Estimated Receiver Position Research Articles

Implementation and testing of single point positioning on IRNSS/NavIC receiver data

SummaryIndia has developed its own regional navigation satellite system called the Indian Regional Navigation Satellite System (IRNSS), also known as Navigation with Indian Constellation (NavIC). IRNSS/NavIC provides standard positioning and timing services for civilians and restricted services for authorized users. In this paper, single point positioning is implemented on the NavIC receiver data. Estimation techniques of the least squares method and extended Kalman filter are implemented after applying broadcast‐based clock corrections, orbit computation, relative time correction, and ionosphere delay correction for single and dual frequency. A data extraction and storage module is developed to store computed parameters at every intermediate stage in separate files. This is followed by an error plotter module to plot the extracted parameters for each satellite and to separately analyze their performance and the obtained errors. The estimated receiver position is visualized on Google Earth. The absolute error in both estimation techniques ranges between 5 and 15 m throughout the period.

A novel kinematic positioning algorithm for GPS applications in urban canyons

Evolve a new kinematic positioning algorithm to estimate the position accurately in low latitude region and assess the algorithm's performance in terms of accuracy and precision is the objective of this research paper. The Extended Kalman Filter (EKF) has been widely recognized as one of the most powerful state estimation techniques in estimating system of state variables and deteriorating measurement noise. The Extended Kalman Filter is required because the uncertainty in the Minimum Mean Square Error (MMSE) estimation can be minimized. In this article, the researcher carried out a new kinematic positioning algorithm named as Cross-Correntropy Kalman Filter (CCKF) to enhance the position accuracy and performance of the Global Positioning System (GPS) receiver. Primarily, the augmentation depends on the Cross-Correntropy criterion which is a measure of local similarity index, and a novel Fixed-Point algorithm for updating subsequent estimates. In this work, the researcher presents a thorough derivation of the method is suggested, and how to estimate the Global Positioning System receiver position in low latitudes of the Indian geographical area accurately. Furthermore, in this research paper, a comparison between estimated receiver positions, measured error and performance metrics (2-Dimensional & 3-Dimensional) together with graphical illustrations for both the algorithms (EKF & CCKF) are presented. Performance of the two algorithms (EKF and CCKF) are evaluated by considering the data of a dual frequency GPS receiver located at IGS station: IISc Bangalore (lat/lon: 13.01° N/77.56° E). The re-enactment outcomes of the proposed algorithm show that the estimated receiver position is much closer to the receiver true position. In this article, batch processing data of IGS station, IISc Bangalore, obtained from IGS network of Scrips Orbit Permanent Array Center (SOPAC) is given as input and output yields in East, North and Up directions of the receiver position.

Performance evaluation of suitable navigation algorithm using raw measurements taken from stationary GPS receiver

The Global Positioning system prevalently known as GPS is a range based positioning scheme that gives a 3D position of an obscure object on top of the earth. Object location accuracy commonly depends on the satellite clock error, atmospheric delays, multipath, poor satellite geometry, and receiver measurement noise, etc. Mostly, none of the above parameters do have constant behavior throughout the world and should be inspected territorially to give an exact solution. The objective of this article is to estimate the receiver position accurately in low latitude regions and assess the algorithm's performance in terms of accuracy and precision. In this work, we implemented three navigational algorithms Least Squares method (LS), Kalman filter (KF), and Extended Kalman Filter (EKF) to estimate the position accurately. Furthermore, in this research paper, a comparison of the estimated receiver position, measured error and mean squared error (RMS) together with graphical illustrations for three algorithms (LS, KF & EKF) is presented. The performance of the implemented algorithms (LS, KF, and EKF) is evaluated by considering the data of a dual-frequency GPS receiver located at IGS station: IISc Bangalore (lat/lon: 13.010 N/77.56 0 E). The re-enactment outcomes show that the estimated receiver position with EKF is much closer to the receiver's true position. To evaluate the algorithms, batch processing data of IGS station, IISc Bangalore, obtained from IGS network of Scrips Orbit Permanent Array Center (SOPAC) is given as input and output yields coordinates (X, Y, Z) of the receiver position.

Doppler‐aided rapid positioning method for BDS receivers

BeiDou navigation satellite system (BDS) adopts mixed constellation design. In addition, the rate of GEO NAV message is 500 bit/s. Therefore, a rapid BDS positioning algorithm with Doppler-aided is presented to reduce the time to first fix. First, the approximate position is obtained using full pseudo-ranges of GEO satellites and Doppler of all visible satellites. Secondly, the full pseudo-ranges of non-GEO satellites can be constructed with the approximate and reference GEO satellites. Finally, the receiver position can be estimated with full pseudo-ranges in an accurate manner. Experimental data processing results show that the proposed method is effective to estimate receiver position.

Indoor positioning modeling by visible light communication and imaging

This letter presents a model of an indoor light positioning system (LPS) based on white LEDs and a camera. The position of an LPS receiver is determined through both its relative position to LEDs according to their images captured by the camera and LEDs' absolute position information in the navigation frame, obtained through a visible light communication (VLC) link. The error performance of the proposed LPS is analyzed. The mean error and mean square error (MSE) of estimated receiver position using least squares (LS) and weighted least squares (WLS) estimators are both derived in the presence of non-uniform measurement bias and white Gaussian noise. The effects of communication data rate on the positioning accuracy are also studied through BER performance.

6-Axis Sensor Assisted Low Complexity High Accuracy-Visible Light Communication Based Indoor Positioning System

The authors focus on the improvement of Visible Light Communication Identification (VLID) system that provides positioning information via LED light bulb, which is a part of Visible Light Communication (VLC) system. The conventional VLID system provides very low positioning estimation accuracy at room level. In our approach, neither additional infrastructure nor modification is required on the transmitter side. On the receiver side, 6-axis sensor is embedded to provide 3-axis of Azimuth and 3-axis of Tilt angulations information to perform positioning estimation. We verify the proposed system characteristics by making both empirical and numerical analysis, to confirm the effectiveness of proposed system. We define two words to justify the characteristic of the proposed system, which are Field-of-View (FOV: ψc) Limit and Sensitivity (RXS) Limit. Both FOV and Sensitivity Limits have crucial impact on positioning estimation accuracy. Intuitively, higher positioning accuracy can be achieved with smaller FOV configuration in any system that has FOV. Conversely, based on system characteristics of VLID, we propose a positioning estimation scheme, namely Switching Estimated Receiver Position (SwERP) yields high accuracy even with wide FOV configuration. Cumulative Distribution Function (CDF) of error distance and Root Mean Square of Error Distance (RMSED) between experimental positions and estimated receiver positions are used to indicate the system performance. We collected 440 samples from 3 receivers' FOV configurations altogether 1320 samples within the experimental area of 1200mm × 5000mm × 2050mm. The results show that with the proposed scheme, the achievable RMSEDs are in the range of 298 and 463mm under different FOV configurations, which attained the maximum accuracy improvement over 80% comparing to the one without positioning estimation scheme. The proposed system's achievable accuracy does not depend on transmitters' orientation; only one transmitter is required to perform positioning estimation.