scholarly journals The influence of system settings on positioning accuracy in acoustic telemetry, using the YAPS algorithm.

2019 ◽  
Author(s):  
Jenna Vergeynst ◽  
Henrik Baktoft ◽  
Ans Mouton ◽  
Tom De Mulder ◽  
Ingmar Nopens ◽  
...  
Keyword(s):  
Animal Movement ◽  
Random Sequence ◽  
Interval Data ◽  
Information Loss ◽  
Positioning Accuracy ◽  
System Parameters ◽  
On Line ◽  
Positioning Algorithm ◽  
Better Than ◽  
Large Burst

Abstract Background: Acoustic positioning telemetry allows to collect large amounts of data on the movement of aquatic animals by use of autonomous receiver stations. Essential in this process is the conversion from raw signal detections to reliable positions. A new advancement in the domain is YAPS (Yet Another Positioning Solver), which combines the detection data on the hydrophones with a model of animal movement. This transparent, flexible and on-line available positioning algorithm overcomes problems related to traditional point-by-point positioning and filtering techniques. However, its performance has only been tested on data from one telemetry system, providing transmitters with stable burst interval. To investigate the performance of YAPS on different system parameters and settings, we conducted a simulation study.Results: This paper discusses the effect of varying burst types, burst intervals, number of observations, reflectivity levels of the environment, levels of out-of-array positioning and temporal hydrophone resolution on positioning accuracy. We found that a hydrophone resolution better than 1~ms is required for accurate fine-scale positioning. The positioning accuracy of YAPS increases with decreasing burst intervals, especially when the number of observations is low, when reflectivity is high or when information out-of-array is used. However, when the burst interval is stable, large burst intervals (in the order of 1 to 2 minutes) can be chosen without strongly hampering the accuracy (although this results in information loss). With random burst intervals, the accuracy can be much improved if the random sequence is known.Conclusions: As it turns out, the key to accurate positioning is the burst type. If a stable burst interval is not possible, the availability of the random sequence improves the positioning of random burst interval data significantly.

scholarly journals The influence of system settings on positioning accuracy in acoustic telemetry, using the YAPS algorithm.

2020 ◽  
Author(s):  
Jenna Vergeynst ◽  
Henrik Baktoft ◽  
Ans Mouton ◽  
Tom De Mulder ◽  
Ingmar Nopens ◽  
...  
Keyword(s):  
Animal Movement ◽  
Random Sequence ◽  
Interval Data ◽  
Positioning Accuracy ◽  
System Parameters ◽  
Study Results ◽  
On Line ◽  
Positioning Algorithm ◽  
Better Than ◽  
Large Burst

Abstract Background: Acoustic positioning telemetry allows to collect large amounts of data on the movement of aquatic animals by use of autonomous receiver stations. Essential in this process is the conversion from raw signal detections to reliable positions. A new advancement in the domain is YAPS (Yet Another Positioning Solver), which combines the detection data on the receivers with a model of animal movement. This transparent, flexible and on-line available positioning algorithm overcomes problems related to traditional point-by-point positioning and filtering techniques. However, its performance has only been tested on data from one telemetry system, providing transmitters with stable burst interval. To investigate the performance of YAPS on different system parameters and settings, we conducted a simulation study. Results: This paper discusses the effect of varying burst types, burst intervals, number of observations, reflectivity levels of the environment, levels of out-of-array positioning and temporal receiver resolution on positioning accuracy. We found that a receiver resolution better than 1 ms is required for accurate fine-scale positioning. The positioning accuracy of YAPS increases with decreasing burst intervals, especially when the number of observations is low, when reflectivity is high or when information out-of-array is used. However, when the burst interval is stable, large burst intervals (in the order of 1 to 2 minutes) can be chosen without strongly hampering the accuracy (although this results in information loss). With random burst intervals, the accuracy can be much improved if the random sequence is known. Conclusions: As it turns out, the key to accurate positioning is the burst type. If a stable burst interval is not possible, the availability of the random sequence improves the positioning of random burst interval data significantly.

scholarly journals The influence of system settings on positioning accuracy in acoustic telemetry, using the YAPS algorithm.

2020 ◽  
Author(s):  
Jenna Vergeynst ◽  
Henrik Baktoft ◽  
Ans Mouton ◽  
Tom De Mulder ◽  
Ingmar Nopens ◽  
...  
Keyword(s):  
Animal Movement ◽  
Random Sequence ◽  
Interval Data ◽  
Positioning Accuracy ◽  
System Parameters ◽  
Study Results ◽  
On Line ◽  
Positioning Algorithm ◽  
Better Than ◽  
Large Burst

Abstract Background Acoustic positioning telemetry allows to collect large amounts of data on the movement of aquatic animals by use of autonomous receiver stations. Essential in this process is the conversion from raw signal detections to reliable positions. A new advancement in the domain is YAPS (Yet Another Positioning Solver), which combines the detection data on the receivers with a model of animal movement. This transparent, flexible and on-line available positioning algorithm overcomes problems related to traditional point-by-point positioning and filtering techniques. However, its performance has only been tested on data from one telemetry system, providing transmitters with stable burst interval. To investigate the performance of YAPS on different system parameters and settings, we conducted a simulation study. Results This paper discusses the effect of varying burst types, burst intervals, number of observations, reflectivity levels of the environment, levels of out-of-array positioning and temporal receiver resolution on positioning accuracy. We found that a receiver resolution better than 1~ms is required for accurate fine-scale positioning. The positioning accuracy of YAPS increases with decreasing burst intervals, especially when the number of observations is low, when reflectivity is high or when information out-of-array is used. However, when the burst interval is stable, large burst intervals (in the order of 1 to 2 minutes) can be chosen without strongly hampering the accuracy (although this results in information loss). With random burst intervals, the accuracy can be much improved if the random sequence is known. Conclusions As it turns out, the key to accurate positioning is the burst type. If a stable burst interval is not possible, the availability of the random sequence improves the positioning of random burst interval data significantly.

scholarly journals A Node Location Algorithm Based on Improved Whale Optimization in Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Pingzhang Gou ◽  
Bo He ◽  
Zhaoyang Yu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Wireless Sensor ◽  
Positioning Accuracy ◽  
Whale Optimization ◽  
Location Algorithm ◽  
Positioning Algorithm ◽  
Better Than

With the popularity of swarm intelligence algorithms, the positioning of nodes to be located in wireless sensor networks (WSNs) has received more and more attention. To overcome the disadvantage of large ranging error and low positioning accuracy caused by the positioning algorithm of the received signal strength indication (RSSI) ranging model, we use the RSSI modified by Gaussian to reduce the distance measurement error and introduce an improved whale optimization algorithm to optimize the location of the nodes to be positioned to improve the positioning accuracy. The experimental results show that the improved whale algorithm performs better than the whale optimization algorithm and other swarm intelligence algorithms under 20 different types of benchmark function tests. The positioning accuracy of the proposed location algorithm is better than that of the original RSSI algorithm, the hybrid exponential and polynomial particle swarm optimization (HPSO) positioning algorithms, the whale optimization, and the quasiaffine transformation evolutionary (WOA-QT) positioning algorithm. It can be concluded that the cluster intelligence algorithm has better advantages than the original RSSI in WSN node positioning, and the improved algorithm in this paper has more advantages than several other cluster intelligence algorithms, which can effectively solve the positioning requirements in practical applications.

scholarly journals Machine Learning for the Dynamic Positioning of UAVs for Extended Connectivity

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4618
Author(s):  
Francisco Oliveira ◽  
Miguel Luís ◽  
Susana Sargento
Keyword(s):  
Machine Learning ◽  
Cellular Networks ◽  
Real Data ◽  
Emerging Technology ◽  
Machine Learning Algorithms ◽  
Base Stations ◽  
Aerial Vehicle ◽  
Positioning Algorithm ◽  
The Military ◽  
Better Than

Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed.

Analysis on Signal-in-Space Range Error and Positioning Accuracy of BDS-3

2021 ◽  
Author(s):  
Weiping Liu ◽  
Bo Jiao ◽  
Jinming Hao ◽  
Zhiwei Lv ◽  
Jiantao Xie ◽  
...  
Keyword(s):  
Navigation System ◽  
Convergence Time ◽  
Asia Pacific ◽  
Single Frequency ◽  
Earth Orbit ◽  
Positioning Accuracy ◽  
Point Positioning ◽  
Range Error ◽  
Space Range ◽  
Better Than

Abstract Being the first mixed-constellation global navigation system, the global BeiDou navigation system (BDS-3) designs new signals, the service performance of which has attracted extensive attention. In the present study, the Signal-in-space range error (SISRE) computation method for different types of navigation satellites was presented. And the differential code bias (DCB) correction method for BDS-3 new signals was deduced. Based on these, analysis and evaluation were done by adopting the actual measured data after the official launching of BDS-3. The results showed that BDS-3 performed better than the regional navigation satellite system (BDS-2) in terms of SISRE. Specifically, the SISRE of the BDS-3 medium earth orbit (MEO) satellites reached 0.52 m, slightly inferior compared to 0.4 m from Galileo, marginally better than 0.57 m from GPS, and significantly better than 2.33 m from GLONASS. And the BDS-3 inclined geostationary orbit (IGSO) satellites achieved the SISRE of 0.90 m, on par with that of the QZSS IGSO satellites. However, the average SISRE of BDS-3 geostationary earth orbit (GEO) satellites was 1.15 m, which was marginally inferior to that of the QZSS GEO satellite (0.91m). In terms of positioning accuracy, the overall three-dimensional single-frequency standard point positioning (SPP) accuracy of BDS-3 B1C, B2a, B1I, and B3I gained an accuracy level better than 5 m. Moreover, the B1I signal exhibited the best positioning accuracy in the Asian-Pacific region, while the B1C signal set forth the best positioning accuracy in the other regions. Owing to the advantage in signal frequency, the dual-frequency SPP accuracy of B1C+B2a surpassed that of the transitional signal of B1I+B3I. Since there are more visible satellites in Asia-Pacific, the positioning accuracy of BDS-3 was moderately superior to that of GPS. The precise point positioning (PPP) accuracy of BDS-3 B1C+B2a or B1I+B3I converged to the order of centimeters, marginally inferior to that of the GPS L1+L2. However, these three combinations had a similar convergence time of approximately 30 minutes.

scholarly journals Factor Graph-Assisted Distributed Cooperative Positioning Algorithm in the GNSS System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3748 ◽  
Author(s):  
Chengkai Tang ◽  
Lingling Zhang ◽  
Yi Zhang ◽  
Houbing Song
Keyword(s):  
Smart Cities ◽  
Factor Graph ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Cooperative Positioning ◽  
Measured Range ◽  
Ranging Error ◽  
Positioning Algorithm ◽  
Range Error ◽  
Cooperative Nodes

The development of smart cities calls for improved accuracy in navigation and positioning services; due to the effects of satellite orbit error, ionospheric error, poor quality of navigation signals and so on, it is difficult for existing navigation technology to achieve further improvements in positioning accuracy. Distributed cooperative positioning technology can further improve the accuracy of navigation and positioning with existing GNSS (Global Navigation Satellite System) systems. However, the measured range error and the positioning error of the cooperative nodes exhibit larger reductions in positioning accuracy. In response to this question, this paper proposed a factor graph-aided distributed cooperative positioning algorithm. It establishes the confidence function of factor graphs theory with the ranging error and the positioning error of the coordinated nodes and then fuses the positioning information of the coordinated nodes by the confidence function. It can avoid the influence of positioning error and ranging error and improve the positioning accuracy of cooperative nodes. In the simulation part, the proposed algorithm is compared with a mainly coordinated positioning algorithm from four aspects: the measured range error, positioning error, convergence speed, and mutation error. The simulation results show that the proposed algorithm leads to a 30–60% improvement in positioning accuracy compared with other algorithms under the same measured range error and positioning error. The convergence rate and mutation error elimination times are only 1 / 5 to 1 / 3 of the other algorithms.

A Localization Algorithm Research to Eliminate the Indoor NLOS Error

2014 ◽  
Vol 989-994 ◽  
pp. 2232-2236 ◽  
Author(s):  
Jia Zhi Dong ◽  
Yu Wen Wang ◽  
Feng Wei ◽  
Jiang Yu
Keyword(s):  
Indoor Environment ◽  
Arrival Time ◽  
Channel Model ◽  
Indoor Positioning ◽  
Superior Performance ◽  
Line Of Sight ◽  
Localization Algorithm ◽  
Positioning Accuracy ◽  
Software Simulation ◽  
Positioning Algorithm

Currently, there is an urgent need for indoor positioning technology. Considering the complexity of indoor environment, this paper proposes a new positioning algorithm (N-CHAN) via the analysis of the error of arrival time positioning (TOA) and the channels of S-V model. It overcomes an obvious shortcoming that the accuracy of traditional CHAN algorithm effected by no-line-of-sight (NLOS). Finally, though MATLAB software simulation, we prove that N-CHAN’s superior performance in NLOS in the S-V channel model, which has a positioning accuracy of centimeter-level and can effectively eliminate the influence of NLOS error on positioning accuracy. Moreover, the N-CHAN can effectively improve the positioning accuracy of the system, especially in the conditions of larger NLOS error.

scholarly journals Channel state information–based multi-level fingerprinting for indoor localization with deep learning

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880671 ◽  
Author(s):  
Tao Li ◽  
Hai Wang ◽  
Yuan Shao ◽  
Qiang Niu
Keyword(s):  
Deep Learning ◽  
Channel State Information ◽  
Deep Neural Networks ◽  
Training Phase ◽  
Channel State ◽  
Positioning Accuracy ◽  
State Information ◽  
Filtering Method ◽  
Multi Level ◽  
Better Than

With the rapid growth of indoor positioning requirements without equipment and the convenience of channel state information acquisition, the research on indoor fingerprint positioning based on channel state information is increasingly valued. In this article, a multi-level fingerprinting approach is proposed, which is composed of two-level methods: the first layer is achieved by deep learning and the second layer is implemented by the optimal subcarriers filtering method. This method using channel state information is termed multi-level fingerprinting with deep learning. Deep neural networks are applied in the deep learning of the first layer of multi-level fingerprinting with deep learning, which includes two phases: an offline training phase and an online localization phase. In the offline training phase, deep neural networks are used to train the optimal weights. In the online localization phase, the top five closest positions to the location position are obtained through forward propagation. The second layer optimizes the results of the first layer through the optimal subcarriers filtering method. Under the accuracy of 0.6 m, the positioning accuracy of two common environments has reached, respectively, 96% and 93.9%. The evaluation results show that the positioning accuracy of this method is better than the method based on received signal strength, and it is better than the support vector machine method, which is also slightly improved compared with the deep learning method.

Thermal Model of a Thinned-Die Cooling System

2003 ◽  
Author(s):  
N. Boiadjieva ◽  
P. Koev
Keyword(s):  
Experimental Data ◽  
Thermal Model ◽  
Cooling System ◽  
Input Power ◽  
The Other ◽  
System Parameters ◽  
Air Intake ◽  
Optical Probing ◽  
Cooling Air ◽  
Better Than

For through-silicon optical probing of microprocessors, the heat generated by devices with power over 100W must be dissipated [1]. To accommodate optical probing, a seemingly elaborate cooling system that controls the microprocessor temperature from 60 to 100° C for device power up to 150W was designed [2]. The system parameters to achieve the desired thermal debug environment were cooling air temperature and air flow. A mathematical model was developed to determine both device temperature and input power. The 3-D heat equation that governs the temperature distribution was simplified to a case of a 1-D rod with one end at the device center and the other at the cooling air intake. Thus the cooling system was reduced to an analytical expression. From experimental data, we computed all coefficients in the model, then ran extensive tests to verify—the accuracy was better than 10% over the entire temperature and power ranges.

Sign in / Sign up

Export Citation Format

Share Document