Distance Estimation Error Research Articles

A scanning spatial-wavenumber filter and PZT 2-D cruciform array based on-line damage imaging method of composite structure

The spatial-wavenumber filtering technique of Lamb wave is gradually applied for damage inspection of composite structure in recent years because it is an effective approach to distinguish the wave propagating direction and mode. But for on-line damage monitoring of composite structure by using spatial-wavenumber filter, the problem is how to realize the spatial-wavenumber filtering of Lamb wave when the wavenumber response cannot be measured or modeled. This paper proposes a scanning spatial-wavenumber filter and piezoelectric sensor (referred to as PZT) 2-D cruciform array based on-line damage imaging method of composite structure. In this method, a 2-D cruciform array constructed by two linear PZT arrays is placed on composite structure permanently to acquire Lamb wave damage scattering signal on-line. For one linear PZT array, a scanning spatial-wavenumber filter which does not rely on any modeled or measured wavenumber response is designed to filter the damage scattering signal at a designed wavenumber bandwidth to give out a wavenumber-time image. Based on the image, the wavenumber of the damage scattering signal projecting at the array can be obtained. The same process can be also applied to the other linear PZT array to get the wavenumber projecting at that array direction. By combining with the two projection wavenumbers, the damage can be localized without blind angle. The method is validated on an aircraft composite oil tank of variable thickness. The validation results show that the damage direction estimation error is less than 2° and the damage distance estimation error is around 20mm in the monitoring area of nearly 600mm×300mm. It indicates an acceptable performance of the damage imaging method for complex composite structure.

Support vector machine based fault classification and location of a long transmission line

This paper investigates support vector machine based fault type and distance estimation scheme in a long transmission line. The planned technique uses post fault single cycle current waveform and pre-processing of the samples is done by wavelet packet transform. Energy and entropy are obtained from the decomposed coefficients and feature matrix is prepared. Then the redundant features from the matrix are taken out by the forward feature selection method and normalized. Test and train data are developed by taking into consideration variables of a simulation situation like fault type, resistance path, inception angle, and distance. In this paper 10 different types of short circuit fault are analyzed. The test data are examined by support vector machine whose parameters are optimized by particle swarm optimization method. The anticipated method is checked on a 400kV, 300km long transmission line with voltage source at both the ends. Two cases were examined with the proposed method. The first one is fault very near to both the source end (front and rear) and the second one is support vector machine with and without optimized parameter. Simulation result indicates that the anticipated method for fault classification gives high accuracy (99.21%) and least fault distance estimation error (<0.21%) for all discussed cases. In order to verify the accuracy of the proposed method, a comparison is carried out with methods published by other researchers. Separate investigation is also carried out with the transmission line placing thyristor controlled series capacitor in the middle and applying the same proposed method. It is observed from the test results of the thyristor controlled series capacitor based transmission line model that fault classification gives a high accuracy of 98.36% and absolute fault location error is >0.29%.

A swarm-based approach to real-time 3D indoor localization: Experimental performance analysis

In this paper, the problem of indoor localization in wireless networks is addressed relying on a swarm-based approach. We assume to know the positions of a few number of sensor nodes, denoted as anchor nodes (ANs), and we aim at finding the position of a target node (TN) on the basis of the estimated distances between each AN and the considered TN. Since ultra wide band (UWB) technology is particularly suited for localization purposes (owing to its remarkable time resolution), we consider a network composed of UWB devices. More precisely, we carry out an experimental investigation using the PulsOn 410 ranging and communication modules (RCMs) produced by time domain. Using four of them as ANs and one of them as TN, various topologies are considered in order to evaluate the accuracy of the proposed swarm-based localization approach, which relies on the pairwise (AN-TN) distances estimated by the RCMs. Then, we investigate how the accuracy of the proposed localization algorithm changes if we apply to the distance estimates a recently proposed stochastic correction, which is designed to reduce the distance estimation error. Our experimental results show that a good accuracy is obtained in all the considered scenarios, especially when applying the proposed swarm-based localization algorithm to the stochastically corrected distances. The obtained results are satisfying also in terms of software execution time, making the proposed approach applicable to real-time dynamic localization problems.

A Monocular Vision Sensor-Based Obstacle Detection Algorithm for Autonomous Robots.

This paper presents a monocular vision sensor-based obstacle detection algorithm for autonomous robots. Each individual image pixel at the bottom region of interest is labeled as belonging either to an obstacle or the floor. While conventional methods depend on point tracking for geometric cues for obstacle detection, the proposed algorithm uses the inverse perspective mapping (IPM) method. This method is much more advantageous when the camera is not high off the floor, which makes point tracking near the floor difficult. Markov random field-based obstacle segmentation is then performed using the IPM results and a floor appearance model. Next, the shortest distance between the robot and the obstacle is calculated. The algorithm is tested by applying it to 70 datasets, 20 of which include nonobstacle images where considerable changes in floor appearance occur. The obstacle segmentation accuracies and the distance estimation error are quantitatively analyzed. For obstacle datasets, the segmentation precision and the average distance estimation error of the proposed method are 81.4% and 1.6 cm, respectively, whereas those for a conventional method are 57.5% and 9.9 cm, respectively. For nonobstacle datasets, the proposed method gives 0.0% false positive rates, while the conventional method gives 17.6%.

Self-localization of autonomous underwater vehicles with accurate sound travel time solution

In underwater acoustic sensor networks, long baseline localization for autonomous underwater vehicles (AUVs) requires distance estimation that always encounters severe problems: (a) Time-synchronization is hard to achieve in underwater environment, which baffles ranging methods based on the synchronized time. (b) Long propagation delay of acoustic signals and the impact of AUVs’ mobility make it rash to use the round trip ranging (RTR) technology. (c) Sound speed uncertainty enlarges the inaccuracy of distance estimation. This work addresses those problems above and proposes an AUVs self-localization algorithm with accurate sound travel time solution (SL-STTS), which is time-synchronization free and ranging optimization based. Simulation results show that under the measurement noise of time and angles, the root mean square error of SL-STTS is decreased by about 8–79% compared with the counterparts. In addition the average distance estimation error of SL-STTS is declined by 42% compared with RTR.

Effects of ambient noise on detectability and localization ofavian songs and tones by observers in grasslands.

Probability of detection and accuracy of distance estimates in aural avian surveys may be affected by the presence of anthropogenic noise, and this may lead to inaccurate evaluations of the effects of noisy infrastructure on wildlife. We used arrays of speakers broadcasting recordings of grassland bird songs and pure tones to assess the probability of detection, and localization accuracy, by observers at sites with and without noisy oil and gas infrastructure in south-central Alberta from 2012 to 2014. Probability of detection varied with species and with speaker distance from transect line, but there were few effects of noisy infrastructure. Accuracy of distance estimates for songs and tones decreased as distance to observer increased, and distance estimation error was higher for tones at sites with infrastructure noise. Our results suggest that quiet to moderately loud anthropogenic noise may not mask detection of bird songs; however, errors in distance estimates during aural surveys may lead to inaccurate estimates of avian densities calculated using distance sampling. We recommend caution when applying distance sampling if most birds are unseen, and where ambient noise varies among treatments.

Complete power distribution system representation and state determination for fault location

&lt;p&gt;The impedance-based approaches for fault location in power distribution systems determine a faulted line section. Next, these require of the estimation of the voltages and currents at one or both section line ends to exactly determine the fault location. It is a challenge because in most of the power distribution systems, measurements are only available at the main substation. This document presents a modeling proposal of the power distribution system and an easy implementation method to estimate the voltages and currents at the faulted line section, using the measurements at the main substation, the line, load, transformer parameters and other serial and shunt connected devices and the power system topology. The approach here proposed is tested using a fault locator based on superimposed components, where the distance estimation error is lower than 1.5% in all of the cases. &lt;/p&gt;

Distribution of the zero-effort miss distance estimation error in interception problems

In interception problems subject to noise corrupted measurements and random bounded target maneuvers, the miss distance is a random variable with an a-priori unknown distribution. In previous works, such a miss distance distribution could be obtained by a computational approach, assuming that the distribution of the zero-effort miss distance estimation error is known as a function of time. In this paper, this assumption is replaced by an analytical approach based on the knowledge of the measurement error distribution, the estimator structure and the (possibly non-linear) guidance law. For this purpose the shaping filter technique is used, considering that the excitation noise due to the random target maneuvers, as well as the observation noise, are Gaussian white noises. This analytical approach is illustrated by numerical examples.

DEDF: lightweight WSN distance estimation using RSSI data distribution-based fingerprinting

When estimating the distance for wireless sensor networks (WSNs), we always suppose that a fixed curve model exists between the received signal strength indicator (RSSI) and communication distance. But there exist some negative factors in practice, which makes this assumption to contradict with the situation in real communication environment. It results in large distance estimation error with low efficiency. Thus, a lightweight WSN communication distance estimation method is presented, which is called distance estimation using distribution-based fingerprinting. First, we considered the uncertainty in RSSI values, and got the fingerprinting relationship in terms of RSSI data distribution, which is gained through a statistical calculation. Then, a data matching algorithm is implemented to estimate the communication distance. Finally, RSSI values in different conditions are utilized to validate this method. Experimental results demonstrated that the new method can obtain better results with high efficiency than other related methods, and can be applied in WSN localization system.

Accurate Range-Free Localization for Anisotropic Wireless Sensor Networks

Position information plays a pivotal role in wireless sensor network (WSN) applications and protocol/algorithm design. In recent years, range-free localization algorithms have drawn much research attention due to their low cost and applicability to large-scale WSNs. However, the application of range-free localization algorithms is restricted because of their dramatic accuracy degradation in practical anisotropic WSNs, which is mainly caused by large error of distance estimation. Distance estimation in the existing range-free algorithms usually relies on a unified per hop length (PHL) metric between nodes. But the PHL between different nodes might be greatly different in anisotropic WSNs, resulting in large error in distance estimation. We find that, although the PHL between different nodes might be greatly different, it exhibits significant locality ; that is, nearby nodes share a similar PHL to anchors that know their positions in advance. Based on the locality of the PHL, a novel distance estimation approach is proposed in this article. Theoretical analyses show that the error of distance estimation in the proposed approach is only one-fourth of that in the state-of-the-art pattern-driven scheme (PDS). An anchor selection algorithm is also devised to further improve localization accuracy by mitigating the negative effects from the anchors that are poorly distributed in geometry. By combining the locality-based distance estimation and the anchor selection, a range-free localization algorithm named &lt;underline&gt;S&lt;/underline&gt;elective &lt;underline&gt;M&lt;/underline&gt;ultilateration (SM) is proposed. Simulation results demonstrate that SM achieves localization accuracy higher than 0.3 r , where r is the communication radius of nodes. Compared to the state-of-the-art solution, SM improves the distance estimation accuracy by up to 57% and improves localization accuracy by up to 52% consequently.

Afocal optical flow sensor for reducing vertical height sensitivity in indoor robot localization and navigation.

This paper introduces a novel afocal optical flow sensor (OFS) system for odometry estimation in indoor robotic navigation. The OFS used in computer optical mouse has been adopted for mobile robots because it is not affected by wheel slippage. Vertical height variance is thought to be a dominant factor in systematic error when estimating moving distances in mobile robots driving on uneven surfaces. We propose an approach to mitigate this error by using an afocal (infinite effective focal length) system. We conducted experiments in a linear guide on carpet and three other materials with varying sensor heights from 30 to 50 mm and a moving distance of 80 cm. The same experiments were repeated 10 times. For the proposed afocal OFS module, a 1 mm change in sensor height induces a 0.1% systematic error; for comparison, the error for a conventional fixed-focal-length OFS module is 14.7%. Finally, the proposed afocal OFS module was installed on a mobile robot and tested 10 times on a carpet for distances of 1 m. The average distance estimation error and standard deviation are 0.02% and 17.6%, respectively, whereas those for a conventional OFS module are 4.09% and 25.7%, respectively.

Look-up table을 이용한 수중 음향파 거리 추정 알고리즘

In this paper, we introduce a underwater ranging algorithm with Look-up Table (LUT) by modifying the existing method which is using the changes of angles of accoustic rays with SSP (Sound Speed Profile). We compare the horizontal distance errors and the calculation times. Our new algorithm exploits Time of Arriva l(ToA) - horizontal distance table based on SSP. This algorithm offers faster calculation speed than the previous one with the slight increase of the distance estimation error.

Robust Power Control Under Location and Channel Uncertainty in Cognitive Radio Networks

In this letter, we consider the power control problem in cognitive radio (CR) networks when both primary user (PU) location and wireless channel are unknown. Prior work in power control assumes perfect knowledge of PU and CR locations, which is not practical due to localization errors and node mobility. We assume the distance estimation error in CR-PU links to model location uncertainties and derive the distribution of channel gain with distance-dependent path loss and shadowing. We then proceed to develop an optimization framework for CR power control, which maximizes the CR data rate under PU interference power constraint. Simulation results showing the CR data rate and interference probability to the PUs are presented to demonstrate the superior performance of the proposed algorithm compared with reference schemes.

The Influence of the Stereo Base on Blind and Sighted Reaches in a Virtual Environment

In virtual environments, perceived distances are frequently reported to be shorter than intended. One important parameter for spatial perception in a stereoscopic virtual environment is the stereo base—that is, the distance between the two viewing cameras. We systematically varied the stereo base relative to the interpupillary distance (IPD) and examined influences on distance and size perception. Furthermore, we tested whether an individual adjustment of the stereo base through an alignment task would reduce the errors in distance estimation. Participants performed reaching movements toward a virtual tennis ball either with closed eyes (blind reaches) or open eyes (sighted reaches). Using the participants' individual IPD, the stereo base was set to (a) the IPD, (b) proportionally smaller, (c) proportionally larger, or (d) adjusted according to the individual performance in an alignment task that was conducted beforehand. Overall, consistent with previous research, distances were underestimated. As expected, with a smaller stereo base, the virtual object was perceived as being farther away and bigger, in contrast to a larger stereo base, where the virtual object was perceived to be nearer and smaller. However, the manipulation of the stereo base influenced blind reaching estimates to a smaller extent than expected, which might be due to a combination of binocular disparity and pictorial depth cues. In sighted reaching, when visual feedback was available, presumably the use of disparity matching led to a larger effect of the stereo base. The use of an individually adjusted stereo base diminished the average underestimation but did not reduce interindividual variance. Interindividual differences were task specific and could not be explained through differences in stereo acuity or fixation disparity.

An efficient adaptive method for estimating the distance between mobile sensors

The received signal strength (RSS) is a common source of information used for estimating the distance between two wireless nodes, whether these nodes are stationary or mobile. Minimum mean squared error distance estimation methods that use the RSS require prior knowledge of both the variance of the noise and, in the case of mobile sensors, the dynamics of the nodes' mobility. In mobile applications, where low computational complexity is important, pseudo-optimal estimations are preferred, as they do not require such information. In this case, the maximum likelihood estimator (MLE) is often used. In this paper, we propose an efficient pseudo-optimal log-power based distance estimation method using RSS under lognormal shadowing, that improves the MLE. It does not require a priori knowledge either of the movement dynamics or of the variance of the noise. The method is based on adaptively minimizing the variance of the prediction error, using a random walk model with correlated increments. It is analytically demonstrated that the distance estimation error variance of the proposed method improves the MLE in both the static and mobile cases. We use a simulated velocity model example to compare its performance with other algorithms in this group, such as the linear mean square filter and the Gauss---Newton search.

A Two-Stage Range-Free Localization Method for Wireless Sensor Networks

Range-free localization plays an important role in low-cost and large scale wireless sensor networks. Many existing range-free localization methods encounter high localization error, especially for the network with a coverage hole. One reason for high localization error is unreasonable distance estimation method. Another reason is that unknown nodes use the shortest distance which has large cumulative distance error to estimate their positions. In this paper, a two-stage centralized range-free localization algorithm (TCRL) is proposed. In the first stage, we design a novel rational distance estimation method to alleviate the distance estimation error between neighbor nodes based on the connectivity information and geometric features. In the second stage, a novel neighborhood function is derived from the estimated distances between neighbor nodes. Then a new localization strategy is proposed based on greedy idea. Finally, the proposed algorithm is compared with the same type algorithms in two network scenarios, namely, random deployment and random deployment with a coverage hole. The simulation results show that TCRL achieves more accurate and reliable results than most of existing range-free methods in the two network scenarios.

DDEUDSC: A Dynamic Distance Estimation using Uncertain Data Stream Clustering in mobile wireless sensor networks

In RSSI (Received Signal Strength Indicator)-based communication distance estimation of mobile wireless sensor network localization, RSSI is assumed to exponential attenuation with increment of communication distance in ideal radio propagation models, which is invalid due to the uncertainty of RSSI data in real communication environment, resulting in considerable error of communication distance estimation. Moreover, dynamic distance estimation demands a high efficiency of computation for the continual generation of RSSI data stream in the mobile node. This paper develops a new dynamic communication distance estimation method using uncertain interval data stream clustering, named as DDEUDSC (Dynamic Distance Estimation method using Uncertain Data Stream Clustering). First, statistical information of RSSI data is used to represent the RSSI-D mapping relationship in terms of interval data. Then we consider the data pattern composed of some consecutive cluster centers, and apply it in our uncertain RSSI data stream clustering algorithm to estimate the dynamic communication distance. Finally, RSSI data streams in three typical communication environments are conducted for experiments. The experimental results show the proposed method is an effective way to improve RSSI-D estimation precision in RSSI data stream with uncertainty and dynamics characteristic.

A closer look at mechanisms underlying perceptual differences in Parkinson’s freezers and non-freezers

Parkinson’s disease patients who suffer from freezing of gait (PD-FOG) may have sensory and/or perceptual deficits, although they are difficult to disentangle. This study evaluated whether visuospatial perception or self-motion perception were more impaired in PD-FOG, and whether distance estimation errors might be related to misperception of physical walking (compared to imagined). Finally, cognitive status was evaluated in order to evaluate whether cognitive status predicts any of the perception deficits identified. Nine PD-FOG and 15 PD-nonFOG were tested. In experiment 1, participants were shown a target, then the target was removed, before participants demonstrated the original position of the target in two different feedback conditions (pointing with a laser, or walking to its original position). In experiment 2, participants walked to a target (3, 4.5, 6m) and then imagined walking to that same target. The time to complete both of these tasks was measured and compared. Experiment 1 found a significantly greater judgment error in PD-FOG across both conditions (p=0.013) (compared to PD-nonFOG). Constant error revealed that both groups significantly underestimated during the self-motion condition only (p=0.01). Interestingly, results from experiment 2 demonstrated a significant discrepancy between the time it took to imagine walking compared to their actual movement times, specifically in PD-FOG (p=0.03). This mismatch as well as cognitive status significantly predicted judgment errors during the self-motion condition from experiment 1. Therefore, this study found evidence that PD-FOG have significantly greater sensory–perception deficits compared to PD-nonFOG. These findings have important clinical implications for further understanding FOG and developing new rehabilitative strategies for FOG symptoms.

Adaptive distance relay algorithm for double circuit line with series compensation

With the onset of fault, nonlinear operation of Metal Oxide Varistor (MOV) in series compensated line introduces error in distance estimation. Distance relay reach accuracy depends on several factors. Mutual coupling effect in double circuit line is highly nonlinear and depends on circuit configuration, fault resistance and remote source infeed. This may lead to inadvertent operation of distance relay for faults outside the operating zone. Variations in system condition, effect of shunt capacitance effect and fault resistance influence distance relay healthy operation. Adaptive setting in digital platform is a greater advantage to estimate the change-in system condition and modify the relay characteristics using the recent information. This paper presents an adaptive algorithm for protection of double circuit series compensated line using local end information. The adaptive algorithm is derived for single-line-to-ground fault condition. A 400kV two source system is considered and simulated using EMTDC/PSCAD.

Orthogonal Regression Based Multihop Localization Algorithm for Large-Scale Underwater Wireless Sensor Networks

For large-scale underwater wireless sensor networks (UWSNs) with a minority of anchor nodes, multihop localization is a popular scheme for determining the geographical positions of normal nodes. However, existing multihop localization studies have considered the anchor positions to be free of errors, which is not a valid assumption in practice. In this paper, the problems existing in nonlinear least square-based node self-localization schemes are analyzed, and the biased distribution characteristic of multihop distance estimation errors is pointed out. Then, the orthogonal regression method is employed for the localization of normal nodes in the presence of anchor position errors. In particular, the influences of errors in independent variables and biases in dependent variables on node coordinate estimation are taken into account simultaneously. Extensive simulation results illustrate the robustness and effectiveness of our method.