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Position Error Analysis of IRNSS Data Using Big Data Analytics

  • M. Geetha Priya
  • D. C. Kiran Kumar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 801)

Abstract

The current investigation involves the study and analysis of position error of IRNSS (Indian Regional Navigation Satellite System) receiver system data with respect to a fixed position using big data analytics tool namely, Hadoop. IRNSS is an indigenously developed navigation system by India with constellation of seven satellites. IRNSS data pertaining to week number 918 has been used to carry out the statistical analysis in three different modes of operation. Queries/commands were created in Hadoop to process more than 6 million data (week no. 918 data). The processed data and calculated statistical parameters show that IRNSS navigation system is a promising solution for various navigation related applications. The results obtained highlights that the position error is minimum in Hybrid (GPS-L1 + IRNSS-S + IRNSS-L5) mode of operation with better accuracy when compared with individual frequency mode of operation (L5 & S) of IRNSS receiver system.

Keywords

GNSS IRNSS Satellites Position error Hadoop 

1 Introduction

Global Navigation Satellite System (GNSS) is used to provide autonomous geo-spatial positioning in terms of longitude and latitude using the constellation of satellites. GPS (Global positioning System) is a one such type of GNSS with network of satellites used to provide the precise information about position that is widely being used by many users all over the world. The GNSS signals are received by the receivers to provide exact location and timing information. IRNSS is India’s own regional navigation system [1] renamed as NAVIC (Navigation with Indian constellation). The NAVIC system is used for facilitating position, navigation, and tracking and also to calculate the time with high accuracy. IRNSS receiver system is designed to deliver position/location information to Indian users and the coverage area extends up to one thousand five hundred (1500) km [2] from the boundary of India, which is its primary service area. Figure 1 shows IRNSS receiver system, antenna and sky plot of available satellites. The IRNSS System is providing a position accuracy of 20 m in principal service area. IRNSS provides two types of services namely RS (Restricted Service) that is encrypted & available only for authorized users with high precision and Standard Positioning Service (SPS) to all users. Presently there are seven satellites in the constellation [3, 4, 5] placed above 36,000 km from the surface of earth. Three satellites are placed in geostationary orbit at 32.5° East, 83° East, and 131.5° East longitude with fixed position. The other four satellites are in geosynchronous orbit, where each set of satellites will cross the equator at 55° and 111.75° East.
Fig. 1.

IRNSS receiver system, antenna and sky plot

The IRNSS satellite signals are available at L5 band (1176.45 × 106 Hz) and S band (2492.028 × 106 Hz) microwave frequencies. The SPS (Standard positioning Service) signal is modulated by 1 × 106 Hz BPSK (Binary Phase shift keying) [6, 7] signal and RS (Restricted Service) signal is modulated with BOC (Binary offset carrier) for high precision and security. To maintain necessary coverage area and signal strength, the signal is being transmitted through phased array antenna.

A Java written open source framework (Apache) big data analytics tool-Hadoop is used for processing of large datasets in a distributed way across network of computers using robust programming models. Hadoop manages applications with MapReduce algorithm by processing the data on different CPU nodes in parallel. In simple, Hadoop framework allows the users to develop applications that are compatible to run on clusters of computers and also it can achieve complete statistical study for a large quantity of data. In this study, Hadoop frame work is used to analyze the IRNSS data statistically to assess the position error. This paper has been organized with methodology & mathematical modeling in Sect. 2 with simulation results and conclusion in Sects. 3 and 4 respectively.

2 Methodology

2.1 IRNSS Data and HDFS

The data from the IRNSS receiver installed at the site with respect to a fixed position (Latitude = 12.77514° N and Longitude = 77.48794° E with X coordinate = 1345092.73, Y coordinate = 6073169.66, Z coordinate = 1408458.60) is utilized to carry out the statistical analysis using Hadoop framework. The IRNSS data pertaining to week No 918 (27-3-2017 to 1-4-2017) received with the installed receiver is the data of interest for this analysis. The data received from the IRNSS SPS RX is loaded to the HDFS (Hadoop Distributed File System) Environment using Sqoop. Sqoop is a tool designed to transfer data between relational database servers and Hadoop. Hadoop is supported with tools like Sqoop and Hive in a Linux platform. Hadoop uses Hive to process data which is a data warehouse structure tool. Figure 2 gives the software framework with architecture storage and functional flow of Hadoop.
Fig. 2.

Software framework (a) Hadoop architecture (b) HDFS storage (c) process flow

In general, the IRNSS data consists of files related to 3 solutions namely sol_1 (Mode 1), sol_2 (Mode 2), sol_3 (Mode 3) along with satellite information, PR (pseudo random) log & iono tropo. For week no 918, Sol_1 was assigned as hybrid mode, which is the combination of GPS (L1) + IRNSS (L5 + S), Sol_2 was assigned as IRNSS S Band and Sol_3 was assigned as IRNSS L5 Band. The three solutions (modes) consists of parameters like week no, user time, system time, solution type, iono type, X coordinates, Y coordinates, Z coordinates, latitude, longitude, altitude, geometric dilution of precision (GDOP), position dilution of precision (PDOP), vertical dilution of precision (VDOP), horizontal dilution of precision (HDOP), time dilution of precision (TDOP), position error, number of satellites, velocity information, etc. Satellite information data (file) consists of week no, user time, system time, pseudo random noise (PRN), elevation, azimuth, carrier to noise density ratio (CNO) and X, Y, Z coordinates of all the satellites. PR log data (file) consists of week no, user time, system time, Doppler Frequency (FDOPP) and Iono tropo data (file) consists of week no, user time, system time, iono, tropo information.

2.2 Mathematical Modeling for Statistical Analysis Using Hadoop

The Hive structural table created for sol_1 (Mode 1), sol_2 (Mode 2), sol_3 (Mode 3), satellite information, PR log & iono tropo using Hive queries can be used for all weeks of IRNSS data. The data is loaded to the hive structured table and week number 918 data is selected by removing all the invalid data from the other sources. The process flow for the IRNSS data analysis using Hadoop is shown in Fig. 3.
Fig. 3.

Process flow

The data processed by Hadoop gives the minimum and maximum values of X, Y & Z coordinates, standard deviation, position errors, RMS error and mean position of X, Y & Z. The 3D RMS position is found by taking the mean square error with the help of standard reference values that was calculated by averaging X, Y and Z coordinates for a specific period of time. The ECEF (Earth center earth fixed) values are taken to calculate the mean X, Y, Z positions by using hive queries.

Hadoop uses Hive to process data which is a data warehouse structure tool. Hive exists on top of Hadoop to querying and analyzing Big Data and this makes querying/analyzing much easier for the user. For the present study, Hive queries are used for measuring the minimum, maximum, standard deviation, RMS error and mean position error of the X, Y & Z coordinates (of IRNSS data week no. 918) to find the positional information of the receiver. A sample of queries used for this study is given in Table 1.
Table 1.

Sample Queries

Data/file

Queries

Sol_1:

CREATE EXTERNAL TABLE Sol_1(Col_1 datatype, Col_2 datatype ….)

Sol_2:

CREATE EXTERNAL TABLE Sol_2(Col_1 datatype, Col_2 datatype ….)

Sol_3:

CREATE EXTERNAL TABLE Sol_3(Col_1 datatype, Col_2 datatype ….)

Satellite info:

CREATE EXTERNAL TABLE Satellite_info(Col_1 datatype, Col_2 datatype …)

Iono tropo:

CREATE EXTERNAL TABLE iono_tropo(Col_1 datatype, Col_2 datatype …)

PR log:

CREATE EXTERNAL TABLE pr_log(Col_1 datatype, Col_2 datatype …)

Commands:

Select the data of week no 918:

select * from sol_1 where week no = 918

Select only the valid data

select * from sol_1 where ux > 0.0

Bias_X, Bias_Y, Bias_Z:

select ux, uy, uz, round((ux - 1345092.73), 2) as bias_x, round((uy - 6073169.66), 2) as bias_y, round((uz - 1408458.60), 2) as bias_z from sol_1_f

Mean_X, Mean_Y, Mean_Z:

select avg(bias_x) as mean_x, avg(bias_y) as mean_y, avg(bias_z) as mean_z from sol_1_f_xyz

Min & Max of X, Y & Z bias:

select min(bias_x) as min_x, max(bias_x) as max_x, min(bias_y) as min_y, max(bias_y) as max_y, min(bias_z) as min_z, max(bias_z) as max_z from sol_1_bias

Standard deviation:

select stddev_pop(bias_x) as std_x, stddev_pop(bias_y) as std_y, stddev_pop(bias_z) as std_z from sol_1_bias

ux, uy & uz pos:

select ux, uy, uz from sol_1_f

Standard deviation of pos:

select stddev_pop(ux) as std_x_pos, stddev_pop(uy) as std_y_pos, stddev_pop(uz) as std_z_pos from sol_11

Mean_X, Mean_Y, Mean_Zpos:

select avg(ux) as mean_x_pos, avg(uy) as mean_y_pos, avg(uz) as mean_z_pos from sol_11

Sum of bias_X, bias_Y, bias_Z:

select sum(bias_x) as sum_x, sum(bias_y) as sum_y, sum(bias_z) as sum_z from sol_1_bias

3 Results and Discussion

In this study, the position error of IRNSS receiver data is analyzed with respect to a fixed position (user location latitude = 12.77514° N and longitude = 77.48794° E with X coordinate = 1345092.73, Y coordinate = 6073169.66, Z coordinate = 1408458.60). This study is an attempt to utilize open source tools like Hadoop to carry out the statistical analysis of position error from the IRNSS data. Figures 4 and 5 gives the position error plot for full week no. 918 & for 8 h of a specific day in all three modes respectively. Using the queries and commands shown in Table 1, the minimum, maximum, standard deviation, mean and other statistical parameters required for the analysis are obtained using Hadoop tool with respect to position error. The statistical parameter values obtained are listed in Table 2 for all modes.
Fig. 4.

Position error plot for week no. 918 for all three modes.

Fig. 5.

Position error plot for 8 h (12 pm to 8 pm, 28-3-2017 of week no. 918) of data.

Table 2.

Statistical analysis of GPS & IRNSS data using Hadoop framework for week no. 918

Parameters

Hybrid: GPS-L1 + IRNSS-S + IRNSS-L5 (m)

IRNSS-S (m)

IRNSS-L5 (m)

3D RMS position error

3.24727

3.23492

3.235524

Max X error

46.23

2.63

4.88

Min X error

−16.91

−8.63

−9

Max Y error

87.08

39.73

47.83

Min Y error

10.95

20.72

19.81

Max Z error

6.76

6.05

7.72

Min Z error

−20.01

−103.35

−102.7

Mean X error

4.97

−5.06

−7.28

Mean Y error

0.0029

7.54

−4.02

Mean Z error

−8.12

−5.55

−8.59

SD X error

0.35

0.57

0.84

SD Y error

0.96

1.41

1.55

SD Z error

0.57

1.42

2.02

Mean X position

1345097.70

1345087.66

1345085.44

Mean Y position

6073169.66

6073177.20

6073165.63

Mean Z position

1408450.47

1408453.04

1408450.00

Reference X position

1345092.73

Reference Y position

6073169.66

Reference Z position

1408458.60

From Table 2, it is observed that,
  • Rows 3, 5 & 7 shows that IRNSS-S band is giving better results in terms of maximum position error (X, Y & Z) compared to Hybrid and IRNSS-L5 band.

  • From rows 4 & 6, it is observed that hybrid mode is better in giving minimum position error for X & Y coordinates compared to IRNSS-S & L5 band. Row 8 shows that IRNSS-S band gives better in terms of minimum position error for Z coordinates in comparison with Hybrid and IRNSS-L5 band.

  • From row 12, 13 & 14, it is seen that the standard deviation (X, Y & Z error) with respect to reference value is minimum in hybrid mode in comparison with IRNSS-S & L5 band. The second best solution is IRNSS-S band.

  • From rows 9, 10 & 11, it is observed that IRNSS-L5 band is giving minimum average position error when compared to Hybrid and IRNSS-S band.

  • From row 15 & 18, it is seen than the difference between mean X position and reference X position is minimum in hybrid solution compared to IRNSS-S & L5 band.

  • From row 16 & 19, it is seen than the difference between mean Y position and reference Y position is minimum in hybrid solution compared to IRNSS-S & L5 band.

  • From row 17 & 20, it is seen than the difference between mean Z position and reference Z position is minimum in IRNSS-S band solution compared to hybrid & IRNSS-L5 band.

The proposed study shows that hybrid mode of operation is giving the best position information with minimum position error and maximum accuracy than the individual L5 or S band of IRNSS receiver system. The second best solution is given by IRNSS-L5 band.

4 Conclusion

This study is an attempt to utilize open source tools like Hadoop to carry out the statistical analysis of position error from the IRNSS data. The Data is collected in three different modes like Hybrid (GPS + IRNSS (L5 + S)), IRNSS(S) and IRNSS (L5) and processed individually to measure the effectiveness of IRNSS. This study cum analysis shows that, there is an improvement in accuracy and visibility with respect to hybrid mode of operation with IRNSS L5 band giving the second best solution. The position error analysis computed using Hadoop for IRNSS receiver, is essential for understanding how IRNSS works, and for knowing what degree of errors could be anticipated. This helps in making corrections for receiver clock errors and other effects. This kind of analysis could be carried out for any GNSS receivers. This paper provides an overview for researchers in GNSS field to choose the appropriate solution depending on the applications.

Notes

Acknowledgment

The authors would like to thank SAC ISRO-Ahmadabad, Sri Sringeri Sharada Peetham-Sringeri and Jyothy Institute of Technology-Bangalore for providing an opportunity to work with IRNSS receiver system.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Centre for Incubation, Innovation, Research and ConsultancyJyothy Institute of TechnologyBangaloreIndia

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