Location Estimation Process Research Articles

Generalized fault diagnosis method of transmission lines using transfer learning technique

Recent artificial intelligence-based methods have shown great promise in the use of neural networks for real-time detection of transmission line faults and estimation of their locations. The expansion of power systems including transmission lines with various lengths have made the fault detection, classification, and location estimation process more challenging. Transmission line datasets are stream data which are continuously collected by various sensors and hence, require generalized and fast fault diagnosis approaches. Newly collected datasets including voltages and currents for faulty and non-faulty situations might not have adequate and accurate labels that are useful to train neural networks. In this paper, a novel transfer learning framework based on a pre-trained LeNet-5 convolutional neural network is proposed. This method is able to diagnose faults for different transmission line lengths and impedances by transferring the knowledge from a source convolutional neural network to predict a dissimilar target dataset. By transferring this knowledge, faults from various transmission lines, even without sufficient data samples with labels, can be diagnosed faster and more efficiently than the existing methods. To prove the feasibility and effectiveness of this methodology, seven different datasets that include various lengths of transmission lines are used. The robustness of the proposed methodology against the generator voltage fluctuations, variations in fault locations, fault inception angle, fault resistance, and phase difference between the two generators are well studied to prove the reliability of this technique for fault diagnosis of transmission lines.

A Robust and Accurate Indoor Localization Using Learning-Based Fusion of Wi-Fi RTT and RSSI.

Great attention has been paid to indoor localization due to its wide range of associated applications and services. Fingerprinting and time-based localization techniques are among the most popular approaches in the field due to their promising performance. However, fingerprinting techniques usually suffer from signal fluctuations and interference, which yields unstable localization performance. On the other hand, the accuracy of time-based techniques is highly affected by multipath propagation errors and non-line-of-sight transmissions. To combat these challenges, this paper presents a hybrid deep-learning-based indoor localization system called RRLoc which fuses fingerprinting and time-based techniques with a view of combining their advantages. RRLoc leverages a novel approach for fusing received signal strength indication (RSSI) and round-trip time (RTT) measurements and extracting high-level features using deep canonical correlation analysis. The extracted features are then used in training a localization model for facilitating the location estimation process. Different modules are incorporated to improve the deep model’s generalization against overtraining and noise. The experimental results obtained at two different indoor environments show that RRLoc improves localization accuracy by at least 267% and 496% compared to the state-of-the-art fingerprinting and ranging-based-multilateration techniques, respectively.

Towards a better indoor positioning system: A location estimation process using artificial neural networks based on a semi-interpolated database

The Wi-Fi-fingerprinting positioning method is used widely in indoor positioning environments due to its simplicity and wide coverage. However, in the offline phase of the method, the collection process is a fundamental and critical step that requires time and effort. Moreover, the location estimation process, which is executed in the second Wi-Fi-fingerprinting phase (online phase), needs to be accurate enough to guarantee efficient indoor positioning. Hence, in this work, a novel indoor location-estimation process based on a semi-interpolated radio map and artificial neural network (ANN) is presented. A mobile application is built to gather the received signal strength indicator (RSSI) fingerprinting to construct a radio map, which is then expanded with the biharmonic spline interpolation (BSI) method through the estimation of more RSSI values. A feedforward back propagation (FFBP) neural network and generalised regression neural network (GRNN) were built in the online phase for the location-estimation process. They were trained using the expanded dataset by taking the reference point (X, Y) coordinates as their desired output and using two different forms of the data as their inputs. The first inputs are the RSSI values from the 17 access points (APs) – three of the APs have dual-band i.e, support both 2.4 and 5 GHz – and the second input is based on a selected set of APs, which produce a high level of acceptable RSSI and their coordinates. A comparison between these two models was done. The results show that FFBP outperforms GRNNs in terms of structure simplicity, while GRNNs achieved more accurate prediction results with an average distance error of up to 0.48 m. Hence, our proposed methodology leverages building a simple neural network topology that has good location estimation results for indoor positioning in a low-cost localisation process.

Delay-Tolerant Sensor Network (DTN) Implementation in Cloud Computing

In this study’s scheme, the objective is to employ the robot to implement the location estimation process for sensor nodes with which it interacts relative to the radio messages’ signal strengths, having received the signals from the sensor nodes. The implication is that the central purpose of the study lies in the elimination of the static sensor nodes’ processing constraints. Imperative to highlight is that the study’s mathematical contribution lies in the utilization of the REKF-based state estimator to analyze the localization problem. Deviating from Kalman Filter, it is worth noting that REKF exhibits computational efficiency and robustness. Indeed, the localization scheme is implemented on a hybrid network test bed.

Analysis of condition number and position estimation error for multiangulation position estimation system

A passive wireless positioning system could be used to detect the location of low-level airborne targets such as drones or unmanned aircraft systems from the electromagnetic emission detected at spatially deployed ground receiving stations GRSs . The multiangulation system proposed in this paper makes use of the angle of arrival AOA of the transmitted signal from the target to estimate its position through a 2-stage process. The AOA is the positiondependent signal parameter PDSP obtained from the target emission in the first stage, and using the PDSP and GRSs, the target location is estimated in the second stage by the angulation algorithm. Noise in the received signal results in AOA estimation error and subsequently error in the position estimation PE . This paper focuses on the angulation process, which is the second stage of the multiangulation target location estimation process. Analysis is conducted to determine the correlation between the PE error and the condition number of the coefficient matrix of a multiangulation position estimation system. The results based on Monte Carlo simulations show that both condition number and PE error distribution increase with the target range, where the higher condition number values correlate with higher PE error values appearing around 80◦ to 110◦ and 260◦ to 280◦ of both distributions.

Joint Scan Rate and Emitter Location Estimation Algorithm in SBR System Based on AOA and DTOST Measurements

A joint scan rate and emitter location estimation algorithm in scan-based radar (SBR) system based on the angle-of-arrival (AOA) and the difference time of scan time (DTOST) measurements is proposed in this paper. SBR localization is a kind of passive geolocation technique for stationary pulsed radars. To date, a huge number of passive localization techniques using AOA, DTOST or other measurements for SBR have been developed assuming prior knowledge of the scan rate. However, it is usually hard to obtain the accurate scan velocity in most of practical civilian and military applications. In this paper, by using the linear correlation between AOA and DTOST, the maximum likelihood (ML) estimation algorithm based on linear constraints is given to obtain the scan rate of SBR. And the new AOA information after fusion is acquired on this basis. Then the weighted least square (WLS) method is used for SBR localization and the Cramer-Rao lower bound (CRLB) is derived for SBR location estimation. A detailed analysis of scan rate and emitter location estimation process is given and the effectiveness of the proposed localization algorithm is illustrated with computer simulations.

DALT: Double Anchor-Based Localization Technique for Wireless Sensor Networks using RSS and AoA Measures

Tracking the location of target nodes/objects plays a vital role in disaster management and emergency rescue operations. The wireless sensor network is an easiest and cheapest solution to track the target nodes/objects in emergency applications. Use of GPS installed devices in wireless sensor networks is one of the solutions to track the target node’s location. Installing GPS device on every target node is very expensive and the GPS device drains the battery power, and increases the size of sensor nodes. Localization is an alternative solution to track the target node’s location. Many localization algorithms are available to track/estimate the target node’s location coordinates, but the accuracy of the estimated target nodes is poor. A new localization technique is proposed in this work to improve the accuracy of the estimated location of the target nodes. The proposed technique uses two anchor nodes, and parameters like linear vector segments, received signal strength, and angle of arrival measures in the location estimation process. This work has been simulated in MATLAB. The proposed algorithm outperforms the existing localization techniques.

Detection of the First Component of the Received LTE Signal in the OTDoA Method

In a modern world there is a growing demand for localization services of various kinds. Position estimation can be realized via cellular networks, especially in the currently widely deployed LTE (Long Term Evolution) networks. However, it is not an easy task in harsh propagation conditions which often occur in dense urban environments. Recently, time-methods of terminal localization within the network have been the focus of attention, with the OTDoA (Observed Time Difference of Arrival) method in particular. One of the main factors influencing the accuracy of location estimation in the OTDoA method is the nature of the propagation channel that affects the ease of isolating the signal component travelling from the transmitter to the receiver through the shortest path. To obtain the smallest possible localization error, it is necessary to detect the first received component of the useful signal. This aim could be achieved by using a proper algorithm within the receiver. This paper proposes a new algorithm for effective detecting of the first component of the LTE downlink signal in the multipath environment. In a mobile terminal location estimation process, CSRS (Cell Specific Reference Signal) signals were used instead of dedicated PRS (Positioning Reference Signal) signals. New solution was verified during the measurement campaign in a real LTE network.

A high accurate localization algorithm with DV-Hop and differential evolution for wireless sensor network

Localization technology has been a core component for Internet of Things (IoT), especially for Wireless Sensor Network (WSN). Among all localization technologies, Distance Vector-Hop (DV-Hop) algorithm is a very frequently used algorithm for WSN. DV-Hop estimates the distance through the hop-count between nodes in which the value of hop-count is discrete, and thus there is a serious consequence that some nodes have the same estimated distance when their hop-count with respect to identical node is equal. In this paper, we ameliorate the value of hop-count by the number of common one-hop nodes between adjacent nodes. The discrete values of hop-count will be converted to more accurate continuous values by our proposed method. Therefore, the error caused by the estimated distance can be effectively reduced. Furthermore, we formulate the location estimation process to be a minimizing optimization problem based on the weighted squared errors of estimated distance. We apply Differential Evolution (DE) algorithm to acquire the global optimum solution which corresponds to the estimated location of unknown nodes. The proposed localization algorithm based on improved DV-Hop and DE is called DECHDV-Hop. We conduct substantial experiments to evaluate the effectiveness of DECHDV-Hop including the comparison with DV-Hop, GADV-Hop and PSODV-Hop in four different network simulation situations. Experimental results demonstrate that DECHDV-Hop can achieve much higher localization accuracy than other algorithms in these network situations.

An Efficient Location Verification Scheme for Static Wireless Sensor Networks.

In wireless sensor networks (WSNs), the accuracy of location information is vital to support many interesting applications. Unfortunately, sensors have difficulty in estimating their location when malicious sensors attack the location estimation process. Even though secure localization schemes have been proposed to protect location estimation process from attacks, they are not enough to eliminate the wrong location estimations in some situations. The location verification can be the solution to the situations or be the second-line defense. The problem of most of the location verifications is the explicit involvement of many sensors in the verification process and requirements, such as special hardware, a dedicated verifier and the trusted third party, which causes more communication and computation overhead. In this paper, we propose an efficient location verification scheme for static WSN called mutually-shared region-based location verification (MSRLV), which reduces those overheads by utilizing the implicit involvement of sensors and eliminating several requirements. In order to achieve this, we use the mutually-shared region between location claimant and verifier for the location verification. The analysis shows that MSRLV reduces communication overhead by 77% and computation overhead by 92% on average, when compared with the other location verification schemes, in a single sensor verification. In addition, simulation results for the verification of the whole network show that MSRLV can detect the malicious sensors by over 90% when sensors in the network have five or more neighbors.

Delay Kalman Filter to Estimate the Attitude of a Mobile Object with Indoor Magnetic Field Gradients.

More and more services are based on knowing the location of pedestrians equipped with connected objects (smartphones, smartwatches, etc.). One part of the location estimation process is attitude estimation. Many algorithms have been proposed but they principally target open space areas where the local magnetic field equals the Earth’s field. Unfortunately, this approach is impossible indoors, where the use of magnetometer arrays or magnetic field gradients has been proposed. However, current approaches omit the impact of past state estimates on the current orientation estimate, especially when a reference field is computed over a sliding window. A novel Delay Kalman filter is proposed in this paper to integrate this time correlation: the Delay MAGYQ. Experimental assessment, conducted in a motion lab with a handheld inertial and magnetic mobile unit, shows that the novel filter better estimates the Euler angles of the handheld device with an 11.7° mean error on the yaw angle as compared to 16.4° with a common Additive Extended Kalman filter.

Enhanced Voting based Secure Localization for Wireless Sensor Networks

Development of location estimation algorithms with improvement in location precision with lower cost, less energy consumption and less hardware support has become more important for many applications in Wireless Sensor Networks (WSNs). This paper addresses the problem of secure location determination, known as secure localization in WSNs using voting based technique which gives a search region in presence of anchor nodes. From the obtained search region trilateration is applied to know the position of sensor nodes. To avoid the involvement of sensor nodes in further location estimation process, bilateration is applied. Experimental analysis shows that the maximum number of nodes can be localized and accurate location of a node can be determined efficiently with low estimation error. To avoid the attacks and involvement of malicious nodes in the localization process, we implement an improved authentication and security algorithm. Using few location reference points in the localization process reduces the communication cost. The proposed scheme also provides very good localization accuracy.

MANCL: a multi‐anchor nodes collaborative localization algorithm for underwater acoustic sensor networks

AbstractLocalization is an essential and major issue for underwater acoustic sensor networks (UASNs). Almost all the applications in UASNs are closely related to the locations of sensors. In this paper, we propose a multi‐anchor nodes collaborative localization (MANCL) algorithm, a three‐dimensional (3D) localization scheme using anchor nodes and upgrade anchor nodes within two hops for UASNs. The MANCL algorithm divides the whole localization process into four sub‐processes: unknown node localization process, iterative location estimation process, improved 3D Euclidean distance estimation process, and 3D DV‐hop distance estimation process based on two‐hop anchor nodes. In the third sub‐process, we propose a communication mechanism and a vote mechanism to determine the temporary coordinates of unknown nodes. In the fourth sub‐process, we use two‐hop anchor nodes to help localize unknown nodes. We also evaluate and compare the proposed algorithm with a large‐scale localization algorithm through simulations. Results show that the proposed MANCL algorithm can perform better with regard to localization ratio, average localization error, and energy consumption in UASNs. Copyright © 2014 John Wiley & Sons, Ltd.

Magnetic perturbations detection and heading estimation using magnetometers

With the advent of smart phones equipped with Micro-Electro-Mechanical Systems, it has become possible to utilise one's orientation information along with location for Location-Based Services applications. Although gyroscopes are considered to be the primary sensors for orientation estimation, associated errors require periodic updates from other sources to mitigate them. Accelerometers can be used for estimating roll and pitch angles and magnetometers for estimating heading. However, the presence of man-made magnetic anomalies caused by electronic devices, ferrous materials, mechanical and electrical infrastructures contaminates the measurements since only the Earth's magnetic field is useful for estimating magnetically derived heading. Without proper magnetic disturbances detection and mitigation technique, heading estimates become erroneous and can corrupt the entire location estimation process. Because heading is the primary source of error in pedestrian navigation and the magnetic field is available everywhere, an exhaustive analysis of magnetic anomalies and their impact on heading estimation has been conducted using data collected in six different pedestrian environments. Based on the statistical outcomes, a magnitude- and angle-based detector of magnetic disturbances has been developed. This information is also used for estimating the accuracy of the magnetically derived heading, which can further assist sensor fusion algorithms. An experimental assessment of the proposed detector was conducted in a shopping mall. A theoretical analysis and experimental results show that the proposed detector is capable of identifying the effects of perturbations on the Earth's magnetic field, which provides users with a better estimate of magnetic heading in different pedestrian navigation environments.