Distance Estimation Error Research Articles

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.

UWB distance estimation errors in (non-)line of sight situations within the context of 3D analysis of human movement

Abstract Integrated Ultrawideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensors are becoming popular for indoor localization applications, as a higher accuracy can be achieved than with just MIMU sensors. These integrated sensors could extend stability and accuracy in the field of 3D analysis of human movement (3D AHM) if they can deliver position estimates with an accuracy close to 1 cm. Achieving this high accuracy of 1 cm remains challenging, with most studies reporting position estimation errors around 5 cm, often due to Non-Line of Sight (NLOS) conditions and systematic UWB sensor distance estimation errors. Studying the distance error characteristic of UWB in situations of 3D AHM is essential to deal with these errors. While research on UWB distance errors in Line of Sight (LOS) and NLOS situations exists, few studies focus on the NLOS errors caused by the human body and were not in the relevant scenarios of 3D AHM. Therefore, this article examines UWB sensor performance and distance error characteristics in LOS and NLOS situations typical for the 3D AHM. Both the LOS and NLOS situations were studied in the typical 3D AHM distance range of 0.2 m to 2 m. The NLOS situations were studied first with a human subject as NLOS causing object and then with simulated human body segments (PVC pipes filled with water) of varying diameters. In LOS situations, consistent systematic bias errors were observed, along with incidental errors at specific positions in the room. In NLOS scenarios caused by the human and simulated body segments, a consistent and reproducible overestimation of distances was found. The reproducibility of these errors based on relative node and object positions suggests that systematic mitigation methods could significantly reduce errors, enabling more accurate and reproducible 3D human movement analysis.

Distance correction range-free localization algorithm for WSNs

Accurate location information is essential for the effective deployment of Wireless Sensor Networks (WSNs) applications. Recently, researchers have developed numerous localization algorithms, with multi-hop methods being particularly attractive due to their cost-effectiveness and robustness, but their accuracy is still unsatisfactory. For better localization accuracy, this paper presented a distance correction range-free localization algorithm (DCRA). Firstly, the proposed algorithm estimates the distance between neighboring nodes based on neighbor connectivity to improve the accuracy of single-hop distance estimation. Secondly, during the multi-hop distance estimation phase, path similarity metrics are employed to ascertain the optimal path correction coefficients, thereby minimizing multi-hop distance estimation errors. Finally, based on the estimated distances, the node localization is converted into an optimization problem, which is then solved using an enhanced particle swarm algorithm to obtain the node coordinates. Simulation results show that the distance estimation accuracy of the DCRA algorithm in different node density scenarios is improved by more than 31 % and 12 % compared to the DV-Hop and DV-RND algorithms, respectively; and in terms of localization accuracy the DCRA algorithm is improved by more than 58 % and 35 % compared to the DV-Hop and DV-RND algorithms, respectively.

CRT-Based Clock Synchronization for Millimeter-Wave Communication with Asymmetric Propagation Delays

The rapid advancement of millimeter-wave communication technology has presented new challenges for time synchronization, driven by the need for high-speed and low-latency data transmission. Ethernet synchronization technologies, such as Precise Time Protocol (PTP), have emerged to overcome the limitations of point-to-point architecture and enable precise synchronization across multiple devices. A key drawback of conventional PTP is its reliance on symmetric packet exchange delays, which may not hold in real-world scenarios where there is relative mobility between master and slave nodes, leading to asymmetric propagation delays and clock offset errors. To address this issue, a novel clock synchronization protocol that integrates Chinese Remainder Theory (CRT) has been proposed. This protocol enhances conventional PTP by incorporating distance estimation based on CRT using multi-carrier phase measurement. By combining a coarse estimation of one-way propagation delay from conventional PTP with a fine estimation of remainder distance from CRT, the protocol accurately determines the distance between master and slave nodes, reducing motion errors and enhancing clock synchronization accuracy. Simulation results indicate that the Root Mean Square Error (RMSE) of distance estimation remains below 10−5 m, with a corresponding motion time error of approximately 0.01 picosecond.

ЗАСТОСУВАННЯ ФІЛЬТРА ЧАСТОЧОК ДО ЗАДАЧ САМОЛОКАЛІЗАЦІЇ ЛИШЕ ЗА ВІДСТАННЮ ДО ДЖЕРЕЛ РАДІОСИГНАЛІВ

This work is aimed at searching for a relatively accurate and computationally efficient algorithm for range-only self-localization. Key requirement to the data sources is cost effectiveness. The radio signal strength indicator (RSSI)-based distance estimation model was chosen, due to wide availability of hardware and cost effectiveness. Key requirements to the algorithm are robustness towards noise introduced by RSSI-based distance estimation errors and flexibility to introduce additional data sources. Particle filter algorithm was chosen, because it satisfies both requirements, although it has bigger computational costs comparing to Kalman filter. A simulation environment was created to conduct experiments with assumed transmitted signal strength of 1W and square root signal decay law. A Particle filter-based actor was implemented. Actor starts by translating geo-spatial coordinates of beacons into relative planar cartesian coordinates, generates particles around beacons. Particles are placed with uniform distribution within a torus with co-planar circular axis radius equal to distance estimation. In each experiment, simulation software walks actor through a pre-defined path, compares obtained position estimates with expected state and calculates MAE and RMSE. Obtained results were satisfactory, in some cases showing MAE=2.5981 and RMSE=2.8474 after short stabilization period of few iterations. This period exists due to suboptimal initial particles placement. A Kalman filter-based actor was implemented as a comparison. A reference Kalman filter-based implementation showed slightly worse overall results with MAE=12.5199 and RMSE=13.2238. This can be explained by a lack of input from IMU. Further research will be directed towards UAV swarm self-localization and collision avoidance.

Assessing the Effect of Data Quality on Distance Estimation in Smartphone-Based Outdoor 6MWT.

As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users' previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions.

VTIL: A multi-layer indoor location algorithm for RSSI images based on vision transformer

As WiFi technology becomes more widespread and integrated, precise location tracking within complex indoor environments is gaining significance in contemporary public spaces.Nevertheless, challenges persist in indoor scenarios, including issues such as noise and multi-path effects stemming from the degradation of the Received Signal Strength Indicator (RSSI). Additionally, obstacles causing signal shading contribute to a decrease in the accuracy of indoor location tracking. Within this paper, we introduce an innovative indoor localization algorithm termed Vision Transformer Indoor Localization (VTIL). This algorithmleverages RSSI and Vision-Transformer (ViT) technologies to enhance indoor positioning accuracy. Initially, the RSSI fingerprints undergo normalization by scaling them to the maximum and minimum values. To mitigate the impact of noise and irrelevant features, the Principal Component Analysis (PCA) algorithmis then employed for effective feature extraction.Secondly, the RSSI fingerprint library is converted into an RSSI gray image library. Then the RSSI gray image is divided into several small blocks, and the position-coding input is performed in the form of a sequence. The ViT model divides the weight ratio of each block in the RSSI image to alleviate the impact of multi-path effects. According to the experimental results using public datasets, our approach achieves a noteworthy 37.26% reduction in the average distance estimation error when compared to existing indoor fingerprint localization algorithms.

Wireless sensor node localization algorithm combined with PSO-DFP

Abstract In wireless communication technology, wireless sensor networks usually need to collect and process information in very harsh environment. Therefore, accurate positioning of sensors becomes the key to wireless communication technology. In this study, Davidon–Fletcher–Powell (DFP) algorithm was combined with particle swarm optimization (PSO) to reduce the influence of distance estimation error on positioning accuracy by using the characteristics of PSO iterative optimization. From the experimental results, among the average precision (AP) values of DFP, PSO, and PSO-DFP algorithms, the AP value of PSO-DFP was 0.9972. In the analysis of node positioning error, the maximum node positioning error of PSO-DFP was only about 21 mm. The results showed that the PSO-DFP algorithm had better performance, and the average positioning error of the algorithm was inversely proportional to the proportion of anchor nodes, node communication radius, and node density. In conclusion, the wireless sensor node location algorithm combined with PSO-DFP has a better location effect and higher stability than the traditional location algorithm.

Measuring neutron star distances and properties with gravitational-wave parallax

ABSTRACT Gravitational-wave astronomy allows us to study objects and events invisible to electromagnetic waves. So far, only signals triggered by coalescing binaries have been detected. However, as the interferometers’ sensitivities improve over time, we expect to observe weaker signals in the future, e.g. emission of continuous gravitational waves from spinning, isolated neutron stars. Parallax is a well-known method, widely used in electromagnetic astronomical observations, to estimate the distance to a source. In this work, we consider the application of the parallax method to gravitational-wave searches and explore possible distance estimation errors. We show that detection of parallax in the signal from a spinning down source can constrain the neutron star moment of inertia. For instance, we found that the relative error of the moment of inertia estimation is smaller than 10 per cent for all sources closer than 300 pc, for the assumed birth frequency of 700 Hz, ellipticity ≥10−7, and for 2 yr of observations by the Einstein Telescope, assuming spin-down due purely to quadrupolar gravitational radiation.

Fast safety distance warning framework for proximity detection based on oriented object detection and pinhole model

Unauthorized approaching dangerous devices can cause serious dangers in electricity industry. Estimation on distances between human and these devices can effectively reduce the probabilities of various accidents, but there are limited studies focusing on it due to the complexity. In this paper, we propose a fast safety distance warning framework to detect proximity to dangerous devices in electrical operations. The framework consists of a customized oriented object detection model to extract precise pixel widths of objects, and a distance estimation method based on monocular camera and pinhole model to estimate distance. A tracking algorithm is applied to achieve targeted warning and fewer false alarms. The framework has been put into use in transformer stations in Yuxi power supply bureau, Yunnan Province, and distance estimation errors can be restricted to 0.5 meters. In experiments, the framework achieves 34 frames per second and 49.5% average precision, which are both state-of-the-art performances.

Node localization in wireless sensor networks using a hyper-heuristic DEEC-Gaussian gradient distance algorithm

In the recent age of technological advancements, wireless sensor networks are an important application for smart modernized environments. In WSNs, node localization has been an issue for over a decade in the research community. One of the goals of localization in a wireless sensor network is to localize sensor nodes in a two-dimensional plane. Localization in wireless sensor networks helps to supply information to aid decision-making from the aggregated data that are sent from packets to base stations. Internet of Things with the use of Global Positioning Systems for tracking sensor zones is not a cost-effective means of solution. In the extant literature, there have been a variety of algorithms to identify unknown sensor locations in wireless sensor networks. This research paper aims to address the problem of determining the location of the sensor node at the base station with minimum localization error when the data between the nodes is transmitted wirelessly. To detect the location of an unknown sensor node packets sent to the destinations, the total number of anchor nodes, location error and distance estimation error were considered. The DEEC-Gauss Gradient Distance Algorithm has a lower localization error than the Weighted Centroid Localizations algorithm, Compensation Coefficient algorithm, DV-Hop algorithm, Weighted Hyperbolic algorithm and Weighted Centroid algorithm for the same ratio of anchor nodes and WSN configuration. According to the study's findings, the DGGDEA has an average localization error of 11% for anchor nodes (20-80), and an average localization error of 11.3% for anchor nodes 200-450. Hence, the DEEC-Gaussian Gradient Distance Elimination Algorithm (DGGDEA) showed higher accuracy with comparison to the modern-day approaches.

카메라-IMU 센서 융합 기반 자세 추정을 통한 전방 객체 거리 정밀 보정

This paper presents a novel method to correct with only the camera and IMU sensors an estimated distance error due to a rapid change in a vehicle’s posture while driving, as it is very important to estimate the distance of a front car in autonomous driving. To estimate the change in vehicle’s posture, the proposed method adopts a visual odometry method using the optical flow and the camera-IMU fusion. The error in the estimated distance caused by an instantaneous change in the vehicle

Automatic social distance estimation for photographic studies: Performance evaluation, test benchmark, and algorithm

The social distancing regulations introduced to slow down the spread of COVID-19 virus directly affect a basic form of non-verbal communication, and there may be longer term impacts on human behavior and culture that remain to be analyzed in proxemics studies. To obtain quantitative results for such studies, large media and/or personal photo collections must be analyzed. Several social distance monitoring methods have been proposed for safety purposes, but they are not directly applicable to general photo collections with large variations in the imaging setup. In such studies, the interest shifts from safety to analyzing subtle differences in social distances. Currently, there is no suitable benchmark for developing such algorithms. Collecting images with measured ground-truth pair-wise distances using different camera settings is cumbersome. Moreover, performance evaluation for these algorithms is not straightforward, and there is no widely accepted evaluation protocol. In this paper, we provide an image dataset with measured pair-wise social distances under different camera positions and settings. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate such algorithms. We also propose an automatic social distance estimation method that can be applied on general photo collections. Our method is a hybrid method that combines deep learning-based object detection and human pose estimation with projective geometry. The method can be applied on uncalibrated single images with known focal length and sensor size. The results on our benchmark are encouraging with 91% human detection rate and only 38.24% average relative distance estimation error among the detected people.

Inter-User Distance Estimation Based on a New Type of Fingerprint in Massive MIMO System for COVID-19 Contact Detection.

In this paper, we address the challenging task of estimating the distance between different users in a Millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (mMIMO) system. The conventional Time of Arrival (ToA) and Angle of Arrival (AoA) based methods need users under the Line-of-Sight (LoS) scenario. Under the Non-LoS (NLoS) scenario, the fingerprint-based method can extract the fingerprint that includes the location information of users from the channel state information (CSI). However, high accuracy CSI estimation involves a huge overhead and high computational complexity. Thus, we design a new type of fingerprint generated by beam sweeping. In other words, we do not have to know the CSI to generate fingerprint. In general, each user can record the Received Signal Strength Indicator (RSSI) of the received beams by performing beam sweeping. Such measured RSSI values, formatted in a matrix, could be seen as beam energy image containing the angle and location information. However, we do not use the beam energy image as the fingerprint directly. Instead, we use the difference between two beam energy images as the fingerprint to train a Deep Neural Network (DNN) that learns the relationship between the fingerprints and the distance between these two users. Because the proposed fingerprint is rich in terms of the users’ location information, the DNN can easily learn the relationship between the difference between two beam energy images and the distance between those two users. We term it as the DNN-based inter-user distance (IUD) estimation method. Nonetheless, we investigate the possibility of using a super-resolution network to reduce the involved beam sweeping overhead. Using super-resolution to increase the resolution of low-resolution beam energy images obtained by the wide beam sweeping for IUD estimation can facilitate considerate improvement in accuracy performance. We evaluate the proposed DNN-based IUD estimation method by using original images of resolution 4 × 4, 8 × 8, and 16 × 16. Simulation results show that our method can achieve an average distance estimation error equal to 0.13 m for a coverage area of 60 × 30 m2. Moreover, our method outperforms the state-of-the-art IUD estimation methods that rely on users’ location information.

An Improved Trilateration Positioning Algorithm with Anchor Node Combination and K-Means Clustering.

As a classic positioning algorithm with a simple principle and low computational complexity, the trilateration positioning algorithm utilizes the coordinates of three anchor nodes to determine the position of an unknown node, which is widely applied in various positioning scenes. However, due to the environmental noise, environmental interference, the distance estimation error, the uncertainty of anchor nodes’ coordinates, and other negative factors, the positioning error increases significantly. For this problem, we propose a new trilateration algorithm based on the combination and K-Means clustering to effectively remove the positioning results with significant errors in this paper, which makes full use of the position and distance information of the anchor nodes in the area. In this method, after analyzing the factors affecting the optimization of the trilateration and selecting optimal parameters, we carry out experiments to verify the effectiveness and feasibility of the proposed algorithm. We also compare the positioning accuracy and positioning efficiency of the proposed algorithm with those of other algorithms in different environments. According to the comparison of the least-squares method, the maximum likelihood method, the classical trilateration and the proposed trilateration, the results of the experiments show that the proposed trilateration algorithm performs well in the positioning accuracy and efficiency in both light-of-sight (LOS) and non-light-of-sight (NLOS) environments. Then, we test our approach in three realistic environments, i.e., indoor, outdoor and hall. The experimental results show that when there are few available anchor nodes, the proposed localization method reduces the mean distance error compared with the classical trilateration, the least-squares method, and the maximum likelihood.

Automated distance estimation for wildlife camera trapping

The ongoing biodiversity crisis calls for accurate estimation of animal density and abundance to identify sources of biodiversity decline and effectiveness of conservation interventions. Camera traps together with abundance estimation methods are often employed for this purpose. The necessary distances between camera and observed animals are traditionally derived in a laborious, fully manual or semi-automatic process. Both approaches require reference image material, which is both difficult to acquire and not available for existing datasets. We propose a fully automatic approach we call AUtomated DIstance esTimation (AUDIT) to estimate camera-to-animal distances. We leverage existing state-of-the-art relative monocular depth estimation and combine it with a novel alignment procedure to estimate metric distances. AUDIT is fully automated and requires neither the comparison of observations in camera trap imagery with reference images nor capturing of reference image material at all. AUDIT therefore relieves biologists and ecologists from a significant workload.We evaluate AUDIT on a zoo scenario dataset unseen during training where we achieve a mean absolute distance estimation error over all animal instances of only 0.9864 m and mean relative error (REL) of 0.113. The code and usage instructions are available at https://github.com/PJ-cs/DistanceEstimationTracking

OFDM-NOMA combined with LFM signal for W-band communication and radar detection simultaneously

In this Letter, an effective integrated waveform generation method where an orthogonal frequency division multiplexing (OFDM)-based non-orthogonal multiple access (NOMA) communication signal combined with a linear frequency modulation (LFM) radar signal is proposed and experimentally demonstrated in a W-band communication and radar detection system. The proposed method can generate communication and radar signals simultaneously. In addition, multiple user communication and multi-target radar detection can be realized. Furthermore, for the OFDM-NOMA signal, discrete Fourier transform (DFT) precoding is used to keep the optimal power ratio consistent on different subcarriers, thus, the communication performance can be effectively improved. The experimental results show that the distance estimation error is 1.2 cm for radar signals. Meanwhile, after transmission over 0.8-m free space, the total aggregate rate for two users with the OFDM-NOMA signal is 3.125 Gb/s under the bit error rate of 8.5 × 10-4.

An Auto-Encoder Multitask LSTM Model for Boundary Localization

Although positioning in both indoor and outdoor environments has been investigated in-depth individually by various means, little attention has been paid to the intersected areas of both environments and the positioning accuracy in these boundary areas remains concern. For indoor positioning, several recent studies have applied machine learning algorithms based on WiFi signals. However, these methods may not be applicable to solve the boundary localization problem which is faced for positioning in boundary areas around a building. In this paper, we propose a deep learning model which concatenates auto-encoders with LSTM networks to perform multi-sensor multi-task fingerprint positioning. The adopted sensors include WiFi, GPS, cellular (fused with GPS), and magnetometer. In our model, we first employ a dense block-based auto-encoder to extract representative latent codes of fingerprints. Such latent codes are proven to be more distinguishable since they are revertible to their original inputs. Then, a sequence of latent codes are injected into an LSTM network performing three output tasks, responsible for location estimation, most suitable sensor selection, and indoor/outdoor classification, respectively. Extensive real-life experiments are performed on two campuses and the results demonstrate that our model achieves high positioning accuracy with an average Euclidean distance estimation error of about 0.43 meter.

Stacked Autoencoders-Based Localization Without Ranging Over Internet of Things

Location information plays an important role in many applications of the Internet of Things (IoT). The low cost and ease of scalability of range-free localization algorithms have attracted the attention of many researchers, but the performance of many localization algorithms available in the literature varies greatly in different networks. Specifically, algorithms designed for anisotropic networks may not perform well in isotropic networks, and vice versa. To improve localization accuracy in both isotropic and anisotropic networks, a novel range-free localization algorithm named LSAE is proposed in this article, oriented to the network positioning without ranging over the IoT. The proposed algorithm utilizes the known information in the network, namely, the hop counts and distances between anchor nodes, to train the stacked autoencoders (SAE) model. In this way, it achieves accurate prediction of the distances between unknown nodes and anchor nodes. To further improve the localization accuracy, the disadvantage of the least square method is analyzed, and a novel coordinate estimation method based on the statistical results of distance estimation errors is proposed. We conducted a huge number of numerical simulations with and without the impact of multiple anisotropic factors in three different types of networks. The results indicate that the proposed algorithm outperforms other state-of-the-art algorithms treating the impact of multiple anisotropic factors, and demonstrates the high accuracy and robustness.

Key Update and Device Location of Internet of Things Based on Smart Contract

In order to reduce the key update time delay of the Internet of Things and reduce the device location error rate and location time, a new key update and device location of Internet of Things based on smart contract was designed. Adopt improved MVIF security mechanism of both parties to build smart contract security mechanism, and the key update of the Internet of Things is realized through key predistribution, user registration and login, and key update. On this basis, multidimensional scaling technology is used to abstract Internet of Things devices into relative coordinates in multidimensional space, and the absolute coordinates are obtained by eliminating the distance estimation error to achieve Internet of Things device positioning. Experimental results show that the proposed method has a lower key update time delay, a lower amount of data used in the key update process, a lower positioning error rate of Internet of Things devices, and a shorter positioning time, which fully verifies the effectiveness of the proposed method.