Accurate Distance Estimation Research Articles

A Non-Linear Temperature Compensation Model for Improving the Measurement Accuracy of an Inductive Proximity Sensor and Its Application-Specific Integrated Circuit Implementation.

The non-linear characteristic of a non-contacting Inductive Proximity Sensor (IPS) with the temperature affects the computation accuracy when measuring the target distance in real time. The linear model based method for distance estimation shows a large deviation at a low temperature. Accordingly, this paper presents a non-linear measurement model, which computes the target distance accurately in real time within a wide temperature range from to . By revisiting the temperature effect on the IPS system, this paper considers the non-linear characteristic of the IPS measurement system due to the change of temperature. The proposed model adopts a non-linear polynomial algorithm rather than the simple linear Look-Up Table (LUT) method, which provides more accurate distance estimation compared to the previous work. The introduced model is fabricated in a 0.18 m Complementary Metal Oxide Semiconductor (CMOS) process and packaged in a CQFN40. For the most commonly used sensing distance of 4 mm, the computed distance deviation of the Application-Specific Integrated Circuit (ASIC) chips falls within the range of mm. According to the test results of the ASIC chips, this non-linear temperature compensation model successfully achieves real-time and high-accuracy computation within a wide temperature range with low hardware resource consumption.

Radar Distance Measurement With Viterbi Algorithm to Resolve Phase Ambiguity

In this article, a precise phase-based radar distance measurement system using a Viterbi decoder to resolve phase ambiguity is presented. Relying on the frequency-modulated continuous-wave radar principle, the introduced method evaluates both the frequency and the phase of the intermediate frequency signal, thus achieving absolute distance measurement with high accuracy. To avoid ambiguity in the distance estimates by half the wavelength of the radio frequency signal, which commonly arises due to 2π ambiguity in the measurement of the phase, the proposed Viterbi-based method estimates the most likely sequence of distances between the radar front end and the target. To verify the system concept, measurements using a commercial 77 GHz radar module were carried out, whereby the contour of a predefined surface containing steps in the height profile had to be determined. The experimental results confirm that the Viterbi-based approach is suitable to resolve phase ambiguity since accurate and repeatable distance estimates were obtained.

BLE Beacons for Indoor Positioning at an Interactive IoT-Based Smart Museum

The Internet of Things (IoT) can enable smart infrastructures to provide advanced services to the users. New technological advancement can improve our everyday life, even simple tasks as a visit to the museum. In this article, an indoor localization system is presented, to enhance the user experience in a museum. In particular, the proposed system relies on Bluetooth Low Energy (BLE) beacons proximity and localization capabilities to automatically provide the users with cultural contents related to the observed artworks. At the same time, a received signal strength-based technique is used to estimate the location of the visitor in the museum. An Android application is developed to estimate the distance from the exhibits and collect useful analytics regarding each visit and provide a recommendation to the users. Moreover, the application implements a simple Kalman filter in the smartphone, without the need of the cloud, to improve localization, precision and accuracy. Experimental results on distance estimation, location, and detection accuracy show that BLE beacon is a promising solution for an interactive smart museum. The proposed system has been designed to be easily extensible to the IoT technologies and its effectiveness has been evaluated through experimentation.

A Novel Range-Free Localization Scheme Based on Anchor Pairs Condition Decision in Wireless Sensor Networks

It is essential to acquire the location of sensor nodes in the wireless sensor networks (WSNs), since the data collected with nodes would become meaningless without their location. In the existing studies, range-free localization schemes have been proved suitable to large-scaled WSNs due to the low cost in hardware implementation. However, the defect of those schemes is their poor accuracy in anisotropic networks with coverage holes. To tackle this problem, we propose a range-free localization scheme that combines the advantages of geometric constraint and hop progress-based methods. The geometric information provided by the combination of anchor pairs and unknown nodes is used to design the discrimination conditions, and each node divides the anchor pairs into one of three proposed categories. For different categories of anchor pairs, corresponding methods are proposed to estimate the distancse between sensor nodes. In this way, the trade-off between distance estimation accuracy and anchor utilization can be achieved. Simulation results indicate that the proposed scheme outperforms other schemes in terms of localization accuracy and proportion of outliers with acceptable computational and time complexity, where the localization accuracy and proportion of outliers in the proposed scheme are improved by up to 47% and 61% compared to DV-maxHop.

Toward Accurate Intervehicle Positioning Based on GNSS Pseudorange Measurements Under Non-Gaussian Generalized Errors

Intervehicle accurate positioning information is a cardinal prerequisite for proper and efficient management for many vehicular applications, such as collision avoidance, navigation, and intelligent transportation systems (ITSs). To improve the positioning accuracy, collaborative vehicular localization techniques based on fused data from different sources have been proposed in vehicular ad hoc networks (VANETs). Although the existing positioning techniques achieve high precision, they heavily rely on large-scale infrastructures and/or on the support of specific hardware devices, some of which are not generally accessible or are expensive, so they are impractical in many scenarios. In this article, a novel collaborative vehicular localization approach termed non-Gaussian weighted least-squares (${n}$ GWLS) is proposed to estimate the intervehicle distances. In this approach, the global navigation satellite system (GNSS) pseudorange double-difference (PDD) measurements are adopted, whereas the position dilution of precision (PDOP) and the number of common satellites of two given GNSS receivers are used to evaluate the quality of the PDD. To achieve reliable pseudorange measurements, the uncommon noise specific to a GNSS receiver and satellite is utilized as non-Gaussian distributed, where the generalized error distribution (GED) is adopted as an approximation to non-Gaussian densities. Moreover, the weighted least-squares technique is exploited, which considers the signal quality of each pseudorange measurement. The comparison study based on real-world experiments and statistical analysis demonstrates that the proposed method can effectively obtain an accurate intervehicle distance estimation.

RSS Ranging Algorithm Based on Kalman Filtering Combined BP Neural Network

Accurate distance estimation is an important prerequisite for positioning using wireless sensor networks (WSN). However, the path loss model (PLM) cannot accurately express the relationship between distance and received signal strength (RSS), because of the complexity of the indoor environment. Therefore, this paper proposes an improved Kalman filter as the RSS preprocessing method to make up for the defects of PLM. The improved algorithm uses BP neural network to correct the Kalman filter error, improve the filtering performance, and use the compensated RSS for PLM-based ranging. The experiment shows that the proposed method has better ranging accuracy than the method based on Kalman filtering. And the average is 0.29m, which has dropped by 0.61m.

FPGA-Based neural network for accurate distance estimation of elderly falls using WSN in an indoor environment

In wireless sensor networks (WSNs), location or distance estimation information for the elderly is a critical issue. This study aims to improve the error in estimated distance between the anchor node and mobile node, which is carried by an elderly person while moving in an indoor environment. The distance between the mobile node and the anchor node was determined based on the measured received signal strength indicator (RSSI) of the anchor nodes and the artificial neural network (ANN). The ANN was implemented into a field programmable gate array (FPGA) so that it could be used in a real-world application. The hardware implementation of the FPGA was executed based on Xilinx (Virtex7). The results revealed that an accurate estimate of the distance error was obtained based on the ANN. The distance error in terms of mean absolute error was 0.019 m for training, 0.07 m for testing, and 0.19 m for validation. In addition, the estimated distances obtained from the FPGA were fully compatible with the actual distances. Moreover, the distance estimation error-based ANN outperformed other existing algorithms and soft computing techniques.

Extended Kalman Filter (EKF) Design for Vehicle Position Tracking Using Reliability Function of Radar and Lidar.

Detection and distance measurement using sensors is not always accurate. Sensor fusion makes up for this shortcoming by reducing inaccuracies. This study, therefore, proposes an extended Kalman filter (EKF) that reflects the distance characteristics of lidar and radar sensors. The sensor characteristics of the lidar and radar over distance were analyzed, and a reliability function was designed to extend the Kalman filter to reflect distance characteristics. The accuracy of position estimation was improved by identifying the sensor errors according to distance. Experiments were conducted using real vehicles, and a comparative experiment was done combining sensor fusion using a fuzzy, adaptive measure noise and Kalman filter. Experimental results showed that the study’s method produced accurate distance estimations.

Object Distance Estimation Using a Single Image Taken from a Moving Rolling Shutter Camera.

This paper proposes a technique to estimate the distance between an object and a rolling shutter camera using a single image. The implementation of this technique uses the principle of the rolling shutter effect (RSE), a distortion within the rolling-shutter-type camera. The proposed technique has a mathematical strength compared to other single photo-based distance estimation methods that do not consider the geometric arrangement. The relationship between the distance and RSE angle was derived using the camera parameters (focal length, shutter speed, image size, etc.). Mathematical equations were derived for three different scenarios. The mathematical model was verified through experiments using a Nikon D750 and Nikkor 50 mm lens mounted on a car with varying speeds, object distances, and camera parameters. The results show that the mathematical model provides an accurate distance estimation of an object. The distance estimation error using the RSE due to the change in speed remained stable at approximately 10 cm. However, when the distance between the object and camera was more than 10 m, the estimated distance was sensitive to the RSE and the error increased dramatically.

Obstacle distance measurement based on binocular vision for high-voltage transmission lines using a cable inspection robot.

Obstacle distance measurement is one of the key technologies for cable inspection robots on high-voltage transmission lines. This article develops a novel method based on binocular vision for extracting the feature points of images and reconstructing 3D scenes. The proposed method seamlessly incorporates camera calibration, dense stereo matching, and 3D reconstruction. We apply a novel calibration method to acquire intrinsic and extrinsic parameters and use an improved Semi-Global Matching (SGM) algorithm based on the least squares fitting interpolation to refine the basic disparity map. Based on the depth information of the optimized disparity map and the principle of binocular vision measurement, a model is established to estimate the distance of an obstacle from the cable inspection robot. Extensive experiments show that the proposed method achieves an estimation accuracy of less than 5% from 0.5 m to 5.0 m, offering extremely high distance estimation accuracy and robustness. The study improves the autonomy and intelligence of inspection robots used in the power industry.

Investigating surface correction relations for RGB stars

ABSTRACTState-of-the-art stellar structure and evolution codes fail to adequately describe turbulent convection. For stars with convective envelopes such as red giants, this leads to an incomplete depiction of the surface layers. As a result, the predicted stellar oscillation frequencies are haunted by systematic errors, the so-called surface effect. Different empirically and theoretically motivated correction relations have been proposed to deal with this issue. In this paper, we compare the performance of these surface correction relations for red giant branch stars. For this purpose, we apply the different surface correction relations in asteroseismic analyses of eclipsing binaries and open clusters. In accordance with previous studies of main-sequence stars, we find that the use of different surface correction relations biases the derived global stellar properties, including stellar age, mass, and distance estimates. We, furthermore, demonstrate that the different relations lead to the same systematic errors for two different open clusters. Our results overall discourage from the use of surface correction relations that rely on reference stars to calibrate free parameters. Due to the demonstrated systematic biasing of the results, the use of appropriate surface correction relations is imperative to any asteroseismic analysis of red giants. Accurate mass, age, and distance estimates for red giants are fundamental when addressing questions that deal with the chemo-dynamical evolution of the Milky Way galaxy. In this way, our results also have implications for fields such as galactic archaeology that draw on findings from stellar physics.

Joint estimation of binaural distance and azimuth by exploiting deep neural networks.

The state-of-the-art supervised binaural distance estimation methods often use binaural features that are related to both the distance and the azimuth, and thus the distance estimation accuracy may degrade a great deal with fluctuant azimuth. To incorporate the azimuth on estimating the distance, this paper proposes a supervised method to jointly estimate the azimuth and the distance of binaural signals based on deep neural networks (DNNs). In this method, the subband binaural features, including many statistical properties of several subband binaural features and the binaural spectral magnitude difference standard deviation, are extracted together as cues to jointly estimate the azimuth and the distance using binaural signals by exploiting a multi-objective DNN framework. Especially, both the azimuth and the distance cues are utilized in the learning stage of the error back-propagation in the multi-objective DNN framework, which can improve the generalization ability of the azimuth and the distance estimation. Experimental results demonstrate that the proposed method can not only achieve high azimuth estimation accuracy but can also effectively improve the distance estimation accuracy when compared with several state-of-the-art supervised binaural distance estimation methods.

Opportunistic Calibration Method for Walking Distance Estimation Using a Waist-Mounted Inertial Sensor

This article introduces an opportunistic calibration method for walking distance estimation using a waist-mounted inertial sensor. In the proposed method, the calibration data are automatically collected from daily normal walking and it does not require a laboratory calibration procedure. While the walking steps can be accurately detected, the walking step length estimation is more difficult in a waist-mounted sensor case. In our method, when short walking data are detected from normal walking, a smoothing algorithm is used to estimate its walking distance. The walking step length and walking features of these data are used to update the step length model. Two experiments are conducted to evaluate the performance of the proposed method. The accuracy of smoother-based short walking distance estimation has been investigated in the first experiment. This experiment also investigates which feature is the best fit for our linear step length model. The second experiment is to demonstrate the opportunistic nature of the proposed method; 3–10 walking steps segments are extracted from 2 h walking data of one healthy subject and used to calibrate his walking profile. This method can be applied to a consumer device for human walking distance estimation without laboratory calibration procedure.

Fast, Accurate Vehicle Detection and Distance Estimation

A large number of people suffered from traffic accidents each year, so people pay more attention to traffic safety. However, the traditional methods use laser sensors to calculate the vehicle distance at a very high cost. In this paper, we propose a method based on deep learning to calculate the vehicle distance with a monocular camera. Our method is inexpensive and quite convenient to deploy on the mobile platforms. This paper makes two contributions. First, based on Light-Head RCNN, we propose a new vehicle detection framework called Light-Car Detection which can be used on the mobile platforms. Second, the planar homography of projective geometry is used to calculate the distance between the camera and the vehicles ahead. The results show that our detection system achieves 13FPS detection speed and 60.0% mAP on the Adreno 530 GPU of Samsung Galaxy S7, while only requires 7.1MB of storage space. Compared with the methods existed, the proposed method achieves a better performance.

Accurate and precise distance estimation for noisy IR sensor readings contaminated by outliers

Infrared(IR) sensors are widely used to estimate distance due to their practicality and cost-effectiveness. The distance is estimated by measuring the reflectance amplitude of IR light from a targeted object. However, the non-linearity characteristic of the reflected IR light makes distance estimation difficult. Additionally, IR sensors have low accuracy and precision. Some researches superficially studied distance estimation using IR sensors. However, a deeper study is needed on this subject as competitive market conditions push companies to produce high-quality devices without increasing the production cost. Therefore, this paper proposed a procedure on improving the accuracy and precision of distance estimation of a low-cost, but noisy IR sensor. The proposed procedure contains six complementary steps. Experimental and simulation validation is performed to prove the effectiveness of the proposed procedure. In the validation, the distance estimation error never exceeds ±2 mm between 10 cm and 400 cm.

Are average speed emission functions scale-free?

Although emission models have been designed using vehicle data over driving cycles of a few minutes, they are often applied at large scale to estimate total emission (inventories). In between, there is a range of scales in use in traffic and environmental studies (road sections, sub-areas, etc.). Coupling a traffic microsimulation with COPERT emission factors at different scales reveals scaling biases. We compare network fuel consumption (FC) and nitrogen oxide (NOx) emissions resulting from emission calculations based on different spatial decompositions. The results show that for an area of Paris covering 3 km2, the differences due to the aggregation scale for emissions range from 5 to 17% depending on the pollutant, spatial partitioning and traffic conditions. These discrepancies can be reduced using a distance-weighted mean speed, which is not a scale-consistent definition of mean travel speed. They can almost be cancelled by using a correction term derived analytically in this paper, thus consistency can be guaranteed between emissions assessed at different scales. Finally, a case study shows that it is possible to evaluate FC and NOx emissions on a large-scale network from a sample of traffic data (probes), and obtain the corrective term to be applied to remove scaling bias. The most critical step is the accurate estimation of the total travel distance. The gaps were successfully reduced to a maximum of 8% in congestion for a penetration rate of about 20%.

A Planar Location Method for DC Arc Faults Using Dual Radiation Detection Points and DANN

Arc faults in dc microgrids or photovoltaic (PV) systems are difficult to detect and isolate. Especially in location research, there are few related results, and arc fault detection is immature. This article proposes a planar location method that requires only two detection points. Because the height of many dc systems is much smaller than their horizontal dimension, an antenna array and a fault source form a horizontal triangle to locate the dc arc faults. Then, the signal pulses are distinguished and extracted using a cross correlation technique. A neural network (NN) and received signal strength indicators (RSSIs) are used to estimate the arc distance. However, the data from the dual detection points are insufficient for meeting the demands of NN training and may lead to a large location error. Therefore, a data-augmented NN (DANN) technique was introduced into the algorithm to enhance the accuracy and robustness of distance estimation. The experimental verification results show that the method can effectively locate faults under a variety of arcing conditions. Moreover, the proposed method has intrinsic immunity to normal current fluctuations (NCFs). It can be used in PV plants or smart buildings with solar cells to avoid nuisance trips and improve the effectiveness of fault protection.

PM-LSH

Nearest neighbor (NN) search in high-dimensional spaces is inherently computationally expensive due to the curse of dimensionality. As a well-known solution to approximate NN search, locality-sensitive hashing (LSH) is able to answer c-approximate NN ( c -ANN) queries in sublinear time with constant probability. Existing LSH methods focus mainly on building hash bucket based indexing such that the candidate points can be retrieved quickly. However, existing coarse-grained structures fail to offer accurate distance estimation for candidate points, which translates into additional computational overhead when having to examine unnecessary points. This in turn reduces the performance of query processing. In contrast, we propose a fast and accurate LSH framework, called PM-LSH, that aims to compute the c -ANN query on large- scale, high-dimensional datasets. First, we adopt a simple yet effective PM-tree to index the data points. Second, we develop a tunable confidence interval to achieve accurate distance estimation and guarantee high result quality. Third, we propose an efficient algorithm on top of the PM-tree to improve the performance of computing c -ANN queries. Extensive experiments with real-world data offer evidence that PM-LSH is capable of outperforming existing proposals with respect to both efficiency and accuracy.

Using the Power Delay Profile to Accelerate the Training of Neural Network-Based Classifiers for the Identification of LOS and NLOS UWB Propagation Conditions

ultra-wideband (UWB) technology enables centimeter-level localization systems based on the accurate estimation of the actual distance between transmitter and receiver, by means of the precise estimation of the signal time-of-flight. However, this is only possible when correctly detecting the first path of the incoming signal instead of a bounce or a reflection, which becomes challenging in non line-of-sight (NLOS) situations. There are many different approaches in the literature to alleviate the wrong detection of the first incoming UWB signal path. One of them considers machine learning techniques to design classifiers capable of distinguishing between line-of-sight (LOS) and NLOS propagation from available signal features. However, the performance and complexity of the obtained classifiers depend largely on the size of the input data associated to such features. Thus, features such as the channel impulse response (CIR) produce large amounts of data, yielding very complex classifiers. In this paper, we propose using a downsampled power delay profile (PDP) as an alternative feature consisting of input data much smaller than the CIR, although sufficiently representative, hence resulting in a lower computational cost while exhibiting a similar classification performance. Furthermore, another of the tasks addressed in this work is the study of the impact on the classification results of using a dataset for training where the samples of each class are not balanced from the point of view of energy. Finally, this work also studies how the classifiers based on the CIR or the PDP improve their performance when considering additional signal features such as the estimated range value or its energy level.

Validity of Two Wheelchair-Mounted Devices for Estimating Wheelchair Speed and Distance Traveled.

This study evaluated the validity of two wheelchair-mounted devices-the Cateye® and Wheeler-for monitoring wheelchair speed and distance traveled. Speed estimates were validated against a calibrated treadmill at speeds from 1.5 to 10km/hr. Twenty-five wheelchair users completed a course of known distance comprising a sequence of everyday wheelchair activities. Speed estimate validity was very good (mean absolute percentage error ≤ 5%) for the Wheeleri at all speeds and for the Cateye at speeds >3km/hr but not speeds <3km/hr (mean absolute percentage error > 20%). Wheeleri distance estimates were good (mean absolute percentage error < 10%) for linear pushing activities and general maneuvering but poor for confined-space maneuvering. Cateye estimates were good for continuous linear propulsion but poor for discontinuous pushing and maneuvering (both general and confined space). Both devices provided valid estimates of speed and distance for typical wheelchair-based exercise activities. However, the Wheeleri provided more accurate estimates of speed and distance during typical everyday wheelchair activities.