scholarly journals A Novel Enhanced Positioning Trilateration Algorithm Implemented for Medical Implant In-Body Localization

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Peter Brida ◽  
Juraj Machaj
Keyword(s):  
Signal Strength ◽  
Received Signal Strength ◽  
Position Estimation ◽  
Time Of Arrival ◽  
Medical Implant ◽  
Exact Position ◽  
Two Phases ◽  
Positioning Algorithm ◽  
Selection Of

Medical implants based on wireless communication will play crucial role in healthcare systems. Some applications need to know the exact position of each implant. RF positioning seems to be an effective approach for implant localization. The two most common positioning data typically used for RF positioning are received signal strength and time of flight of a radio signal between transmitter and receivers (medical implant and network of reference devices with known position). This leads to positioning methods: received signal strength (RSS) and time of arrival (ToA). Both methods are based on trilateration. Used positioning data are very important, but the positioning algorithm which estimates the implant position is important as well. In this paper, the proposal of novel algorithm for trilateration is presented. The proposed algorithm improves the quality of basic trilateration algorithms with the same quality of measured positioning data. It is called Enhanced Positioning Trilateration Algorithm (EPTA). The proposed algorithm can be divided into two phases. The first phase is focused on the selection of the most suitable sensors for position estimation. The goal of the second one lies in the positioning accuracy improving by adaptive algorithm. Finally, we provide performance analysis of the proposed algorithm by computer simulations.

scholarly journals An Efficient Algorithm for Partial Discharge Localization in High-Voltage Systems Using Received Signal Strength

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4000 ◽  
Author(s):  
Umar F. Khan ◽  
Pavlos I. Lazaridis ◽  
Hamd Mohamed ◽  
Ricardo Albarracín ◽  
Zaharias D. Zaharis ◽  
...  
Keyword(s):  
High Voltage ◽  
Time Synchronization ◽  
Clock Synchronization ◽  
Human Life ◽  
Partial Discharge ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Time Of Arrival ◽  
Localization Algorithm ◽  
Require Time

The term partial discharge (PD) refers to a partial bridging of insulating material between electrodes that sustain an electric field in high-voltage (HV) systems. Long-term PD activity can lead to catastrophic failures of HV systems resulting in economic, energy and even human life losses. Such failures and losses can be avoided by continuously monitoring PD activity. Existing techniques used for PD localization including time of arrival (TOA) and time difference of arrival (TDOA), are complicated and expensive because they require time synchronization. In this paper, a novel received signal strength (RSS) based localization algorithm is proposed. The reason that RSS is favoured in this research is that it does not require clock synchronization and it only requires the energy of the received signal rather than the PD pulse itself. A comparison was made between RSS based algorithms including a proposed algorithm, the ratio and search and the least squares algorithm to locate a PD source for nine different positions. The performance of the algorithms was evaluated by using two field scenarios based on seven and eight receiving nodes, respectively. The mean localization error calculated for two-field-trial scenarios show, respectively, 1.80 m and 1.76 m for the proposed algorithm for all nine positions, which is the lowest of the three algorithms.

Improved Algorithm on Wide Range Positioning for Partial Discharge in Substation

2013 ◽  
Vol 860-863 ◽  
pp. 2177-2181
Author(s):  
Xi Ran Wang ◽  
Huai Dong Liu ◽  
Yi Fan He ◽  
Qi Ming Zhao ◽  
He Wu
Keyword(s):  
Electrical Discharge ◽  
Partial Discharge ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Experimental Result ◽  
Wide Range ◽  
Positioning Algorithm ◽  
Received Signal Strength Indication ◽  
Improved Algorithm

This paper proposes a Improved positioning algorithm of electrical partial discharge applied for substations. This algorithm is based on received signal strength indication, and taken practical condition of sensors into consideration by replenishing beacon nodes. Compared with traditional trilateral weighting positioning algorithm, this paper introduces indefinite amount of localization perpendicular lines and combined them with trilateral districts to calculate the weighting result, which can reduce error. This model meets the requirement of reality that the height of electrical discharge spots differentiate from the height of the plane formed by beacon nodes (signal sensors). The experimental result indicates that the revised position model proposed by this paper can effectively fit the condition of monitoring hardware. Error of this algorithm is less than that of traditional trilateral localization.

scholarly journals Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

2019 ◽  
Vol 9 (18) ◽  
pp. 3930 ◽  
Author(s):  
Jaehyun Yoo ◽  
Jongho Park
Keyword(s):  
Feature Extraction ◽  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Position Estimation ◽  
Training Data ◽  
Data Sets ◽  
Feature Extraction Method ◽  
Fingerprinting Method ◽  
Trajectory Learning

This paper studies the indoor localization based on Wi-Fi received signal strength indicator (RSSI). In addition to position estimation, this study examines the expansion of applications using Wi-Fi RSSI data sets in three areas: (i) feature extraction, (ii) mobile fingerprinting, and (iii) mapless localization. First, the features of Wi-Fi RSSI observations are extracted with respect to different floor levels and designated landmarks. Second, the mobile fingerprinting method is proposed to allow a trainer to collect training data efficiently, which is faster and more efficient than the conventional static fingerprinting method. Third, in the case of the unknown-map situation, the trajectory learning method is suggested to learn map information using crowdsourced data. All of these parts are interconnected from the feature extraction and mobile fingerprinting to the map learning and the estimation. Based on the experimental results, we observed (i) clearly classified data points by the feature extraction method as regards the floors and landmarks, (ii) efficient mobile fingerprinting compared to conventional static fingerprinting, and (iii) improvement of the positioning accuracy owing to the trajectory learning.

scholarly journals Considerations about the Signal Level Measurement in Wireless Sensor Networks for Node Position Estimation

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4179 ◽  
Author(s):  
Stelian Dolha ◽  
Paul Negirla ◽  
Florin Alexa ◽  
Ioan Silea
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Signal Strength ◽  
Data Access ◽  
Received Signal Strength ◽  
Position Estimation ◽  
Wireless Sensor ◽  
Node Localization ◽  
Received Signal Strength Indicator ◽  
Starting Point

Wireless Sensor Networks (WSN) are widely used in different monitoring systems. Given the distributed nature of WSN, a constantly increasing number of research studies are concentrated on some important aspects: maximizing network autonomy, node localization, and data access security. The node localization and distance estimation algorithms have, as their starting points, different information provided by the nodes. The level of signal strength is often such a starting point. A system for Received Signal Strength Indicator (RSSI) acquisition has been designed, implemented, and tested. In this paper, experiments in different operating environments have been conducted to show the variation of Received Signal Strength Indicator (RSSI) metric related to distance and geometrical orientation of the nodes and environment, both indoor and outdoor. Energy aware data transmission algorithms adjust the power consumed by the nodes according to the relative distance between the nodes. Experiments have been conducted to measure the current consumed by the node depending on the adjusted transmission power. In order to use the RSSI values as input for distance or location detection algorithms, the RSSI values can’t be used without intermediate processing steps to mitigate with the non-linearity of the measured values. The results of the measurements confirmed that the RSSI level varies with distance, geometrical orientation of the sensors, and environment characteristics.

scholarly journals Analysis of Received Signal Strength Quantization in Fingerprinting Localization

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3203 ◽  
Author(s):  
Syed Khandker ◽  
Joaquín Torres-Sospedra ◽  
Tapani Ristaniemi
Keyword(s):  
Quality Of Service ◽  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Storage Space ◽  
Data Traffic ◽  
Positioning Accuracy ◽  
Hardware Configuration ◽  
Fine Resolution

In recent times, Received Signal Strength (RSS)-based Wi-Fi fingerprinting localization has become one of the most promising techniques for indoor localization. The primary aim of RSS is to check the quality of the signal to determine the coverage and the quality of service. Therefore, fine-resolution RSS is needed, which is generally expressed by 1-dBm granularity. However, we found that, for fingerprinting localization, fine-granular RSS is unnecessary. A coarse-granular RSS can yield the same positioning accuracy. In this paper, we propose quantization for only the effective portion of the signal strength for fingerprinting localization. We found that, if a quantized RSS fingerprint can carry the major characteristics of a radio environment, it is sufficient for localization. Five publicly open fingerprinting databases with four different quantization strategies were used to evaluate the study. The proposed method can help to simplify the hardware configuration, enhance security, and save approximately 40–60% storage space and data traffic.

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