Distance Estimation Scheme Research Articles

An enhanced node segmentation and distance estimation scheme with a reduced search space boundary and improved PSO for obstacle-aware wireless sensor network localization

This paper proposes an enhanced method for localizing sensor nodes in wireless sensor networks with obstacles. Such environment settings lead to lower localization accuracy because locations are estimated based on detour distances circumventing the obstacles; we, therefore, improve the segmentation technique to address the issue as they divide the whole area into multiple smaller ones, each containing fewer or no obstacles. Nevertheless, when radio transmissions between sensor nodes are obstructed (as simulated by the radio irregularity model), the signal-strength variation tends to be high, reducing localization accuracy; thus, we provide a method for accurately approximating the distances between pairs of an anchor node (whose location is known) and an unknown node by incorporating the related error into the approximation process. Additionally, when the nodes with unknown locations are outside the polygon formed by the anchor nodes, the search area for localization is relatively large, resulting in lower accuracy and a longer search time; we then propose a method for reducing the size of approximation areas by forming boundaries based on the two intersection points between the ranges of two anchor nodes used to localize an unknown node. However, these reduced search areas could still be large; we further increase the accuracy of the PSO location estimation method by adaptively adjusting the number of particles. In addition, with PSO, the accuracy of unknown node location estimation depends on having a properly selected fitness function; therefore, we incorporate appropriate variables to reflect the distance approximation accuracy between each anchor-unknown node pair. In experiments, we measure performance in sensor node deployment areas of three different shapes: C-shaped, with 1 hole, and with 2 rectangular holes. The results show that our method provides higher localization accuracy than others in small-, medium-, and large-scaled WSNs. Specifically, our proposed method is 27.46%, 49.28%, 50.33%, and 74.62% more accurate on average than IDE-NSL, PSO–C, min-max PSO, and niching PSO, respectively.

A survey on estimation schemes in molecular communications

This survey paper focuses on the estimation schemes in molecular communication (MC) systems. The existing studies in estimation schemes can be divided into parameter estimation (e.g., distance, diffusion coefficient, and flow velocity) and channel estimation. In this paper, we present, for the first time, a comprehensive survey on i) distance estimation, since distance is the most widely estimated parameter in current studies, ii) estimation of other parameters (i.e., the parameters excluding distance), and iii) channel estimation that focuses on the channel impulse response (CIR). Moreover, we examine the noise that may impact on the estimation performance and the metrics applied to evaluate the performance of different estimation schemes. Numerical results are provided to compare the performance of different distance estimation schemes. In addition, future research directions in parameter estimation and channel estimation are identified and discussed.

Deep Learning-Based Indoor Distance Estimation Scheme Using FMCW Radar

In the distance estimation scheme using Frequency-Modulated-Continuous-Wave (FMCW) radar, the frequency difference, which was caused by the time delay of the received signal reflected from the target, is calculated to estimate the distance information of the target. In this paper, we propose a distance estimation scheme exploiting the deep learning technology of artificial neural network to improve the accuracy of distance estimation over the conventional Fast Fourier Transform (FFT) Max value index-based distance estimation scheme. The performance of the proposed scheme is compared with that of the conventional scheme through the experiments evaluating the accuracy of distance estimation. The average estimated distance error of the proposed scheme was 0.069 m, while that of the conventional scheme was 1.9 m.

Distance Estimation of High-Speed Underwater Targets Based on a Frequency-Coded Continuous Wave

Robust and continuous distance estimation of a rapidly approaching threat is a significant concern in underwater technology. Although frequency modulated continuous waves (FMCWs) have been widely used in near-range situations, FMCWs are challenging to use in a specific underwater situation. If a high-speed underwater target appears, the target echo can no longer be modeled as a simple Doppler-shifted version of the wave, which increases the distance estimation error of conventional FMCW methods. This situation also requires frequent updates of distance estimates. Thus, to obtain the distance of a high-speed underwater target, we propose a signal format of a frequency coded continuous wave (FCCW) and develop an FCCW-based distance estimation scheme. The proposed distance estimation algorithm has three steps: group correlators and accumulated sum (GCAS), time-delay map (TDM), and Doppler line fitting (DLF). The structure of the proposed FCCW enables the detection of each frequency hopping time (FHT) within a shorter period than the observation time. The GCAS algorithm detects FHTs in the FCCW and is designed to easily extend the matched filter length, resulting in an extension of the acquisition time. The TDM and DLF provide robust distance estimates and enable the classification of multiple targets. Simulation results show that the proposed method can detect FHTs and estimate the target distance more stably in low signal-to-noise-ratio environments by increasing the matched filter's acquisition time.

Distance Estimation Scheme Exploiting IR-UWB Radar with Clutter Suppressing Algorithm in Indoor Environments

This paper proposes a distance estimation scheme with an impulse radio ultrawide-band (IR-UWB) radar for a long-range target in an indoor environment. When the pulse is transmitted from the IR-UWB radar and is reflected from the target, fading effects make it difficult to estimate the time-of-arrival of a direct path, which can be distorted by the clutter in indoor environments. The proposed scheme can improve the performance of distance estimation by mitigating the effect of the clutter with a low-rank approximation scheme and a singular value decomposition scheme. The performance of the proposed scheme is evaluated through experiments in a general office building, and the results of the experiment show that the proposed scheme can track the dynamic distance of a target from the radar up to 11 m in an open space and 14 m in a narrow corridor, respectively, with an average error of less than 0.2 m.

A hybrid method for fault location estimation in a fixed series compensated lines

This paper proposes a fault distance estimation scheme for fixed series capacitor compensated parallel transmission lines using discrete wavelet transform and decision tree regression. The purpose of the data mining based scheme is to avoid the complicated equation based methods that have been suggested by researchers to overcome the drawbacks of conventional fault location scheme. Although decision tree has inherent advantage over other methods like artificial neural network and support vector machines to work with large data sets, it has not been used in fault location estimation in series compensated (SC) transmission line so far. Decision tree is chosen to locate the faults because of its ability to work with large data set and high accuracy in associating the fault pattern to the fault distance using regression analysis. The discrete wavelet transform processed signals makes the decision process of decision tree regression easy by providing appropriate features. The proposed method is evaluated with variation of fault location, fault type, pre-fault load angle, location of series capacitor, degree of series compensation, fault inception angle, line parameters, inter-circuit faults and fault resistance. The test result of decision tree regression based location estimation scheme ensures that, it can estimate the fault distance accurately.

Statistical Distance Estimation Algorithms With RSS Measurements for Indoor LTE-A Networks

An indoor base station (BS), such as a remote radio head or home eNodeB, is a cost-effective solution to achieve ubiquitous access and positioning functions in indoor Long-Term Evolution Advanced (LTE-A) networks. In this paper, two distance estimation algorithms adopt received signal strength (RSS) to estimate the corresponding distance between a BS and a mobile station. The statistical inference distance estimation (SIDE) algorithm is proposed to provide a consistent distance estimator when the particle number is larger than an inferential theoretic lower bound given a confidence level and an error constraint. Moreover, the particle-based distance estimation (PDE) algorithm is proposed to estimate distance information with the technique of particle filtering under mixed line-of-sight (LOS) and non-line-of-sight (NLOS) conditions in indoor LTE-A networks. Furthermore, the theoretic Cramer–Rao lower bound (CRLB), considering the variations from fading effects and time-variant channels, is derived as a benchmark to evaluate the precision of distance estimators. The performance of the proposed SIDE algorithm is verified through simulations, and the results fulfill the requirements of different confidence levels and error constraints. Furthermore, the proposed PDE algorithm outperforms other distance estimation schemes and reveals robustness against mixed-sight and time-variant indoor LTE-A networks.

A Novel Decision Tree Regression-Based Fault Distance Estimation Scheme for Transmission Lines

In this paper, a decision tree regression (DTR)-based fault distance estimation scheme for double-circuit transmission lines is presented. Fault location is estimated using the information obtained from fault events data. The DTR was chosen because it requires less training time, offers greater accuracy with a large data set, and robustness than all other techniques like artificial neural networks, support vector machines, adaptive neurofuzzy inference systems, etc. Hitherto, DT has been used for fault detection/classification, but it has not been used for fault location. Three-phase current and voltage signals measured at one end of the line are used as inputs to a fault-location network. The proposed method does not require a communication link as it uses only one-end measurements. Signals are processed with two signal-processing techniques-discrete Fourier transforms and discrete wavelet transform. A comparative study of both techniques has been carried out to observe the effect of signal processing on the fault-location estimation method. The proposed method is tested on three test systems, namely: 1) the 2-bus; 2) the WSCC-9-bus; and 3) the IEEE 14-bus test systems. The test results confirm that the proposed DTR-based algorithm is not affected by the variation in fault type, fault location, fault inception angle, fault resistance, prefault load angle, SCC, load variation, and line parameters. The proposed scheme is relatively simple and easy in comparison with complex equation-based fault-location estimation methods.

전장환경 하에서 보행자의 다양한 이동유형을 고려한 관성항법 기반의 위치인식 기법

일반적으로 보행자의 위치를 파악하는데 사용하는 시스템을 개인 항법 장치 (PNS: Personal Navigation System)라고 한다. 위성 항법 시스템(GPS: Global Positioning System)은 PNS의 대표적인 사례이나, GPS 위성 신호 수신이 어려운 지역에서는 적용이 어려운 단점이 있다. GPS 신호 음영지역에서의 위치정보를 획득하기 위한 방법으로서 보행자 관성 항법(PDR: Pedestrian Dead Reckoning)은 별도의 인프라 없이 관성측정장치(IMU: Inertial Measurement Unit)만을 이용하여 보행자의 위치를 추정하는 방식으로서 인프라 구축이 어려운 특수 분야에 적용이 적합한 방식이다. 본 논문에서는 전장환경과 같은 GPS가 제한되는 특수한 환경 하에서 보행자의 다양한 이동유형을 고려한 관성항법 기반의 보행자용 위치인식 기법을 제안한다. 걷기, 뛰기, 포복과 같은 다양한 이동 형태에 따른 보행 거리 추정을 위해 IMU에서 제공되는 센서의 정보를 활용하여 걸음 검출과 보폭 추정으로 구성되는 보행거리 추정 기법과 HDR 알고리즘과 EKF(Extended Kalman Filter) 기반의 보행방향 추정 기법을 제안한다. 또한 건물입구와 같은 GPS 신호가 수신이 되나 신뢰성이 떨어지는 구간에서의 GPS와 PDR간 위치정보 융합 기법을 제안한다. 제안 기법의 성능 검증을 위해 자체 위치인식 모듈을 제작하여 국외제품과 비교 실험을 실시하였다. 실험결과, 제안 기법은 약 600m의 이동경로에서 평균 위치오차 거리는 5.64m, 이동거리 오차율 3.41%의 결과를 보였다. In general, Personal Navigation Systems (PNSs) can be defined systems to acquire pedestrian positional information. GPS is an example of PNS. However, GPS can only be used where the GPS signal can be received. Pedestrian Dead Reckoning (PDR) can estimate the positional information of pedestrians using Inertial Measurement Unit (IMU). Therefore, PDR can be used for GPS-disabled areas. This paper proposes a PDR scheme considering various movement types over GPS-disabled areas as combat environments. We propose a movement distance estimation scheme and movement direction estimation scheme as pedestrian's various movement types such as walking, running and crawling using IMU. Also, we propose a fusion algorithm between GPS and PDR to mitigate the lack of accuracy of positional information at the entrance to the building. The proposed algorithm has been tested in a real test bed. In the experimental results, the proposed algorithms exhibited an average position error distance of 5.64m and position error rate in goal point of 3.41% as a pedestrian traveled 0.6km.

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%.

Distance Estimation Schemes for Diffusion Based Molecular Communication Systems

Molecule concentration is often used as the information carrier to accomplish diffusion-based molecular communications (DMC) among nano-machines. To achieve the optimal functionality, knowing the distance between the transmitter nano-machine (TN) and the receiver nano-machine (RN) is of high importance. In this paper, two distance estimation schemes are proposed based upon the RN-sensed concentration which changes with regards to the time and distance. The RN estimates the distance by means of measuring either the concentration-peak time or received concentration energy. Simulations are performed to compare the accuracy of each scheme and to discover how the diffusion channel and noise may influence the accuracy. Results show that both schemes will provide a beneficial enhancement to molecular communication systems.

Single-View Distance-Estimation-Based Formation Control of Robotic Swarms

This paper presents a practical formation motion control scheme for robotic swarms based on single-view distance estimation, which is performed using a single nonsophisticated camera on each robot agent, and the prior information about the heights of the robots and other objects in the environment. Here, a nonsophisticated camera means one that has limited field of view and limited resolution. First, the vision mechanism is analyzed, and a single-view distance estimation scheme is designed. Then, a set of decentralized control laws, to be incorporated with distance estimation scheme, is introduced to move the robotic swarm in formation from an arbitrary initial position to an arbitrary final position without deforming the formation shape. The robots do not have any global positioning sensors, and they do not communicate with each other. The stability and performance of the overall system are analyzed mathematically. Later, practical issues are discussed regarding the proposed scheme, and the effects of delay and quantization in distance estimation are formally analyzed. Finally, we present experimental results demonstrating the performance of the proposed control scheme in real time.

Distance Estimation for Radar Systems Using DS-UWB Signals

In this paper, we propose a distance estimation scheme for radar systems using direct sequence ultra wideband (DS-UWB) signals. The proposed distance estimation scheme averages out the noise by accumulating the correlator outputs of the radar, and thus, helps the radar to employ a short-length DS-UWB signal reducing the correlation processing time. Numerical results confirm that the proposed distance estimation scheme provides a better estimation performance and a reduced correlation processing time compared with those of the conventional DS-UWB radars. Keywords—Radar, DS-UWB, distance estimation, correlation accumulation.

A Hybrid Distance Estimation for Wireless Body Sensor Networks

Wireless body area networks become a new research issue in recent years. In this paper, we propose hibernation and distance estimation schemes for wireless USB (WUSB) over wireless body area networks (WBAN) hierarchical protocol for body sensor networks (BSNs). The proposed middleware platform is composed of hibernation and optimal short distance estimation solution. Firstly, this paper provides WUSB private channel allocation scheme at the m-periodic allocation hibernation of a WBAN superframe. Secondly, three typical location estimation schemes of time difference of arrival (TDoA), frequency difference of arrival (FDoA), and angle of arrival (AoA) are analyzed through comparison of experiment results to propose optimal short distance estimation solution which is executed at each body sensor node on the basis of WUSB over WBAN protocol. And the WBAN host interprets status and motion of the body-sensor objects. The hybrid TDoA/FDoA/AoA algorithm based on UWB ranging shows best performances among the other hybrid schemes for wireless BSNs.

A Long Distance Estimation Scheme with a Large Aperture Lens Based on Infrared Stereo Vision System for Ship Navigation

A Long Distance Estimation Scheme with a Large Aperture Lens Based on Infrared Stereo Vision System for Ship Navigation

A Review of Tags Anti-collision and Localization Protocols in RFID Networks

Radio Frequency IDentification (RFID) has allowed the realization of ubiquitous tracking and monitoring of physical objects wirelessly with minimum human interactions. It plays a key role in a wide range of applications including asset tracking, contactless payment, access control, transportation and logistics, and other industrial applications. On the other side, RFID systems face several technical challenges that need to be overcome in order to achieve their potential benefits; tags collisions and localization of tagged objects are two important challenges. Numerous anti-collision and localization protocols have been proposed to address these challenges. This paper reviews the state-of-art tags' anti-collision and localization protocols, and provides a deep insight into technical issues of these protocols. The probabilistic and deterministic anti-collision protocols are critically studied and compared in terms of different parameters. We further review distance estimation, scene analysis, and proximity localization schemes and provide useful suggestions. We also introduce a new hybrid direction that utilizes power control to spatially partition the interrogation range of a reader for more efficient anti-collision and localization. Finally, we present the applications of RFID systems in healthcare sectors.

Distance Estimation With Weighted Least Squares for Mobile Beacon-Based Localization in Wireless Sensor Networks

In large-scale sensor networks, localization with mobile beacons is one of the most efficient ways to deploy sensor nodes as well as locate them. Direct communication with mobile beacons has an advantage of improvement in location accuracy by enabling sensor nodes to measure distances to the mobile beacons. Thus, it is important to improve the accuracy in the distance for high accurate positioning. In this letter, we propose a distance estimation scheme with weighted least squares in mobile beacon-based localization. First, we model distance measurements to a beacon node moving along the given linear tracks. Given our measurement model, the proposed scheme uses weighted least squares to minimize errors in distance measurements. Additionally we analyze the lower bound of errors in our distance estimation based on the Cramer-Rao bound. Simulation results show that our scheme can provide improved accuracy in both distance estimation and position estimation.

Geographic Channel Assignment Framework for Broadband Wireless Access Networks

This letter proposes a novel geographic channel assignment (GCA) framework for dynamic channel allocation (DCA) in broadband wireless access networks (BWANs). The proposed GCA scheme is based on the characteristics of radio propagation, which focuses on the relationship between transmission distances and communication parameters, e.g., signal-to-noise ratio (SNR) etc., and uses a signal-aware distance estimation scheme to determinate an appropriate channel for communication. This method significantly increases the capacity of the BWA system. Simulation results show that the GCA framework can yield approximate two times throughput of the IEEE 802.16 standard specifications as well as obtain significantly lower call blocking probability compared with classical channel assignment methods.

Estimating network proximity and latency

Network proximity and latency estimation is an important component in discovering and locating services and applications. With the growing number of services and service providers in the large-scale Internet, accurately estimating network proximity/latency with minimal probing overhead becomes essential for scalable deployment. Although there exist a number of network distance estimation schemes, they either rely on extensive infrastructure support, require the IP address of the potential targets, falsely cluster distant nodes, or perform poorly with even few measurement errors. We propose Netvigator , a scalable network proximity and latency estimation tool that uses information obtained from probing a small number of landmark nodes and intermediate routers (termed milestones) that are discovered en route to the landmarks, to identify the closest nodes. With very little additional probing overhead, Netvigator uses distance information to the milestones to accurately locate the closest nodes. We developed a Netvigator prototype and report our performance evaluation on PlanetLab and in the intranet of a large enterprise. Netvigator is a running service on PlanetLab as a part of HP Labs' S 3 (Scalable Sensing Service).

On the Kinematic Origin of the Luminosity–Pulse Lag Relationship in Gamma-Ray Bursts

This Letter presents an interpretation based on gamma-ray burst source kinematics for the relationship found by Norris et al. between peak luminosity and energy-dependent pulse lag. I argue that the correlation should instead be between number luminosity and pulse lag. This interpretation improves the least-squares fit of this correlation for the known bursts by 25% or more. It also suggests a distance estimation scheme. I propose that this relationship is due to the variation in line-of-sight velocity among bursts. This interpretation allows one to speculate on the range of gamma-ray burst expansion velocities or the size of their jet opening angles.