Accurate Distance Estimation Research Articles

FFK: Fourier-Transform Fuzzy-c-means Kalman-Filter Based RSSI Filtering Mechanism for Indoor Positioning.

As indoor positioning has been widely utilized for many applications of the Internet of Things, the Received Signal Strength Indication (RSSI) fingerprint has become a common approach to distance estimation because of its simple and economical design. The combination of a Gaussian filter and a Kalman filter is a common way of establishing an RSSI fingerprint. However, the distributions of RSSI values can be arbitrary distributions instead of Gaussian distributions. Thus, we propose a Fouriertransform Fuzzyc-means Kalmanfilter (FFK) based RSSI filtering mechanism to establish a stable RSSI fingerprint value for distance estimation in indoor positioning. FFK is the first RSSI filtering mechanism adopting the Fourier transform to abstract stable RSSI values from the low-frequency domain. Fuzzy C-Means (FCM) can identify the major Line of Sight (LOS) cluster by its fuzzy membership design in the arbitrary RSSI distributions, and thus FCM becomes a better choice than the Gaussian filter for capturing LOS RSSI values. The Kalman filter summarizes the fluctuating LOS RSSI values as the stable latest RSSI value for the distance estimation. Experiment results from a realistic environment show that FFK achieves better distance estimation accuracy than the Gaussian filter, the Kalman filter, and their combination, which are used by the related works.

Edge-Oriented Social Distance Monitoring System Based on MTCNN

Social distance monitoring (SDM) systems are vital in fighting the spread of the coronavirus (COVID-19). Existing SDM systems employ bounding box-method, which imposes inaccurate distance estimation due to the high variance in its output coordinates. To solve this problem, an SDM system based on multitask cascaded convolutional neural networks (MTCNN) is proposed. Instead of using bounding box coordinates, face detection and facial landmarks localization of MTCNN is used to provide fixed coordinates and increase the distance estimation accuracy of SDM. However, while the accuracy issue is solved by using MTCNN, the SDM system suffer from large computational requirements due to the cascaded networks added on top of the distance estimation process. To deal with this challenge, a constrained optimization technique is employed to each stage of MTCNN with the goal of reducing its hardware requirements while keeping the same reliability as the original implementation. Experimental results show that the SDM system based on the optimized MTCNN achieves higher accuracy performance with reduced computational requirements as compared to conventional SDM systems. This allows the proposed SDM system using optimized MTCNN to be deployed efficiently on edge devices.

Hybrid Learning for Mobile Ad-Hoc Distancing/Positioning Using Bluetooth Low Energy

With the advent of Bluetooth Low Energy (BLE)-enabled smartphones, there has been considerable interest in investigating BLE-based distancing/positioning methods (e.g., for social distancing applications). In this paper, we present a novel hybrid learning method to support Mobile Ad-hoc Distancing (MAD) / Positioning (MAP) using BLE-enabled smartphones. Compared to traditional BLE-based distancing/positioning methods, the hybrid learning method provides the following unique features and contributions. First, it combines unsupervised learning, supervised learning and genetic algorithms for enhancing distance estimation accuracy. Second, unsupervised learning is employed to identify three pseudo channels/clusters for enhanced RSSI data processing. Third, its underlying mechanism is based on a new pattern-inspired approach to enhance the machine learning process. Fourth, it provides a flagging mechanism to alert users if a predicted distance is accurate or not. Fifth, it provides a model aggregation scheme with an innovative two-dimensional genetic algorithm to aggregate the distance estimation results of different machine learning models. As an application of hybrid learning for distance estimation, we also present a new MAP scenario with an iterative algorithm to estimate mobile positions in an ad-hoc environment. Experimental results show the effectiveness of the hybrid learning method. In particular, hybrid learning without flagging and with flagging outperform the baseline by 57 and 65 percent respectively in terms of mean absolute error. By means of model aggregation, a further 4 percent improvement can be realized. The hybrid learning approach can also be applied to previous work to enhance distance estimation accuracy and provide valuable insights for further research.

ВРЕМЕННАЯ СИНХРОНИЗАЦИЯ В СИСТЕМЕ МОБИЛЬНОЙ РАДИОСВЯЗИ ПЯТОГО ПОКОЛЕНИЯ

In this paper, the issue of time synchronization between the base station and user equipment in a fifth-generation cellular communication system is considered. In order to reduce the complexity of the current method of estimating the start symbol offset, a modification of the algorithm is proposed, in which the cross-correlation is computed in parallel for each DMRS (Demodulation Reference Signal) symbol. Furthermore, calculating the cross-correlation for each DMRS symbol allows for the development of a new method of estimating the sampling frequency offset based on the time interval estimation between two consecutive DMRS signals at the output of the matched filter. It is demonstrated that using a parabolic interpolation of the local regions around the correlation peak at the matched filter outputs yields an accurate estimation of the temporal distance between the received DMRS signals. Numerical simulation results show that the suggested technique can estimate the sampling frequency offset with an accuracy of less than 1 ppm over the entire range of signal-to-noise ratio values.

A Self-Capacitance Proximity E-Skin With Long-Range Sensibility for Robotic Arm Environment Perception

Proximity sensing is a major requirement for intelligent robots and robotic arms to ensure safety and closed-loop control in human–robot interactions. E-skins have received increasing attention because of their ease of arraying and installation. However, problems, such as insufficient sensing distance and inaccurate distance prediction, still exist. To solve these problems, we propose a self-capacitance-based long-range proximity sensing e-skin. First, two electrode shape classifications, external edge and internal holes, were analyzed and optimized in COMSOL to explore the variation trends of charge distribution and capacitance proximity characteristics. Then, based on the self-capacitance proximity theory, the parameters of the fitting function for the actual capacitance–distance curves for the four electrodes were solved using the planning solution method. The results show that the sensor has a good dynamic response, a long proximity sensing range (10.9 cm), high distance estimation accuracy (σ = 0.45), and good repeatability (>2 h). Finally, we integrate the sensor array with the end joint of the robotic arm to complete a sorting task. A closed-loop control demonstration of safe obstacle avoidance and deceleration was achieved, thereby demonstrating the great potential of e-skin for future human–machine collaboration scenarios.

Autonomous multicopter landing on a moving vehicle based on RSSI

AbstractCurrently, most of the studies on unmanned aerial vehicle (UAV) automatic landing systems mainly depend on image information to determine the landing location. However, the system requires a camera, a gimbal system and a separate image-processing device, which increases the weight and power consumption of the UAV, resulting in a shorter flight time. In addition, a large amount of computation and slow reaction speed can cause the camera to miss a proper landing moment. To solve these problems, in this study, the moving direction and relative distance between an object and the automatic landing system were measured using a receive signal strength indicator of the radio-frequency (RF) signal. To improve the movement direction and relative distance estimation accuracy, the noise in the RF signal was minimised using a low pass filter and moving average filter. Based on the filtered RF signal, the acceleration of the multicopter to reach the object was estimated by adopting the proportional navigation algorithm. The performance of the proposed algorithm for precise landing on a moving vehicle was demonstrated through experiments.

Can we predict long-duration running power output? A matter of selecting the appropriate predicting trials and empirical model.

When facing a long-distance race, athletes and practitioners could develop an efficient pacing strategy and training paces if an accurate performance estimate of the target distance is achieved. Therefore, this study aims to determine the validity of different empirical models (i.e. critical power [CP], Power law and Peronnet) to predict long-duration power output (i.e. 60min) when using two or three time trial configurations. In a 5-week training period, fifteen highly trained athletes performed nine-time trials (i.e. 1, 2, 3, 4, 5, 10, 20, 30, and 60min) in a randomized order. Their power-duration curves were defined through the work-time (CPwork), power-1/time (CP1/time), two-parameter hyperbolic (CP2hyp), three-parameter hyperbolic (CP3hyp) CP models using different two- and three-time trial configurations. The undisclosed proprietary CP models of the Stryd (CPstryd) and Golden Cheetah training software (CPcheetah)were also computed as well as the non-asymptotic Power law and Peronnet models. These were extrapolated to the 60-min power output and compared to the actual performance. The shortest valid configuration (95% confidence interval < 12 W) for CPwork and CP1/time was 3-30min (Bias: 8.3 [4.9 to 11.7] W), for CPstryd was 10-30min (Bias: 4.2 [-1.0 to 9.4] W), for CP2hyp, CP3hyp and CPcheetah was 3-5-30min (Bias < 5.7 W), for Power law was 1-3-10min (-1.0 [-11.9 to 9.9] W), and for Peronnet was 4-20min (-3.0 [-10.2 to 4.3] W). All the empirical models provided valid estimates when the two or three predicting trial configurations selected attended each model fitting needs.

Analyzing Accurate Egocentric Distance Estimates of University Students in Virtual Environments with a Desktop Display and Gear VR Display

The perception of distances is crucial in both the real world and virtual environments. However, distances can be incorrectly estimated in the latter one, and they can be affected by technological and human factors. We created a virtual environment to take a step toward understanding this phenomenon. We assessed the egocentric distance estimation skills of 239 university students at 10 various distances between 25 cm and 160 cm at 15 cm intervals. A desktop display was used by 157 students, while the Gear VR display was used by 72 students. The effects of the following factors were analyzed: gender, height, dominant arm, previous VR experience, gaming hours per week, whether the participants wore glasses, their field of study, and display device. Logistic regression analysis was performed to assess their influences on the probabilities of accurate distance estimates, while linear regression analysis was conducted to examine their effects on estimation times. The results show that except for the factors of whether the participants wore glasses and their field of study, the probabilities of accurate distance estimates can be affected along with estimation times themselves.

Research on a Super-Resolution and Low-Complexity Positioning Algorithm Using FMCW Radar Based on OMP and FFT in 2D Driving Scene.

Multitarget positioning technology, such as FMCW millimeter-wave radar, has broad application prospects in autonomous driving and related mobile scenarios. However, it is difficult for existing correlation algorithms to balance high resolution and low complexity, and it is also difficult to ensure the robustness of the positioning algorithm using an aging antenna. This paper proposes a super-resolution and low-complexity positioning algorithm based on the orthogonal matching pursuit algorithm that can achieve more accurate distance and angle estimation for multiple objects in a low-SNR environment. The algorithm proposed in this paper improves the resolving power by two and one orders of magnitude, respectively, compared to the classical FFT and MUSIC algorithms in the same signal-to-noise environment, and the complexity of the algorithm can be reduced by about 25-30%, with the same resolving power as the OMP algorithm. Based on the positioning algorithm proposed in our paper, we use the PSO algorithm to optimize the arrangement of an aging antenna array so that its angle estimation accuracy is equivalent to that observed when the antenna is intact, improving the positioning algorithm's robustness. This paper also further realizes the use of the proposed algorithm and a single-frame intermediate frequency signal to estimate the position angle information of the object and obtain its motion trajectory and velocity, verifying the proposed algorithm's estimation ability when it comes to these qualities in a moving scene. Furthermore, this paper designs and carries out simulations and experiments. The experimental results verify that the positioning algorithm proposed in this paper can achieve accuracy, robustness, and real-time performance in autonomous driving scenarios.

Non-metric distance judgements are influenced by image projection geometry and field of view.

Despite its mathematical simplicity and ubiquity in imaging technology, there has long been doubt about the ability of linear perspective to best represent human visual space, especially at wide-angle fields of view under natural viewing conditions. We investigated whether changes to image geometry had an impact on participants' performance, specifically in terms of non-metric distance estimates. Our multidisciplinary research team developed a new open-source image database to study distance perception in images by systematically manipulating target distance, field of view, and image projection using non-linear natural perspective projections. The database consists of 12 outdoor scenes of a virtual three-dimensional urban environment in which a target ball is presented at increasing distance, visualised using both linear perspective and natural perspective images, rendered, respectively, with three different fields of view: 100°, 120°, and 140° horizontally. In the first experiment (N = 52), we tested the effects of linear versus natural perspective on non-metric distance judgements. In the second experiment (N = 195), we investigated the influence of contextual and previous familiarity with linear perspective, and individual differences in spatial skills on distance estimations. The results of both experiments showed that distance estimation accuracy improved in natural compared with linear perspective images, particularly at wide-angle fields of view. Moreover, undertaking a training session with only natural perspective images led to more accurate distance judgements overall. We argue that the efficacy of natural perspective may stem from its resemblance to the way objects appear under natural viewing conditions, and that this can provide insights into the phenomenological structure of visual space.

Evaluation and revision of long-range single-site lightning location accuracy considering the time delay of ground wave

Detecting the distance and orientation of long-distance thunderstorms has very important practical significance. The multi-station lightning location system relies on a high-precision time module and good network communication capabilities, but in some cases these conditions cannot be met, but there is still a need for lightning activity monitoring, and it is very important to establish a single-site lightning location system. In this paper, we have established a long-distance single-site lightning location station, and in order to improve the accuracy of distance estimation, a numerical algorithm is used to obtain the relationship between the ground wave arrival time delay and the propagation distance, and it is used to revise the time difference between the peak value of the skywave and ground wave. Moreover, we used multi-station lightning location data to revise the site-error in magnetic direction finder method to improve the accuracy of the direction calculation. The results show that the effective detection range of the single-site we have been established is 200 km–2000 km, and the revised average direction deviation dropped from 12.3° to 8.6°. The verification results of thunderstorms within the effective detection range show that the relative error of single-site lightning location is 8.4%–18.6% after the revision.

A Distance Estimation Model for DV-Hop Localization in WSNs

The localization of sensor nodes in wireless sensor networks (WSNs) has been a thorny research problem. Existing DV-Hop based range-free localization algorithms estimate the distance from an unknown node to an anchor node by the multi-hop forwarding principle and the location information of anchor nodes. However, they lack an accurate distance estimation model of anchor nodes under different hops, resulting in inaccurate localization. To address this issue, this paper proposes a distance estimation model (DEM) for DV-Hop localization in WSNs. Specifically, DEM mathematically derives the farthest distance detected by an anchor node under different hops. Then, the average distance is calculated by the upper and lower boundaries according to the derived farthest distance. Finally, the average distance is embedded into a multi-objective optimization to solve the DV-Hop localization problem. The simulation results show that the proposed model is significantly superior to the state of the art in terms of sensor localization accuracy and cost effectiveness.

Reliability of distance estimation in virtual reality space: A quantitative approach for construction management

This paper reports a study aimed at assessing the reliability of decision-making for construction management based on visual observation of the built environment in virtual reality. The focus of this study is on egocentric distance perception, a critical factor in construction management decisions based on visual observation. The study evaluates the accuracy of distance estimation and walking a target distance in virtual reality, as compared to the real world, through experiments. The experimental results demonstrate that participants consistently underestimate distance and walk beyond the target distance. Notably, in virtual reality, they underestimated the distance by 22% more than they did in the real world, and they overwalked 53% more than they did in the real world. These findings offer valuable insights for construction managers reviewing digital twin models in virtual reality to make informed decisions. Furthermore, this study lays a foundation for future research on the reliability of understanding space occupancy in virtual reality.

Determination of Distance, Extinction, Mass, and Age for Stars in LAMOST DR7

Large scale spectroscopic surveys such as that using Large-sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) have collected spectra of millions stars in the Milky Way. Utilizing this huge sample of stars to study the assembling history and structure of our Galaxy requires accurate estimates of distance, extinction, age, and mass for individual stars. Combining the parallax constraint from Gaia EDR3 with Bayesian inference, we have estimated the distance and extinction for stars observed in LAMOST DR7, as well as the stellar mass and age for evolved stars in this data release. We validated the accuracies of the stellar parameters by comparing our results against various measurements, including the star-pair technique, asteroseismology, globular clusters, and isochrone fits to main sequence stars and subgiants. This is a valuable catalog of stellar parameters under a Bayesian framework estimated using the data from Gaia EDR3 and LAMOST spectroscopic data. With this data set we explored the stellar population of the Galactic massive substructure Gaia-Sausage-Enceladus (GSE). The kinematically selected members of GSE have a median metallicity of [Fe/H] = −1.29 and a median age of 11.6 Gyr.

A distance Protection/Fault location algorithm for Double-Circuit transmission lines with asymmetrical circuits during Cross-Country faults

The impedance-measuring elements and fault locators in distance relays suffer from measuring the correct fault impedance and fault location when facing cross-country faults (CCFs). During such atypical faults, this paper proposes a distance protection along with a fault location algorithm for double-circuit lines with asymmetrical circuits. This is fulfilled through instantaneous fault-branch currents innovatively derived from the line six-phase circuit model analysis on a time-domain basis. The currents derived lead to the relations that are mathematically linear combinations of designated vectors. This makes the possibility of establishing vector-input adaptive linear combiners (VI-ALC) in accordance with the relations derived. Being fed from the vectors of instantaneous measurements at the relaying side, the VI-ALCs with a distinctive capability of online training would swiftly give an accurate estimation of the fault distances during CCFs. The estimations are exploited in a logical procedure for both of the distance protection and fault location functions, taking account of different position status of the CCFs’ comprising LG faults. The proposed algorithm is feasibly implementable and is independent of the system configuration. It operates robustly in high-ground resistance and noisy conditions. Through extensive simulation studies in PSCAD/EMTDC, performance of the proposed algorithm would be investigated and verified in a variety of fault scenarios.

Numerical and theoretical modeling for transmission of droplet carrying virus

Safe social distance is an important parameter for the prevention and treatment of a virus transmitted through droplets. However, the distance selected is not suitable for all air environments, and the calculation method of fluid research is time-consuming. Therefore, rapid and accurate prediction of safe social distance is the key to epidemic prevention and control. However, it is difficult for the existing fluid research to obtain the safe social distance rapidly. In this study, we set up a simple and effective numerical model and develop codes that combine the effects of evaporation, drag force, and gravity. We further conducted numerical simulations to investigate the motion of droplets in various air conditions. We completed a single case simulation with only one core and within several minutes, and determined that the resistance time and velocity evolution directly impact the transmission distance. We also observed two-stage regularity of motions in both the vertical and horizontal directions, and competition between evaporation and vertical falling. In addition, we derived a set of analytical solutions to describe the evaporation time and vertical and horizontal distances. The results demonstrated the good accuracy of the predicted data. Herein, we obtained an approximate rather than an accurate solution and, together with empirical coefficients, fitted it based on our numerical simulation. The proposed method can provide a rapid and accurate estimation of safe social distances for various environmental conditions. Common clinical cases were also analyzed, and prevention and control recommendations were provided based on the outcomes of the study.

An Optimization Method Combining RSSI and PDR Data to Estimate Distance between Smart Devices for COVID‐19 Contact Tracing

Distance estimation methods arise in many applications, such as indoor positioning and COVID‐19 contact tracing. The received signal strength indicator (RSSI) is favored in distance estimation. However, the accuracy is not satisfactory due to the signal fluctuation. Besides, the RSSI‐only method has a large‐ranging error because it uses fixed parameters of the path loss model. Here, we propose an optimization method combining RSSI and pedestrian dead reckoning (PDR) data to estimate the distance between smart devices. The PDR may provide high accuracy of walking distance and direction. Moreover, the parameters of the path loss model are optimized to dynamically fit the complex electromagnetic environment. The proposed method is evaluated in outdoor and indoor environments and compared with the RSSI‐only method. The results show that the mean absolute error is reduced up to 0.51 m and 1.02 m, with an improvement of 10.60% and 64.55% for outdoor and indoor environments, respectively, compared with the RSSI‐only method. Consequently, the proposed optimization method has better accuracy of distance estimation than the RSSI‐only method, and its feasibility is demonstrated through real‐world evaluations.

Vehicle Distance Estimation from a Monocular Camera for Advanced Driver Assistance Systems

The purpose of this study is to propose a framework for accurate and efficient vehicle distance estimation from a monocular camera. The proposed framework consists of a transformer-based object detector, a transformer-based depth estimator, and a distance predictor. The object detector detects various objects that are mostly symmetrical from an image captured by the monocular camera and provides the type of each object and the coordinate information of a bounding box around each object. The depth estimator generates a depth map for the image. Then, the bounding boxes are overlapped with the depth map to extract the depth features of each object, such as the mean depth, minimum depth, and maximum depth of each object. The present study then trained three models—eXtreme Gradient Boosting, Random Forest, and Long Short-Term Memory—to predict the actual distance between the object and the camera based on the type of the object, the bounding box of the object (including its coordinates and size), and the extracted depth features. The present study proposes including the trimmed mean depth of an object to predict the actual distance by excluding the background pixels around an object but within the bounding box of the object. The evaluation results show that the proposed framework outperformed existing studies.

Trajectory big data reveals spatial disparity of healthcare accessibility at the residential neighborhood scale

Quantifying spatial disparities of accessibility to basic services is fundamentally important for achieving the SDGs goal of universal access to these services. Trajectory big data shows great potential to address such disparities, but we know little about its efficacy. Here, taking patients from residential neighborhoods to healthcare in Beijing, China, as an example, we tested the efficacy of applying taxi data on the patients' travel behavior and thereby the potential of measuring accessibility at the residential neighborhood scale. Our results showed that the taxi data quantified a decreased healthcare-seeking behavior with increased distance from hospitals and identified hospitals' catchment areas. Meanwhile, the exponential function provided a more accurate estimation of the decay distance of patients to healthcare than the Gaussian and Power functions. Additionally, results showed that using taxi data had great potential to quantify the accessibility to hospitals, and more importantly, to reveal the spatial disparity of accessibility at a finer scale than blocks or subdistricts. The approach developed in this study can improve our understanding of accessibility and its spatial disparity and provide fundamental data for the increasing interest in the 15-min community life circle planning worldwide, and to address local healthcare inequality.

동적 장면에서의 차간 거리 보정을 위한 광학 흐름 기반 차량 자세 추정

Stable inter-vehicle distance estimations are required in dynamic scenes as they are essential for advanced driver-assistance systems (ADAS) and high-level autonomous driving. However, instantaneous changes in the pose of a vehicle in the dynamic scene may cause errors in the estimation of the inter-vehicle distance, which can lead to traffic accidents and also affect obstacle avoidance performance. In this study, the optical flow motion vector is histogrammed to extract a representative value, and the correlation between the representative value and inertial measurement unit (IMU) data is confirmed to verify the accuracy in vehicle pose estimation based on the optical flow motion vector. Then, an algorithm is proposed for improving the vehicle distance estimation accuracy in real-time ground compensation. Experiments have been conducted using an autonomous vehicle on the road with speed bumps, and the data from the IMU sensor installed in the vehicle and LiDAR detection results are used as the ground-truth data. The pose estimation with the proposed method shows an average error of 0.0029 rad, and the distance correction error can be reduced from 2.73 m (14.8%) to 0.86 m (4.6%), demonstrating the possibility of accurate real-time inter-vehicle distance correction.