estimates for AUV locations. A modeling study comparing AUV localization uncertainties from the linearized inversion that results from nonlinear Monte Carlo analysis shows that linearization errors are small, and hence linearized analysis is used to efficiently map AUV localization uncertainty as a function of position over the test range. These approaches are applied to model localization uncertainty for an AUV test range associated with the Victoria Experimental Network Under the Sea cabled observatory system, examining the factors mentioned above as well as the effects of water-column refraction and receiver geometry.

On finite-sample robustness of directional location estimators

Robust location estimators for directional data are known for about 30 years. Scientific literature has focused on studying the asymptotic properties of these estimators like consistency and influence function. Apart from the finite-sample breakdown point, the finite-sample performance of robust directional location estimators has attracted less attention. Hence, it is discussed how the finite-sample max-bias of directional location estimators can be evaluated. Additionally, two new robust estimators of the mean direction are introduced: the spherical Minimum Covariance Determinant estimator (sMCD) and the spherical Minimum Spanning Tree estimator (sMST). The sMCD seeks to identify the densest subset of a given size while the sMST seeks for a well-separated subset. Finally, the robust estimators are compared with respect to the max-bias and to the bias under shift outlier scenarios by means of an extensive simulation study. The results indicate that –in contrast to linear data– the maximum likelihood estimator shows high robustness in terms of the finite-sample max-bias. However, robust estimators are clearly superior to the maximum likelihood estimator in shift outlier contamination schemes.

Uniaxial Acoustic Vector Sensors for direction-of-arrival estimation

In this paper, a specific reduced-channel Acoustic Vector Sensor (AVS) is proposed comprising one omni-directional microphone and only one particle velocity transducer, such that it can have an arbitrary orientation. Such a reduced transducer configuration is referred to as a Uniaxial AVS (U-AVS). The DOA performance of an array of U-AVSs is analyzed through its beampattern and compared to conventional configurations. It is shown that the U-AVS array beampattern results in an asymptotically biased estimate of the source location and it can be varied by choosing the orientation angles of the particle velocity transducers. Analytical expressions for the asymptotic bias of classical beamforming are proposed and verified both numerically as well as experimentally for Uniform Linear Arrays (ULAs). Furthermore, the Cramér-Rao Bound (CRB) and Mean Square Error (MSE) expressions are derived for a U-AVS array under a single source scenario and they are numerically evaluated for ULA. The implications of changing the orientations of the U-AVSs in the array on the MSE are discussed as well.

Tracking sperm whales (Physeter macrocephalus) detected with irregular time intervals

Passive acoustic monitoring (PAM) is a method that has been proved to be a powerful tool for monitoring sperm whales. Sperm whales frequently emit loud, short duration, clicks for the purposes of echolocation. The clicks are emitted at intervals of typically once per second, but the exact interval between pulses varies irregularly. To achieve the monitoring goals, it is necessary to localise the vocalising animal. This is typically achieved by detecting the animal’s echolocation clicks on a hydrophone array, estimating time delays between clicks and then employing a localisation algorithm. By adopting a tracking-based approach we are able to smooth the results, based on a model of whale motion, to remove noise and improve the estimates of location. The paper will consider the problem of tracking whales using the data from fixed bottom mounted sensors and using a tracking algorithm to follow the motion of such animals. The tracking will be implemented using a Kalman filter, but in this instance that filter needs to be designed to allow for data measured at irregular intervals. The method uses a version of the Kalman filter using a varying time-step. The time-step varying Kalman filter is applied to track data from simulated and measured datasets. The measured dataset is publicly available as part of the 2nd International Workshop on Detection and Localisation of Marine Mammals, Monaco, 2005 and comprises of a single animal moving within an array of 5 sensors.Passive acoustic monitoring (PAM) is a method that has been proved to be a powerful tool for monitoring sperm whales. Sperm whales frequently emit loud, short duration, clicks for the purposes of echolocation. The clicks are emitted at intervals of typically once per second, but the exact interval between pulses varies irregularly. To achieve the monitoring goals, it is necessary to localise the vocalising animal. This is typically achieved by detecting the animal’s echolocation clicks on a hydrophone array, estimating time delays between clicks and then employing a localisation algorithm. By adopting a tracking-based approach we are able to smooth the results, based on a model of whale motion, to remove noise and improve the estimates of location. The paper will consider the problem of tracking whales using the data from fixed bottom mounted sensors and using a tracking algorithm to follow the motion of such animals. The tracking will be implemented using a Kalman filter, but in this instance that filter...

Robustness of score-driven location and scale models to extreme observations: An application to the Chinese stock market

Recently, the use of dynamic conditional score (DCS) time series models are suggested in the body of literature on time series econometrics. DCS models are robust to extreme observations because those observations are discounted by the score function that updates each dynamic equation. Examples of the DCS models are the quasi-autoregressive (QAR) model and the Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model, which measure the dynamics of location and scale, respectively, of the dependent variable. Both QAR and Beta-t-EGARCH discount extreme observations according to a smooth form of trimming. Classical dynamic location and scale models (for example, the AR and the GARCH models) are sensitive to extreme observations. Thus, the AR and the GARCH modelsmay provide imprecise estimates of location and scale dynamics. In the application presented in this paper, we use data from the Shanghai Stock Exchange A-Share Index and the Shenzhen Stock Exchange A-Share Index for the period of 5th January 1998 to 29th December 2017. For the corresponding stock index return time series, a relatively high number of extreme values are observed during the sample period. We find that the statistical performance of QAR plus Beta-t-EGARCH is superior to that of AR plus t-GARCH, due to the robustness of QAR plus Beta-t-EGARCH to extreme unexpected returns.

Aspect-Aware Target Detection and Localization by Wireless Sensor Networks.

This paper considers the active detection of a stealth target with aspect dependent reflection (e.g., submarine, aircraft, etc.) using wireless sensor networks (WSNs). When the target is detected, its localization is also of interest. Due to stringent bandwidth and energy constraints, sensor observations are quantized into few-bit data individually and then transmitted to a fusion center (FC), where a generalized likelihood ratio test (GLRT) detector is employed to achieve target detection and maximum likelihood estimation of the target location simultaneously. In this context, we first develop a GLRT detector using one-bit quantized data which is shown to outperform the typical counting rule and the detection scheme based on the scan statistic. We further propose a GLRT detector based on adaptive multi-bit quantization, where the sensor observations are more precisely quantized, and the quantized data can be efficiently transmitted to the FC. The Cramer-Rao lower bound (CRLB) of the estimate of target location is also derived for the GLRT detector. The simulation results show that the proposed GLRT detector with adaptive 2-bit quantization achieves much better performance than the GLRT based on one-bit quantization, at the cost of only a minor increase in communication overhead.

Weighted likelihood estimation of multivariate location and scatter

A novel approach to obtain weighted likelihood estimates of multivariate location and scatter is discussed. A weighting scheme is proposed that is based on the univariate distribution of the Mahalanobis distances rather than the multivariate distribution of the data at the assumed model. This strategy allows to avoid the curse of dimensionality affecting multivariate non-parametric density estimation, that is involved in the construction of the weights through the Pearson residuals. Asymptotic properties of the proposed weighted likelihood estimator are also discussed. Then, weighted likelihood-based outlier detection rules and robust dimensionality reduction techniques are developed. The effectiveness of the methodology is illustrated through some numerical studies and real data examples.