Estimate Of Location Research Articles

Design and Analysis of a Non-Iterative Estimator for Target Location in Multistatic Sonar Systems with Sensor Position Uncertainties

Target location is the basic application of a multistatic sonar system. Determining the position/velocity vector of a target from the related sonar observations is a nonlinear estimation problem. The presence of possible sensor position uncertainties turns this problem into a more challenging hybrid parameter estimation problem. Conventional gradient-based iterative estimators suffer from the problems of initialization difficulties and local convergence. Even if there is no problem with initialization and convergence, a large computational cost is required in most cases. In view of these drawbacks, we develop a computationally efficient non-iterative position/velocity estimator. The main numerical computation involved is the weighted least squares optimization, which makes the estimator computationally efficient. Parameter transformation, model linearization and two-stage processing are exploited to prevent the estimator from iterative computation. Through performance analysis and experimental verification, we find that the proposed estimator reaches the hybrid Cramér–Rao bound and has linear computational complexity.

A Semidefinite Relaxation Method for Elliptical Location

Wireless location is a supporting technology in many application scenarios of wireless communication systems. Recently, an increasing number of studies have been conducted on range-based elliptical location in a variety of backgrounds. Specifically, the design and implementation of position estimators are of great significance. The difficulties arising from implementing a maximum likelihood estimator for elliptical location come from the nonconvexity of the negative log-likelihood functions. The need for computational efficiency further enhances the difficulties. Traditional algorithms suffer from the problems of high computational cost and low initialization justifiability. On the other hand, existing closed-form solutions are sensitive to the measurement noise levels. We recognize that the root of these drawbacks lies in an oversimplified linear approximation of the nonconvex model, and accordingly design a maximum likelihood estimator through semidefinite relaxation for elliptical location. We relax the elliptical location problems to semidefinite programs, which can be solved efficiently with interior-point methods. Additionally, we theoretically analyze the complexity of the proposed algorithm. Finally, we design and carry out a series of simulation experiments, showing that the proposed algorithm outperforms several widely used closed-form solutions at a wide range of noise levels. Extensive results under extreme noise conditions verify the deployability of the algorithm.

Neuronal population correlates of target selection and distractor filtering

Frontal Eye Field (FEF) neurons discriminate between relevant and irrelevant visual stimuli and their response magnitude predicts conscious perception. How this is reflected in the spatial representation of a visual stimulus at the neuronal population level is unknown. We recorded neuronal population activity in the FEF while monkeys were performing a forced choice cued detection task with identical target and distractor stimuli. We quantified, using machine learning techniques, estimates of target and distractor location from FEF population multiunit activities. We found that the FEF population activity provides a precise single trial estimate of reported stimuli locations. Importantly, the closer this prefrontal population single trial estimate is to the veridical stimulus location, the higher the probability that the target or the distractor is reported as perceived. We show that stimulus perception is rescued by the estimate of attention allocation specifically when the latter is close enough to the actual stimulus location, thus indicating a partial independence between attention and perception. Overall, we thus show that how and what we perceive of our environment depends on the spatial precision with which this environment is coded by prefrontal neuronal populations.

Modeling positive COVID-19 cases in Bandung City by means geographically weighted regression

Coronavirus disease 2019 (COVID-19) has rocked the world since the beginning of 2020, Indonesia is no exception. Bandung, as one of the metropolitan cities and one of the largest cities in West Java, has been exposed to the virus since early March 2020, among the impacts is the occurrence of panic buying, mass unemployment due to layoffs, and increased crime rates. The statistical analysis used for modeling the number of COVID-19 cases is the Geographically Weighted Regression (GWR) model. The GWR model is the development of a linear regression model that produces local model parameter estimators for each observation location. The aim of the study was to model the number of COVID-19 cases in the period of March to June 4, 2020, in Bandung. The results showed that variations in the variable population size, distance to the capital city, number of ODP, number of PDP, and number of health facilities in the GWR model were able to explain variations in the number of positive cases of COVID-19 in Bandung City.

Uniform high-order convergence of multiscale finite element computation on a graded recursion for singular perturbation

<p style='text-indent:20px;'>A multiscale finite element method is proposed for addressing a singularly perturbed convection-diffusion model. In reference to the a priori estimate of the boundary layer location, we provide a graded recursion for the mesh adaption. On this mesh, the multiscale basis functions are able to capture the microscopic boundary layers effectively. Then with a global reduction, the multiscale functional space may efficiently reflect the macroscopic essence. The error estimate for the adaptive multiscale strategy is presented, and a high-order convergence is proved. Numerical results validate the robustness of this novel multiscale simulation for singular perturbation with small parameters, as a consequence, high accuracy and uniform superconvergence are obtained through computational reductions.</p>

ANALYSIS OF SPRITES FROM VIDEO OBSERVATIONS MADE BY THE CROATIAN METEOR NETWORK

Sprites are optical phenomena belonging to the category of transient luminous events. They were observed using cam- eras of the Croatian Meteor Network. The sprite images extracted from the recordings from cameras located in Rijeka and Visnjan were analysed using two methods. Sprites recorded on the night of 2017/05/31 at 21:46:18.781 and 22:03:27.299 only from the camera in Rijeka were analysed using the single-station analysis method. This method works on the as- sumption that sprites occur directly above the parent lightning and uses the lightning strike location provided by the Blitzortung website together with camera recordings from one station to make the spatial positioning of the sprite pos- sible. Sprites recorded on the night of 2017/05/31 at 21:57:44.376 and 22:07:16.440 from both cameras in Rijeka and Visnjan were analysed using the double-station analysis method, which is based on the lines of sight method. Both double-station sprites were also analysed using the single-station analysis method. The resulting heights and lateral distances were compared. It was concluded that this method of single-station analysis may provide a good estimate of the sprite location in case of small lateral distances between the sprite and parent lightning, while in other cases, errors may be significant. The double-station analysis method is preferred whenever possible, as it is mathematically exact and the errors arising from it depend only on the quality of the observation data.

Experimental estimates of angular coordinates and location of radio sources by highly mobile detector direction finders

Using the panoramic digital detector direction finder located onboard the pilotless aircraft of plane type of small range in the conditions of a radio measuring polygon experimental estimates of angular data and location of the sources of a radio emission functioning at fixed frequencies and in the mode of program reorganization of operating frequency are carried out. Angular data of objects decided by a phase method on use of procedures of fast Fourier transform, synchronous spectrum analysis of signals in receiving channels and elimination of ambiguity of measurements of attacks of phases by finding of indicators of their multiplicity. At a fixing of radiators the choice of a flight trajectory of the pilotless aircraft by criterion of a minimum of mean square errors of estimates of location was carried out; options of the movement of the carrier of the direction finder equipment on an arc with the maximum corner of a notch of a bearing are implemented and filched at a task of course corners in turning points of a route on the basis of a posteriori values of dispersion of coordinates of objects. The analysis of mean square measurement errors of angular data and fixing of simulators of the sources of a radio emission executed on the basis of standard signal generators, and radio stations in frequency range 30…3000 MHz is carried out.

A Novel Estimator for TDOA and FDOA Positioning of Multiple Disjoint Sources in the Presence of Calibration Emitters

Multiple-target localization is extensively applied in wireless connected networks. However, sensor location uncertainty is known to degrade significantly the target localization accuracy. Fortunately, calibration emitters such as unmanned aerial vehicles (UAV) with known location can be used to reduce the loss in localization accuracy due to sensor location errors. This paper is devoted to the use of UAV calibration emitters for time differences of arrival (TDOA) and frequency differences of arrival (FDOA) positioning of multiple targets. The study starts with deriving the Cramer-Rao bound (CRB) for TDOA/FDOA-based target location estimate when several UAV calibration signals are available. Subsequently, the paper presents an iterative constrained weighted least squares (ICWLS) estimator for multiple-target joint localization using TDOA/FDOA measurements from both target sources and UAV calibration emitters. The newly proposed method consists of two stages. In the first phase, the sensor locations are refined based on the calibration measurements as well as the prior knowledge of sensor locations. The second step provides the estimate of multiple-target locations by combining the measurements of target signals as well as the estimated values in the first phase. An efficient ICWLS algorithm is presented at each stage. Both the two algorithms are implemented by using matrix singular value decomposition (SVD), which is able to provide a closed-form solution and update the weighting matrix at every iteration. Finally, the convergence behavior and estimation mean-square-error (MSE) of the new estimator are deduced. Both theoretical analysis and simulation results show that the developed method can improve the TDOA/FDOA localization accuracy obviously with the help of UAV calibration emitters.

Short-term earthquake prediction in Kamchatka using low-frequency magnetic fields

This paper is devoted to a method of short-term earthquake (EQ) prediction in Kamchatka, Russia. Properties of low-frequency magnetic fields are the basics of the method, and we used two seismo-electromagnetic phenomena in the EQ prediction: 1. seismo-ionospheric depression in the frequency range of 0.01–0.1 Hz (ULF depression), 2. seismo-atmospheric radiation in the frequency range of 1–30 Hz (ULF/ELF radiation). It is now generally accepted that gas eruption before an EQ causes these ULF/ELF phenomena. We propose a hypothesis that gas emanates from the area in the bottom of Kuril–Kamchatka or Aleutian trenches closest to the epicenter of a forthcoming EQ. The three parameters of an EQ are (i) when (time), (ii) where (position) a next EQ is coming with (iii) how big (magnitude) in the short-term EQ prediction. Position of the source of atmospheric radiation gives an estimate of the epicenter location. Then, we estimate the local magnitude in consequence of its statistical dependence on ULF depression and epicenter distance. Date of a coming EQ is determined by the statistical dependence of delays of EQs relative to the dates of their precursors. The result of application of this method to real magnetic field data is illustrated by official prediction processes during a period of March–May 2016. Limits and possible errors of the method as well as methods to enhance the reliability of the prediction are discussed.

Generalized Bayesian shrinkage and wavelet estimation of location parameter for spherical distribution under balance-type loss: Minimaxity and admissibility

One of the most important subject in multivariate analysis is parameters estimation. Among different methods, the shrinkage estimation is of interest. In this paper we consider the generalized Bayes shrinkage estimator of location parameter for spherical distribution under balance-type loss. We assume that the random vector having a spherical symmetric distribution with the known scalar variational component. Also, we find minimax and admissible estimator of location parameter based on generalized Bayes estimator. We investigate wavelet generalized Bayes estimator of location under balance-LINEX loss function. At the end, the performance evaluation of the proposed class of estimators is checked through a simulation study.

Measurement Extraction for Two Closely-Spaced Objects Using an Imaging Sensor

This paper considers measurement extraction for two closely-spaced objects with unknown equal intensities in an imaging sensor's focal plane array (FPA). Given a screen of FPA data, the first part of the measurement extractor, target location estimator, can extract the location estimates for two targets or one, with the corresponding accuracy given by the Cramer Rao lower bound (CRLB). The second part of the measurement extractor, target detector, selects among the hypotheses of two resolved targets and a single one using information-theoretic criteria and hypothesis tests. Simulation results have been conducted to evaluate the measurement extraction performance including the probability of resolving the two hypotheses, and the efficiency and unbiasedness of the target location estimates for the selected hypothesis using different hypothesis detection schemes. The generalized likelihood ratio test (GLRT) based on linearized observation model using second order Taylor series expansion is most appealing as it provides an explicit expression of the probability of detecting two targets as a function of the target separations, the signal-to-noise ratio at a given false resolution probability. It is shown that the simulation-based resolution performance for the GLRT using the estimated center location of the two targets matches well with the analytic performance assuming known center.

STUPEFY: Set-Valued Box Particle Filtering for Bluetooth Low Energy-Based Indoor Localization

With the rapid emergence of Internet of Things (IoT), we are more and more surrounded by smart connected devices with integrated sensing, processing, and communication capabilities. Bluetooth Low Energy (BLE), referred to as Bluetooth Smart, is considered as the main-stream technology to perform identification and localization/tracking in IoT applications. While single-model BLE-based tracking has been investigated from different aspects, application of multi-model (hybrid) solutions are still in their infancy. In this regard, the letter proposes a novel BLE-based tracking framework, referred to as the STUPEFY, which incorporates set-valued information within box particle filtering context. More specifically, the proposed multiple-model STUPEFY framework consists of three integrated modules, i.e., an intriguing Smoothing Module based on Kalman filtering to reduce the Received Signal Strength Indicator (RSSI) fluctuations and facilitate comparison of Gaussian models of the RSSI values in distribution with the learned ones; A learning-based model (Coordination Module) utilized in an intuitive fashion to provide/construct a coarse estimate of the target's location together with the smallest axes-aligned box containing the ellipsoid associated with each zone's learned RSSI distribution, and; A novel set-valued box particle filtering (SBPF) approach (Micro-Localization Module). The proposed STUPEFY framework is evaluated based on real BLE datasets and results illustrate significant potentials in terms of improving overall BLE-based achievable tracking accuracy.

Acoustic localization of crows in pre-roost aggregations.

Crows are highly intelligent and social creatures. Each night during the non-breeding period, they gather on large pre-roost aggregations as they move towards their communal roost where they sleep. Crows make numerous and varied vocalizations on these pre-roost aggregations, but the purpose of these calls, and vocal communication in general, in these pre-roost aggregations is not fully understood. In this paper, an array of four microphones is used as a non-intrusive means to observe crow vocal behavior in pre-roost aggregations in the absence of human observers. By passively localizing animal vocalizations, the location of individuals can be monitored while simultaneously recording the acoustic structure and organization of their calls. Simulations and experiment are undertaken to study the performance of two time difference of arrival-based methods (hyperbolic location estimator and maximum likelihood estimator) for call localization. The effect of signal-to-noise ratio and uncertainty in measurement on the localization error is presented. By describing, modeling, and testing these techniques in this innovative context, the authors hope that researchers will employ the authors' approaches in future empirical studies to more fully understand crow vocal behavior.

Implementing a Distance Estimator for a Wildlife Tracking System Based on 802.15.4

In this work, a novel distance estimation mechanism using received signal strength indication (RSSI) signals with ZigBee modules is designed, implemented and tested in several scenarios. This estimator was used for a research project focused on a wildlife behavioral classification system deployed in Doñana’s National Park. As a supporting feature for that project, this work was implemented for locating animal’s collars acting as wireless nodes in order to find those who went outside of the coverage area of the network or that were accidentally detached from animals. This work describes the system architecture and the implementation of a mobile assistant capable of recovering devices located beyond the coverage of the network. The analytical model needed for distance estimation and the signal filtering are described, as well as the difficulties that the researchers must deal when building robust location estimators. This theoretical model was applied to three different scenarios and tested with two validation experiments.

Independent estimate of asteroid 433 Eros' spin state using surface Doppler from the NEAR-Shoemaker mission

This paper demonstrates the viability of using Doppler collected from an asteroid's surface to precisely estimate the asteroid's rotation state. This technique is applied to archived data from the NEAR-Shoemaker mission. At the end of the NEAR mission, the spacecraft landed on asteroid 433 Eros, and 50 h of two-way Doppler data was subsequently received through the DSN. This data was originally used to determine the landing location of the spacecraft but was never incorporated into estimates of Eros' rotation state. The surface Doppler yields a precise estimate of Eros', with an estimated right ascension of 11.364 ± 0.001° and declination of 17.234 ± 0.001°. No wobble greater than 0.001° has been detected. Additionally, the body-fixed landing location of the NEAR spacecraft is also re-estimated purely from the Doppler data. The estimated landing location shows good agreement with the candidate site determined by the navigation team. When the spacecraft is constrained to lay on the highest resolution shape model, the most precise estimate of the landing location is achieved which is latitude = 41.28 ± 0.40° S, East longitude = 80.34 ± 0.05° W, radius = 6.57 ± 0.04 km.

Application of Iterative Maximum Weighted Likelihood Estimation in 3-D Target Localization

Time-of-flight (ToF)-based 3-D target localization is a very challenging topic because of the pseudo-targets introduced by ToF measurement errors in traditional ToF-based methods. Although the influence of errors in ToF measurement can be reduced by the probability-based ToF method, the accuracy of localization is not very high. This paper proposes a new 3-D target localization method, Iterative Maximum Weighted Likelihood Estimation (IMWLE), that takes into account the spatial distribution of pseudo-targets. In our method, each pseudo-target is initially assigned an equal weight. At each iteration, Maximum Weighted Likelihood Estimation (MWLE) is adopted to fit a Gaussian distribution to all pseudo-target positions and assign new weight factors to them. The weight factors of the pseudo-targets, which are far from the target, are reduced to minimize their influence on localization. Therefore, IMWLE can reduce the influence of pseudo-targets that are far from the target and improve the accuracy of localization. The experiments were carried out in a water tank to test the performance of the IMWLE method. Results revealed that the estimated target area can be narrowed down to the target using IMWLE and a point estimate of target location can also be obtained, which shows that IMWLE has a higher degree of accuracy than the probability-based ToF method.

Effect of Sensor Motion on Time Delay and Doppler Shift Localization: Analysis and Solution

This paper investigates active localization of a stationary object using time delay alone or together with Doppler shift measurements observed by a number of dynamic monostatic sensors having collocated transmitters and receivers, where the sensor motion during the interrogation period is not negligible. Non-negligible motion effect appears when the sensor speed relative to the signal propagation speed is significant, such as in an acoustic or an underwater environment. The motion effect causes the signal sent and received locations different, with their separation proportional to the two-way propagation time delay that is dependent on the object location. We first provide new measurement models for time delay and Doppler shift in the presence of sensor motion. They come as recursive equations and their non-recursive forms are derived. Analysis of the motion effect based on the Cramer-Rao lower bound (CRLB) is conducted, and the performance loss in terms of the location bias and variance when ignoring the sensor motion effect is evaluated. Two kinds of estimators for the object location are next proposed by exploiting the non-recursive forms of the model equations. One is a closed-form solution by algebraic evaluation and the other is a semi-definite relaxation solution by convex optimization. The first is computationally efficient and is shown to achieve the CRLB performance over the small error region under Gaussian noise, while the second can handle the positioning problem better when the noise level is high. Simulations validate the theoretical analysis and the performance of the proposed estimators.

Reconstructing historical exposures to elongate mineral particles (EMPs) in the taconite mining industry for 1955–2010

As part of ongoing epidemiological studies for assessing the association between exposure to dust from taconite operations and the development of respiratory diseases, the goal of this study was to reconstruct the exposures of workers to elongate mineral particle (EMP) in the Minnesota taconite mining industry from 1955–2010. Historical NIOSH-7400 and equivalent EMP personal exposure data were extracted from two sources: (1) the Mine Safety and Health Administration (MSHA) online database recorded for all inspection results since 1978 with 655 EMP monitoring records from 1978–2010 for 13 MSHA Mine IDs associated with this study; and (2) the mining companies’ internal monitoring reports contained 96 personal EMP exposure records. NIOSH-7400 EMP personal exposures were measured for workers in different jobs in all active mines in 2010 by obtaining 1,285 personal samples. After data treatment, all data were grouped into seven mines and eight departments. Within each mine-department, the yearly EMP mean concentration in f/cc for each year of operation was predicted using two approaches. The performance of two approaches varied by situation. The assumptions underlying each approach described in this article have limitations. A linear regression based on limited historical measurements and those made in 2010–2011 (Approach 1) does not yield reasonable and plausible values of the slope. Approach 2 assumes that the EMP and the respirable dust in the same department share the same historical time trend. This approach allowed us to avail of the more reasonable slope estimates from the historical respirable dust data set and yielded more plausible historical exposure estimates for most locations. This work with two different job exposure matrix (JEMs) provides a unique research opportunity to study the potential impact of exposure assessment to epidemiological results. Both JEMs are being used to assess associations between EMP and respiratory disease in epidemiological studies.

Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks

The use of Computed Tomography (CT) imaging for patients with stroke symptoms is an essential step for triaging and diagnosis in many hospitals. However, the subtle expression of ischemia in acute CT images has made it hard for automated methods to extract potentially quantifiable information. In this work, we present and evaluate an automated deep learning tool for acute stroke lesion core segmentation from CT and CT perfusion images. For evaluation, the Ischemic Stroke Lesion Segmentation (ISLES) 2018 challenge dataset is used that includes 94 cases for training and 62 for testing. The presented method is an improved version of our workshop challenge approach that was ranked among the workshop challenge finalists. The introduced contributions include a more regularized network training procedure, symmetric modality augmentation and uncertainty filtering. Each of these steps is quantitatively evaluated by cross-validation on the training set. Moreover, our proposal is evaluated against other state-of-the-art methods with a blind testing set evaluation using the challenge website, which maintains an ongoing leaderboard for fair and direct method comparison. The tool reaches competitive performance ranking among the top performing methods of the ISLES 2018 testing leaderboard with an average Dice similarity coefficient of 49%. In the clinical setting, this method can provide an estimate of lesion core size and location without performing time costly magnetic resonance imaging. The presented tool is made publicly available for the research community.

FPR—Fast Path Risk Algorithm to Evaluate Collision Probability

As mobile robots and autonomous vehicles become increasingly prevalent in human-centred environments, there is a need to control the risk of collision. Perceptual modules, for example machine vision, provide uncertain estimates of object location. In that context, the frequently made assumption of an exactly known free-space is invalid. Clearly, no paths can be guaranteed to be collision free. Instead, it is necessary to compute the probabilistic risk of collision on any proposed path. The FPR algorithm, proposed here, efficiently calculates an upper bound on the risk of collision for a robot moving on the plane. That computation orders candidate trajectories according to (the bound on) their degree of risk. Then paths within a user-defined threshold of primary risk could be selected according to secondary criteria such as comfort and efficiency. The key contribution of this letter is the FPR algorithm and its `convolution trick' to factor the integrals used to bound the risk of collision. As a consequence of the convolution trick, given K obstacles and N candidate paths, the computational load is reduced from the naive O(NK), to the qualitatively faster O(N + K).