scholarly journals A noise robust automatic radiolocation animal tracking system

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Liang Wang ◽  
Foivos Diakogiannis ◽  
Scott Mills ◽  
Nigel Bajema ◽  
Ian Atkinson ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Standard Deviation ◽  
Learning Algorithm ◽  
Tracking System ◽  
Sensor Arrays ◽  
Semantic Segmentation ◽  
Global Navigation Satellite Systems ◽  
Localization Error ◽  
Satellite Systems ◽  
Deep Learning Algorithm

AbstractAgriculture is becoming increasingly reliant upon accurate data from sensor arrays, with localization an emerging application in the livestock industry. Ground-based time difference of arrival (TDoA) radio location methods have the advantage of being lightweight and exhibit higher energy efficiency than methods reliant upon Global Navigation Satellite Systems (GNSS). Such methods can employ small primary battery cells, rather than rechargeable cells, and still deliver a multi-year deployment. In this paper, we present a novel deep learning algorithm adapted from a one-dimensional implementing a convolutional neural network (CNN) model, originally developed for the task of semantic segmentation. The presented model () both converts TDoA sequences directly to positions and reduces positional errors introduced by sources such as multipathing. We have evaluated the model using simulated animal movements in the form of TDoA position sequences in combination with real-world distributions of TDoA error. These animal tracks were simulated at various step intervals to mimic potential TDoA transmission intervals. We compare to a Kalman filter to evaluate the performance of our algorithm to a more traditional noise reduction approach. On average, for simulated tracks having added noise with a standard deviation of 50 m, the described approach was able to reduce localization error by between 66.3% and 73.6%. The Kalman filter only achieved a reduction of between 8.0% and 22.5%. For a scenario with larger added noise having a standard deviation of 100 m, the described approach was able to reduce average localization error by between 76.2% and 81.9%. The Kalman filter only achieved a reduction of between 31.0% and 39.1%. Results indicate that this novel 1D CNN like encoder/decoder for TDoA location error correction outperforms the Kalman filter. It is able to reduce average localization errors to between 16 and 34 m across all simulated experimental treatments while the uncorrected average TDoA error ranged from 55 to 188 m.

scholarly journals A Noise Robust Automatic Radiolocation Animal Tracking System

2021 ◽  
Author(s):  
Liang Wang ◽  
Foivos Diakogiannis ◽  
Scott Mills ◽  
Nigel Bajema ◽  
Ian Atkinson ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Learning Algorithm ◽  
Tracking System ◽  
Sensor Arrays ◽  
Semantic Segmentation ◽  
Satellite System ◽  
Deep Learning Algorithm ◽  
Small Primary ◽  
Noise Robust ◽  
Global Navigation Satellite

Abstract Agriculture is becoming increasingly reliant upon accurate data from sensor arrays, with localization an emerging application in the livestock industry. Ground-based Time Difference of Arrival (TDoA) radiolocation methods have the advantage of being lightweight and exhibit higher energy effciency than methods reliant upon Global Navigation Satellite System (GNSS). Such methods can employ small primary cell batteries, rather than rechargeable cells, and still deliver a multi-year deployment. In this paper, we present a novel deep learning algorithm adapted from a one dimensional U-Net like a convolutional neural network (CNN) model, originally developed for the task of semantic segmentation. This model both converts TDoA sequences directly to positions and reduces positional errors introduced by sources such as multipathing. We have evaluated the model by using simulated animal movements in the form of TDoA position sequences in combination with known distributions of TDoA error. When errors with a standard deviation of 50 m and 100 m are added to simulated TDoA transmissions the model is able to reduce this error to 22 m and 27 m (RMSE) respectively. Without correction, the standard deviation of these errors is on the order of 90 and 200 m. Accordingly, the model can reduce the error by greater than 80 m (> 80%), demonstrating the effectiveness of this novel 1D CNN U-Net like encoder/decoder for error correction of TDoA position estimates.

scholarly journals Performance Evaluation of GPS Auto-Surveying Techniques

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7374
Author(s):  
João Manito ◽  
José Sanguino
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Unscented Kalman Filter ◽  
Weighted Least Squares ◽  
Base Station ◽  
Global Navigation Satellite Systems ◽  
Precise Position ◽  
Satellite Systems ◽  
Global Navigation Satellite ◽  
Position Estimate

With the increase in the widespread use of Global Navigation Satellite Systems (GNSS), increasing numbers of applications require precise position data. Of all the GNSS positioning methods, the most precise are those that are based in differential systems, such as Differential GNSS (DGNSS) and Real-Time Kinematics (RTK). However, for absolute positioning, the precision of these methods is tied to their reference position estimates. With the goal of quickly auto-surveying the position of a base station receiver, four positioning methods are analyzed and compared, namely Least Squares (LS), Weighted Least Squares (WLS), Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), using only pseudorange measurements, as well as the Hatch Filter and position thresholding. The research results show that the EKF and UKF present much better mean errors than LS and WLS, with an attained precision below 1 m after about 4 h of auto-surveying. The methods that presented the best results are then tested against existing implementations, showing them to be very competitive, especially considering the differences between the used receivers. Finally, these results are used in a DGNSS test, which verifies a significant improvement in the position estimate as the base station position estimate improves.

scholarly journals Automatic Tunnel Crack Detection Based on U-Net and a Convolutional Neural Network with Alternately Updated Clique

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 717 ◽  
Author(s):  
Gang Li ◽  
Biao Ma ◽  
Shuanhai He ◽  
Xueli Ren ◽  
Qiangwei Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crack Width ◽  
Crack Detection ◽  
Detection Method ◽  
Learning Algorithm ◽  
Semantic Segmentation ◽  
Fully Convolutional Networks ◽  
Deep Learning Algorithm ◽  
Mean Width

Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of 496   ×   496 pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder–decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it’s proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from −11.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.

scholarly journals Kalman Filter-Based RAIM for Reliable Aircraft Positioning with GPS and NavIC Constellations

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6606
Author(s):  
Susmita Bhattacharyya ◽  
Dinesh Mute
Keyword(s):  
Kalman Filter ◽  
Weighted Least Squares ◽  
Unmanned Aircraft ◽  
Global Navigation Satellite Systems ◽  
Navigation Systems ◽  
Satellite Systems ◽  
Vector Tracking ◽  
Receiver Autonomous Integrity Monitoring ◽  
Global Navigation Satellite ◽  
Computational Resources

This paper presents a novel Kalman filter (KF)-based receiver autonomous integrity monitoring (RAIM) algorithm for reliable aircraft positioning with global navigation satellite systems (GNSS). The presented method overcomes major limitations of the authors’ previous work, and uses two GNSS, namely, Navigation with Indian Constellation (NavIC) of India and the Global Positioning System (GPS). The algorithm is developed in the range domain and compared with two existing approaches—one each for the weighted least squares navigation filter and KF. Extensive simulations were carried out for an unmanned aircraft flight path over the Indian sub-continent for validation of the new approach. Although both existing methods outperform the new one, the work is significant for the following reasons. KF is an integral part of advanced navigation systems that can address frequent loss of GNSS signals (e.g., vector tracking and multi-sensor integration). Developing KF RAIM algorithms is essential to ensuring their reliability. KF solution separation (or position domain) RAIM offers good performance at the cost of high computational load. Presented range domain KF RAIM, on the other hand, offers satisfactory performance to a certain extent, eliminating a major issue of growing position error bounds over time. It requires moderate computational resources, and hence, shows promise for real-time implementations in avionics. Simulation results also indicate that addition of NavIC alongside GPS can substantially improve RAIM performance, particularly in poor geometries.

scholarly journals Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8441
Author(s):  
Susmita Bhattacharyya
Keyword(s):  
Kalman Filter ◽  
Measurement Errors ◽  
Weighted Least Squares ◽  
Temporal Variations ◽  
Global Navigation Satellite Systems ◽  
Model Parameters ◽  
Integrity Monitoring ◽  
Satellite Systems ◽  
Performance Analyses ◽  
Global Navigation Satellite

This paper evaluates the performance of an integrity monitoring algorithm of global navigation satellite systems (GNSS) for the Kalman filter (KF), termed KF receiver autonomous integrity monitoring (RAIM). The algorithm checks measurement inconsistencies in the range domain and requires Schmidt KF (SKF) as the navigation processor. First, realistic carrier-smoothed pseudorange measurement error models of GNSS are integrated into KF RAIM, overcoming an important limitation of prior work. More precisely, the error covariance matrix for fault detection is modified to capture the temporal variations of individual errors with different time constants. Uncertainties of the model parameters are also taken into account. Performance of the modified KF RAIM is then analyzed with the simulated signals of the global positioning system and navigation with Indian constellation for different phases of aircraft flight. Weighted least squares (WLS) RAIM used for comparison purposes is shown to have lower protection levels. This work, however, is important because KF-based integrity monitors are required to ensure the reliability of advanced navigation methods, such as multi-sensor integration and vector receivers. A key finding of the performance analyses is as follows. Innovation-based tests with an extended KF navigation processor confuse slow ramp faults with residual measurement errors that the filter estimates, leading to missed detection. RAIM with SKF, on the other hand, can successfully detect such faults. Thus, it offers a promising solution to developing KF integrity monitoring algorithms in the range domain. The modified KF RAIM completes processing in time on a low-end computer. Some salient features are also studied to gain insights into its working principles.

scholarly journals Performance assessment of a low-complexity autoregressive Kalman filter for GNSS carrier tracking using real scintillation time series

GPS Solutions ◽  
2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Sergi Locubiche-Serra ◽  
Gonzalo Seco-Granados ◽  
José A. López-Salcedo
Keyword(s):  
Kalman Filter ◽  
A Priori ◽  
Gaussian Function ◽  
Real Data ◽  
Autoregressive Process ◽  
Low Complexity ◽  
Global Navigation Satellite Systems ◽  
Ionospheric Scintillation ◽  
Satellite Systems ◽  
Phase Dynamics

AbstractIonospheric scintillation is one of the most challenging sources of errors in global navigation satellite systems (GNSS). It is an effect of space weather that introduces rapid amplitude and phase fluctuations to transionospheric signals and, as a result, it severely degrades the tracking performance of receivers, particularly carrier tracking. It can occur anywhere on the earth during intense solar activity, but the problem aggravates in equatorial and high-latitude regions, thus posing serious concerns to the widespread deployment of GNSS in those areas. One of the most promising approaches to address this problem is the use of Kalman filter-based techniques at the carrier tracking level, incorporating some a priori knowledge about the statistics of the scintillation to be dealt with. These techniques aim at dissociating the carrier phase dynamics of interest from phase scintillation by modeling the latter through some correlated Gaussian function, such as the case of autoregressive processes. However, besides the fact that the optimality of these techniques is still to be reached, their applicability for dealing with scintillation in real-world environments also remains to be confirmed. We carry out an extensive analysis and experimentation campaign on the suitability of these techniques by processing real data captures of scintillation at low and high latitudes. We first evaluate how well phase scintillation can be modeled through an autoregressive process. Then, we propose a novel adaptive, low-complexity autoregressive Kalman filter intended to facilitate the implementation of the approach in practice. Last, we provide an analysis of the operational region of the proposed technique and the limits at which a performance gain over conventional tracking architectures is obtained. The results validate the excellence of the proposed approach for GNSS carrier tracking under scintillation conditions.

Accuracy assessment of relative and precise point positioning online GPS processing services

2019 ◽  
Vol 13 (3) ◽  
pp. 215-227 ◽  
Author(s):  
Ahmed El Shouny ◽  
Yehia Miky
Keyword(s):  
Standard Deviation ◽  
Accuracy Assessment ◽  
Cost Effective ◽  
Global Navigation Satellite Systems ◽  
Real Field ◽  
Relative Positioning ◽  
Time Duration ◽  
Satellite Systems ◽  
Positioning Technique ◽  
Time Required

AbstractOver the last few years, several institutions, research centers, and organizations have developed different strategies for GPS data processing and analysis to obtain high accurate coordinates. They are taking into considerations overcoming restrictions and limitations such as necessity of using relative positioning, time consuming, and high cost of different processing software. One of these developed strategies is the web–based online GNSS (Global Navigation Satellite Systems) processing service which estimates coordinates with centimeter or decimeter level accuracy. This study aims to represent the most popular of these services, namely AUSPOS, OPUS, APPS, GAPS, Trimble RTX, and CSRS–PPP, also to perform in-between comparison of these services with respect to their achieved accuracy using field observations with different durations. The obtained results were compared with a real field data measured using traditional relative positioning technique with high accuracy standards. The set of data used in this study was performed in the Eastern zone in Egypt. The results of this comparison indicate that these services can be used as an alternative method for relative positioning technique due to their simplicity, cost-effective and saving in time. They saved about 75 % from the time required for traditional method to produce coordinate with reasonable accuracy. The maximum standard deviation obtained was 15.4 cm from APPS service with time less than two hours, while the minimum standard deviation was 0.07 cm and obtained from CSRS–PPP with time duration from 6 to 12 hours. For achieving more accurate results, it is recommended to use time duration more than two hours.

scholarly journals Analysis of “Kropiva”, “Ukrop”, “Artos” and “Basalt-M” aparatus standard deviation related to the catalog of geodetic points coordinates

2021 ◽  
Vol 42 (II) ◽  
pp. 18-27
Author(s):  
T. KRAVETS ◽  
◽  
O. POLETS ◽  
A. SHCHERBA ◽  
◽  
...  
Keyword(s):  
Standard Deviation ◽  
Experimental Studies ◽  
Armed Forces ◽  
Global Navigation Satellite Systems ◽  
Navigation Systems ◽  
Satellite Systems ◽  
Hardware System ◽  
Satellite Navigation Systems ◽  
Global Navigation Satellite ◽  
Military Units

The aim of the article is to present the results of global navigation satellite systems (GNSS) and geographic information systems (GIS) analysis in military units on the example of software and hardware system (computer appliance CA) “Kropyva”, “Ukrop”, “Artos” and “Basalt-M”, in particular comparison of coordinates determination accuracy by devices in relation to the catalog of geodetic points coordinates and the list of points coordinates of special geodetic networks. Method. The research was carried out on the basis of available literature sources analysis on this subject and practical application of CA “Kropyva”, “Ukrop”, “Artos” and “Basalt-M”, comparison of coordinates definition accuracy and development of recommendations on their basis. Experimental studies of coordinate determination with the help of software without obstacles that would interfere with the passage of satellite signal, and with obstacles, on the basis of which the conclusions are based. Results. Theoretical, methodical and practical problems of using CA “Kropyva”, “Ukrop”, “Artos” and “Basalt-M” in troops for coordinates determining have been studied. The tendencies and prospects of the studied CA are analyzed, taking into account the devices error in the coordinates determining and the expediency of their use for topographic and geodetic support of units. A thorough analysis of four tools for coordinates determining was compared and performed. The ways of satellite navigation systems application and geoinformation systems in military units are presented, on the example of CA “Kropyva”, “Ukrop”, “Artos” and “Basalt-M”. The main tasks are substantiated. Experimental researches of coordinates determining by different CA are carried out, different time intervals of comparisons the data with the catalog of geodetic points coordinates are received. Scientific novelty. The need for this study is due to the fact that although PJSC “Kropyva”, “Ukrop” and “Artos” are authorated for use in the Ukrainian Armed Forces, and “Basalt-M” is in service, or actively used in units, including the Area Joint Forces (JF) operations, but anyone did not performed comparison of coordinate accuracy and application recommendations. There are no scientific works in Ukraine that would relate to the coordinates determining accuracy of the devices in relation to the catalog of geodetic points coordinates of special geodetic boundaries. The main emphasis of the study is on the peculiarities of the use of CA. Their analysis and prospects in the military sphere are carried out. Practical meaning. Based on the analysis of “Kropyva”, “Ukrop”, “Artos” and “Basalt-M” aparatus standard deviation in relation to catalog of geodetic points coordinates, recommendations for the use of tactical level commanders have been developed. The temporal features of PAC application and their influence on the coordinate determinating accuracy are singled out. The CA which is most expedient to apply is established. The results of the study are designed for unit commanders who can use them for more effectively performing of their combat missions.

Utilization of geographically weighted regression for geoid modelling in Egypt

2020 ◽  
Vol 14 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Gomaa M. Dawod ◽  
Tarek M. Abdel-Aziz
Keyword(s):  
Standard Deviation ◽  
Geographically Weighted Regression ◽  
Spatial Modelling ◽  
Global Navigation Satellite Systems ◽  
Weighted Regression ◽  
Geopotential Model ◽  
Satellite Systems ◽  
Precious Resource ◽  
Regression Techniques ◽  
Global Navigation Satellite

AbstractModelling the spatial variations of a specific Global Geopotential Model (GGM) over a spatial area is important to enhance its local performance in Global Navigation Satellite Systems (GNSS) surveying. This study aims to investigate the potential of utilizing some of Geographic Information Systems (GIS) geospatial analysis tools, particularly Geographically Weighted Regression (GWR), in geoid modelling for the first time in Egypt as a case study. Its main target is developing an optimum regression method to be applied in spatial modelling of the deviations of a specific GGM (e. g., PGM17). Using a precise local geodetic dataset of 803 GPS/levelling stations, PGM17 undulation differences have been modelled using different regression techniques to evaluate their precision and accuracy. Based on investigating 13 possible regression formulas of probable combinations of independent variables, results showed that the PGM17 discrepancies over Egypt depend mostly on the terrain heights and geoidal undulations. Over 80 checkpoints, the attained variations between the GWR model and known values varied from −0.574 m to 0.500 m, with a mean of 0.001 m and a standard deviation equals ±0.205 m. Based on available data, it has been found that GWR improved the PGM17 deviations by 9 % in terms of standard deviation and by 98 % in terms of the mean. Additionally, the study generates a reasonably innovative product for the local geodetic community by building an enhanced version of the PGM17. This surface will be a precious resource in GNSS surveying in Egypt for heights conversion, leading to considerable cost reduction in civil engineering works and mapping projects.

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