Distance Estimation Research Articles

Estimating the Distance Between Map Points May Be Related to Forms of Representing the Map´s Scale.

The way a map scale is represented plays a key role in comprehending it. In this research, we examined the relationships between the form of representation of the map's scale (numerical scale, linear graphic scale and circular graphic scale), the user's gender, and the distance between two map objects on user estimates of the distance between two objects on the map (i.e., a 5, 10, 15 and 20-min walk). We gave 183 college students 84 maps of three types: 28 numerical scale, 28 linear graphic scale, and 28 circular graphic scale. Each map presented varied distances to be estimated. We assessed the participants' accurate hits, errors, and their hits minus errors in these distance estimations. Participants had both more hits and more errors on circular scale maps than on linear or numerical scale maps, and the distances between objects and gender also significantly related to the estimated e distance. Both the type of scale and the distance between objects influenced the number of hits minus the number of errors.

Testing kinematic distances under a realistic Galactic potential. Investigating systematic errors in the kinematic distance method arising from a non-axisymmetric potential

Obtaining reliable distance estimates to gas clouds within the Milky Way is challenging in the absence of certain tracers. The kinematic distance approach has been used as an alternative, and it is derived from the assumption of circular trajectories around the Galactic centre. Consequently, significant errors are expected in regions where gas flow deviates from purely circular motions. We aim to quantify the systematic errors that arise from the kinematic distance method in the presence of a Galactic potential that is non-axisymmetric. We investigated how these errors differ in certain regions of the Galaxy and how they relate to the underlying dynamics. We performed 2D isothermal hydrodynamical simulation of the gas disk with the moving-mesh code Arepo adding the capability of using an external potential provided by the Agama library for galactic dynamics. We introduced a new analytic potential of the Milky Way, taking elements from existing models and adjusting parameters to match recent observational constraints. In line with results of previous studies, we report significant errors in the kinematic distance estimate for gas close to the Sun along sight lines towards the Galactic centre and anti-centre and associated with the Galactic bar. Kinematic distance errors are low within the spiral arms, as gas resides close to local potential minima and the resulting line-of-sight velocity is similar to what is expected for an axisymmetric potential. Interarm regions exhibit large deviations at any given Galactic radius, and this is caused by the gas being sped up or slowed down as it travels into or out of spiral arms. In addition, we identify ‘zones of avoidance’ in the $lv$-diagram, where the kinematic distance method is particularly unreliable and should only be used with caution and we find a power-law relation between the kinematic distance error and the deviation of the projected line-of-sight velocity from circular motion.

A bio-inspired looming detection for stable landing in unmanned aerial vehicles**This research was funded by the National Natural Science Foundation ofchina, grant number 62206065, and the Bagui Scholar Program of Guangxi.

Flying insects, such as flies and bees, have evolved the capability to rely solely on visual cues for smooth and secure landings on various surfaces. In the process of carrying out tasks, micro unmanned aerial vehicles (UAVs) may encounter various emergencies, and it is necessary to land safely in complex and unpredictable ground environments, especially when altitude information is not accurately obtained, which undoubtedly poses a significant challenge. Our study draws on the remarkable response mechanism of the Lobula Giant Movement Detector to looming scenarios to develop a novel UAV landing strategy. The proposed strategy does not require distance estimation, making it particularly suitable for payload-constrained micro aerial vehicles. Through a series of experiments, this strategy has proven to effectively achieve stable and high-performance landings in unknown and complex environments using only a monocular camera. Furthermore, a novel mechanism to trigger the final landing phase has been introduced, further ensuring the safe and stable touchdown of the drone.

The structure of the stellar halo of the Andromeda galaxy explored with the NB515 for Subaru/HSC. I.: New insights on the stellar halo up to 120 kpc

Abstract We analyse the M31 halo and its substructure within a projected radius of 120 kpc using a combination of Subaru/HSC NB515 and CFHT/MegaCam g- & i-bands. We succeed in separating M31’s halo stars from foreground contamination with ∼ 90 % accuracy by using the surface gravity sensitive NB515 filter. Based on the selected M31 halo stars, we discover three new substructures, which associate with the Giant Southern Stream (GSS) based on their photometric metallicity estimates. We also produce the distance and photometric metallicity estimates for the known substructures. While these quantities for the GSS are reproduced in our study, we find that the North-Western stream shows a steeper distance gradient than found in an earlier study, suggesting that it is likely to have formed in an orbit closer to the Milky Way. For two streams in the eastern halo (Stream C and D), we identify distance gradients that had not been resolved. Finally, we investigate the global halo photometric metallicity distribution and surface brightness profile using the NB515-selected halo stars. We find that the surface brightness of the metal-poor and metal-rich halo populations, and the all population can be fitted to a power-law profile with an index of α = −1.65 ± 0.02, −2.82 ± 0.01, and −2.44 ± 0.01, respectively. In contrast to the relative smoothness of the halo profile, its photometric metallicity distribution appears to be spatially non-uniform with nonmonotonic trends with radius, suggesting that the halo population had insufficient time to dynamically homogenize the accreted populations.

Differential Positioning with Bluetooth Low Energy (BLE) Beacons for UAS Indoor Operations: Analysis and Results

Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the RSSI (received signal strength indicator) for distance estimation and positioning. Distance information from measured RSSI values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the RSSI and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the RSSI and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique.

SFMD‐X: A New Functional Data Classifier Based on Shrinkage Functional Mahalanobis Distance

ABSTRACTIn this article, we propose a novel classification approach for functional data based on a shrinkage estimate of functional Mahalanobis distance. We first introduce a new shrinkage functional Mahalanobis distance (SFMD), by using this new distance, we transform the functional observations into a set of vector‐valued pseudo‐samples. Furthermore, we adopt some good classification algorithms designed for multivariate data to this pseudo‐samples instead of the original functional data. The new approach has advantage of highly flexible and scalable, that is, it can easily combine with any classification algorithm, such as support vector machine, tree‐based methods, and neural networks. We demonstrate the performance of the proposed functional classifier through both extensive simulation studies and two real data applications.

Variational inference for acceleration of SN Ia photometric distance estimation with BayeSN

Abstract Type Ia supernovae (SNe Ia) are standarizable candles whose observed light curves can be used to infer their distances, which can in turn be used in cosmological analyses. As the quantity of observed SNe Ia grows with current and upcoming surveys, increasingly scalable analyses are necessary to take full advantage of these new datasets for precise estimation of cosmological parameters. Bayesian inference methods enable fitting SN Ia light curves with robust uncertainty quantification, but traditional posterior sampling using Markov Chain Monte Carlo (MCMC) is computationally expensive. We present an implementation of variational inference (VI) to accelerate the fitting of SN Ia light curves using the BayeSN hierarchical Bayesian model for time-varying SN Ia spectral energy distributions (SEDs). We demonstrate and evaluate its performance on both simulated light curves and data from the Foundation Supernova Survey with two different forms of surrogate posterior – a multivariate normal and a custom multivariate zero-lower-truncated normal distribution – and compare them with the Laplace Approximation and full MCMC analysis. To validate of our variational approximation, we calculate the pareto-smoothed importance sampling (PSIS) diagnostic, and perform variational simulation-based calibration (VSBC). The VI approximation achieves similar results to MCMC but with an order-of-magnitude speedup for the inference of the photometric distance moduli. Overall, we show that VI is a promising method for scalable parameter inference that enables analysis of larger datasets for precision cosmology.

On the accuracy of distance estimates by propagation time of sound signals on the arctic shelf

As part of numerical modeling, estimates are made of the accuracy of determining the distance between underwater sources and sound receivers located at a distance of several kilometers from each other in the Kara Sea in the autumn. It is assumed that the main source of possible errors in determining the distance is the lack of accurate data on the vertical profile of the sound speed along the propagation path of acoustic signals. Data from September and November were analyzed, in the interval between which significant changes in the profile take place, when the vertical gradient of sound speed changes from negative to positive values. Characteristic values of sound speed variations were obtained by statistical processing of hydrological data taken from the World Ocean Database. The results obtained are important for analyzing the capabilities of underwater acoustic navigation.

The red giant branch tip in the SDSS, PS1, JWST, NGRST, and Euclid photometric systems

We used synthetic photometry from Gaia DR3 BP and RP spectra for a large selected sample of stars in the Large Magellanic Cloud (LMC) and Small Magellanic Cloud (SMC) to derive the magnitude of the red giant branch (RGB) tip for these two galaxies in several passbands across a range of widely used optical photometric systems, including those of space missions that have not yet started their operations. The RGB tip is estimated by fitting a well motivated model to the RGB luminosity function (LF) within a fully Bayesian framework, allowing for a proper representation of the uncertainties of all the involved parameters and their correlations. By adopting the best available distance and interstellar extinction estimates, we provide a calibration of the RGB tip as a standard candle for the following passbands: Johnson-Kron-Cousins I (mainly used for validation purposes), Hubble Space Telescope F814W, Sloan Digital Sky Survey i and z, PanSTARRS 1 y, James Webb Space Telescope F090W, Nancy Grace Roman Space Telescope Z087, and Euclid IE, with an accuracy within a few per cent, depending on the case. We used theoretical models to explore the trend of the absolute magnitude of the tip as a function of colour in the different passbands (beyond the range spanned by the LMC and SMC), as well as its dependency on age. These calibrations can be very helpful to obtain state-of-the-art RGB tip distance estimates to stellar systems in a very large range of distances directly from data in the natural photometric system of these surveys and/or missions, without recurring to photometric transformations. We have made the photometric catalogues publicly available for calibrations in additional passbands or for different approaches in the estimate of the tip, as well as for stellar populations and stellar astrophysics studies that may take advantage of large and homogeneous datasets of stars with magnitudes in 22 different passbands.

Joint DOA and Distance Estimation via Spatial Diversity Wideband Signal Synthesis Calibrated by Distance Parameter

Joint DOA and Distance Estimation via Spatial Diversity Wideband Signal Synthesis Calibrated by Distance Parameter

Visual Perceptual Confidence: Exploring Discrepancies Between Self-reported and Actual Distance Perception In Virtual Reality.

Virtual Reality (VR) systems are widely used, and it is essential to know if spatial perception in virtual environments (VEs) is similar to reality. Research indicates that users tend to underestimate distances in VR. Prior work suggests that actual distance judgments in VR may not always match the users self-reported preference of where they think they most accurately estimated distances. However, no explicit investigation evaluated whether user preferences match actual performance in a spatial judgment task. We used blind walking to explore potential dissimilarities between actual distance estimates and user-selected preferences of visual complexities, VE conditions, and targets. Our findings show a gap between user preferences and actual performance when visual complexities were varied, which has implications for better visual perception understanding, VR applications design, and research in spatial perception, indicating the need to calibrate and align user preferences and true spatial perception abilities in VR.

A novel quantitative particle distance estimation method of low water/binder cementitious composites (LW/B-CC): Modeling and verification

This paper intends to employ water film theory and 1H Low-field NMR (Lf-NMR) to quantitatively evaluate the particle packing state of low water/binder cementitious composite (LW/B-CC) at different scales. The specific approach involves establishing particle distance models to evaluate the macroscopic, mesoscopic, and microscopic particle packing states of LW/B-CC samples using water film theory and Lf-NMR. The results indicate that Macro_PD is positively related to the product of plastic viscosity and distance of aggregate segregation, Meso_PD shows negatively related to the elastic modulus of ITZ. And Micro_PD can indicate different hydration period. Finally, a multiple linear regression analysis is used to establish a quantitative relationship between PD of different scales and compressive strength. The established particle distance models at different scales are proved to have significant guiding implications for various properties of LW/B-CC. This study presents a novel evaluation method for the particle packing state and provides guidance for the mix design of LW/B-CC.

Effect of self-movement on visually directed throwing: Implications for distance perception

ABSTRACT This study investigated how running on a treadmill influences the accuracy of throwing a ball to a hoop. Based upon previous research carried out in an open field that demonstrates that targets are perceived further away when running on a treadmill compared to when standing, we examined if this effect persisted in a visually directed throwing task. Fourteen athletes with normal or corrected vision participated. In Experiment 1, participants made indirect distance estimates of targets at 6 m, showing larger estimates while running. In Experiment 2, participants performed a throwing task, aiming to throw balls into a hoop while running or standing on a treadmill. The results showed that running on a treadmill influenced distance estimation, leading to larger throws. These findings suggest that the perception of distance is a process that relies on perceptual cues and sensorimotor feedback.

Enhancing Surveillance Systems with Cost-Effective Ultrasonic Radar for Object Detection and Tracking

Object detection is critical in surveillance systems because it enables timely identification of unauthorized objects, which is essential for maintaining security and mitigating risks. Existing approaches to surveillance typically suffer from limited coverage, variable detection range, moderate accuracy, high cost, and complex setup requirements. This article presents an approach to object detection using microcontroller-based ultrasonic sensors. Our approach leverages four distinct processing phases to create a radar-like system capable of real-time detection and autonomous decision-making. In this study, we conducted experiments by integrating more than six peripheral modules with the Arduino platform. By evaluating 17 diverse test cases in various scenarios and environments, the approach demonstrated enhanced object tracking and robustness compared to existing methods. The system effectively detects objects within the targeted area, providing precise distance measurements (around 50 cm) and position information (0 to 360 degrees). Moreover, our approach enabled the identification of three risk zones—medium, high-risk, and mild danger—within the critical region, contributing to improved security and risk management. The results reveal the system’s effectiveness in real-time detection, accurate distance estimation, and comprehensive risk assessment. Overall, our study significantly contributes to the advancement of object detection technology by effectively addressing the limitations of existing methods and offering a cost-effective and practical solution. Our research has the potential to monitor objects in surveillance systems across various domains, with significant implications for enhancing security, risk mitigation, and monitoring in industries reliant on effective object detection systems. Jagannath University Journal of Science, Volume 11, Number 1, June 2024, pp. 157−171

The impact of camera-monitor system viewing angles on drivers’ distance perception: A simulated driving study

Camera-monitor systems (CMS) are increasingly used in driving. CMS separates the driver's sight line from the camera view, due to the lack of mirror reflection, only changing the camera's visual axis angle may affect the driver's rear view perception. While previous research has explored camera height and field of view, the effects of horizontal and vertical viewing angles alone remain unclear. This study aimed to evaluate how the horizontal viewing angle and vertical viewing angle of CMS camera affect distance estimation and car-following tasks. By changing the horizontal and vertical viewing angle, different self-vehicle references and horizon positions were formed in the image. Two experiments were conducted with the CMS around the steering wheel (Experiment 1) and at the bottom of the A-pillar (Experiment 2). Independent variables were the horizontal viewing angle (reference scale: 1/4, 1/3, 1/2) and vertical viewing angle (horizon position: 1/2, 1/3). Dependent variables included distance estimation error ratio and following distance. Experiment 1 demonstrated a significant interaction effect: a smaller reference scale and higher horizon position reduced distance underestimation. Additionally, a smaller reference scale for the participants' self-vehicle resulted in shorter following distances. In Experiment 2, the distance estimation outcomes on the left display aligned with those of Experiment 1; however, the influence of the viewing angle was diminished on the right display. The study suggests CMS design should balance vehicle reference inclusion with environmental cues, enhancing distance perception and driving safety. The consistency between CMS design and driver familiarity also needs to be considered.

Distance and stellar parameter estimations of solar-like stars from the LAMOST spectroscopic survey

The Gaia mission has opened up a new era for the precise astrometry of stars, thus revolutionizing our understanding of the Milky Way. However, beyond a few kiloparseconds from the Sun, parallax measurements become less reliable, and even within 2 kpc, there still exist stars with large uncertainties. Our aim was to determine the distance and stellar parameters of 521,424 solar-like stars from LAMOST DR9; these stars lacked precise distance measurements (uncertainties higher than 20<!PCT!> or even without any distance estimations) when checked with Gaia. We proposed a convolutional neural network (CNN) model to predict the absolute magnitudes, colors, and stellar parameters ( and directly from low-resolution spectra. For spectra with signal-to-noise ratios at $g$ band ($ S/N_g $) greater than 10, the model achieves a precision of 85 K for 0.07 dex for 0.06 dex for 0.25 mag for and 0.03 mag for The estimated distances have a median fractional error of 4<!PCT!> with a standard deviation of 8<!PCT!>. We applied the trained CNN model to 521,424 solar-like stars to derive the distance and stellar parameters. Compared with other distance estimation studies and spectroscopic surveys, the results show good consistency. Additionally, we investigated the metallicity gradients of the Milky Way from a subsample, and find a radial gradient ranging from $-0.05 < R < 0.0\ dex\ $ and a vertical gradient ranging from $-0.26 < Z < -0.07\ dex\ We conclude that our method is effective in estimating distances and stellar parameters for solar-like stars with limited astrometric data. Our measurements are reliable for Galactic structure studies and hopefully will be useful for exoplanet researches.

Standard Errors for Calibrated Parameters

Abstract Calibration, the practice of choosing the parameters of a structural model to match certain empirical moments, can be viewed as minimum distance estimation. Existing standard error formulas for such estimators require a consistent estimate of the correlation structure of the empirical moments, which is often unavailable in practice. Instead, the variances of the individual empirical moments are usually readily estimable. Using only these variances, we derive conservative standard errors and confidence intervals for the structural parameters that are valid even under the worst-case correlation structure. In the over-identified case, we show that the moment weighting scheme that minimizes the worst-case estimator variance amounts to a moment selection problem with a simple solution. Finally, we develop tests of over-identifying or parameter restrictions. We apply our methods empirically to a model of menu cost pricing for multi-product firms and to a heterogeneous agent New Keynesian model.

Quarta: quantum supervised and unsupervised learning for binary classification in domain-incremental learning

Quantum machine learning recently gained prominence due to the promise of quantum computers in solving machine learning problems that are intractable on a classical computer. Nevertheless, several studies on problems which remain challenging for classical computing algorithms are emerging. One of these is classifying continuously incoming data instances in incremental fashion, which is studied in this paper through a hybrid computational solution that combines classical and quantum techniques. Hybrid approaches represents one of the current ways for the use of quantum computation in practical applications. In this paper, we show how typical issues of domain-incremental learning can be equally addressed with the properties of quantum mechanics, until to offer often better results. We propose the framework QUARTA to combine algorithms of quantum supervised learning, that is, variational quantum circuits, and techniques used in quantum unsupervised learning, that is, distance estimation. We aim at keeping the classification capabilities, which have learned on previously processed data instances, preserved as much as possible, and then acquiring new knowledge on new data instances. Experiments are performed on real-world datasets with quantum simulators.

Exploring faint white dwarfs and the luminosity function with Subaru HSC and SDSS in Stripe 82

Abstract We present 5,080 white dwarf (WD) candidates selected from stars matching between the multi-band imaging datasets of the Subaru Hyper Suprime-Cam (HSC) Survey and the Sloan Digital Sky Survey (SDSS) in the Stripe82 region covering about 165 deg2. We select WD candidates from the “reduced proper motion” diagram by combining the apparent magnitude in the range i = 19 – 24 and the proper motion measured from the datasets among a baseline of ∼ 14 years. We refine the WD candidates by fitting blackbody and template WD atmosphere models to HSC photometries for each candidate, enabling the estimation of distance and tangential velocity (vt). The deep HSC data allow us to identify low-temperature (<4000 K) and faint WD candidates down to absolute magnitude, Mbol ≃ 17. We evaluate the selection function of our WD candidates using a mock catalogue of spatial and kinematic distributions of WDs in the (thin and thick) disc and halo regions based on a Galactic model. We construct samples of disc and halo WD candidates by selecting WDs with tangential velocity, 40 < vt/[km s−1] < 80 and 200 < vt/[km s−1] < 500, respectively. The total number densities of the disc and halo WDs are (9.33 ± 0.89) × 10−3 pc−3 and (6.34 ± 2.90) × 10−4 pc−3. Our luminosity functions (LF) extend down to fainter absolute magnitudes compared with previous work. The faint WDs could represent the oldest generation of building blocks over the past ∼10 billion years of the assembly history of our Milky Way.

Multi-line laser 3D reconstruction method based on spatial quadric surface and geometric estimation

The traditional multi-line laser 3D reconstruction is based on binocular epipolar constraints and the equation of the laser optical plane. Firstly, matching points are identified based on the epipolar constraints. Then, the correct matching points are selected based on the optical plane equation of the multi-line laser. Finally, 3D reconstruction is performed using the selected matching points. In actual production processes involving multi-line lasers, noticeable distortion occurs due to the projection of Diffractive Optical Elements (DOE) at a large angle. This results in curved laser light planes and curved reflections on the measured objects. Under such circumstances, efficiently screening matching points using the traditional method based on the laser plane equation becomes challenging. Additionally, inherent noise in multi-line laser systems introduces errors in extracted laser center coordinates, making it impossible to directly obtain high-precision 3D data. To address these issues, this paper proposes a method that utilizes spatial quadric surfaces and geometric estimation for completing multi-line laser 3D reconstructions. By analyzing the distortion principle of DOE and the positional relationship after optical diffraction, the multi-line laser manifests as a quadric surface on the optical output plane. Consequently, by calibrating the equations of the quadric surface and applying binocular polar constraints, suitable matching points for the multi-line laser can be chosen. Once an accurate matching point is identified, a minimum geometric distance estimation can be established based on the distance constraint between the point and its corresponding epipolar line. This distance represents the separation between the left and right camera’s laser center points and their respective epipolar lines. Utilizing this estimate of the minimum geometric distance, a refined calculation can be performed to better satisfy epipolar constraints and obtain new matching points. Ultimately, utilizing these new matching points enables the completion of 3D reconstruction for multi-line lasers. In comparison with methods relying on spatial plane and epipolar constraints, our algorithm exhibits a superior degree of matchability and accuracy.According to multiple groups of test data, the average error before optimization is 0.3126 mm, and can be increased to 0.0336 mm after optimization.