Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles

Elsheikh, Mohamed; Abdelfatah, Walid; Nourledin, Aboelmagd; Iqbal, Umar; Korenberg, Michael J.

doi:10.3390/s19224896

Cited by 39 publications

(30 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the nonlinear model can be used as a reference model to ensure the adequacy of the Kalman filter model and the real change process of INS errors. Figure 1 presents a scheme of INS correction by genetic algorithm (GA) [28,29], where NKF denotes the nonlinear Kalman filter, and C denotes the divergence indicator of the evaluation process. The listed implementations of the Kalman nonlinear filter require linearization of the INS error model using the Taylor series, representing the posterior density as a set of δ functions, or replacing the posterior density with a system of partial Gaussian densities with different weights.…”

Section: Methods Of Realization Of Nonlinear Kalman Filtermentioning

confidence: 99%

Section: Methods Of Realization Of Nonlinear Kalman Filtermentioning

confidence: 99%

See 1 more Smart Citation

Correction Algorithm for the Navigation System of an Autonomous Unmanned Underwater Vehicle

Chen

Neusypin

Selezneva

2020

Sensors

View full text Add to dashboard Cite

More accurate navigation systems are always required for autonomous unmanned underwater vehicles (AUUV)s under various circumstances. In this paper, a measuring complex of a heavy unmanned underwater vehicle (UUV) was investigated. The measuring complex consists of an inertial navigation platform system, a Doppler lag (DL) and an estimation algorithm. During a relatively long-term voyage of an UUV without surfacing and correction from buoys and stationary stations, errors of the measuring complex will increase over time. The increase in errors is caused by an increase in the deviation angles of the gyro platform relative to the accompanying trihedron of the selected coordinate system. To reduce these angles, correction is used in the structure of the inertial navigation system (INS) using a linear regulator. To increase accuracy, it is proposed to take into account the nonlinear features of INS errors; an adaptive nonlinear Kalman filter and a nonlinear controller were used in the correction scheme. Considering that, a modified nonlinear Kalman filter and a regulator in the measuring complex are proposed to improve the accuracy of the measurement information, and modification of the nonlinear Kalman filter was performed through a genetic algorithm, in which the regulator was developed by the State Dependent Coefficient (SDC) method of the formulated model. Modeling combined with a semi-natural experiment with a real inertial navigation system for the UUV demonstrated the efficiency and effectiveness of the proposed algorithms. Doppler lag, which are combined into a measuring complex (MC). The INS and lag signals are processed together using the Kalman filter [8][9][10][11].When the UUV is working under ice fields in the Arctic, there is no possibility of periodic ascent to the surface of the sea; thus, INS correction from a gyro-stabilized platform (GSP) is not provided. In the case of long-term autonomous navigation with the use of a strapdown INS, errors increase over time due to the instability of sensitive elements. Moreover, when UUVs perform maneuvers to complete tasks with long-term autonomous navigation, even for platform INS, errors will reach large values. This is due to an increase in the deviation angles of the gyro-stabilized platform (GSP) relative to the accompanying coordinate system (SC). Even with the correction of INS from a lag and information processing by the Kalman filter, errors of the navigation information increase as the model of INS errors in the Kalman filter becomes inadequate for the real process.Considering the prospective applications, scientists have been interested in AUUVs and all the particular constraints in different media have been formulated into mathematical problems. Wu et al.[12] generated the optimal paths based on the Particle Swarm Optimization (PSO) algorithm and the Kalman filter to finish an underwater target strike mission; Batista et al. [13] proposed a filtering method with applications to estimate the linear motion of underwater vehicles, taking into considert...

show abstract

Section: Methods Of Realization Of Nonlinear Kalman Filtermentioning

confidence: 99%

Section: Methods Of Realization Of Nonlinear Kalman Filtermentioning

confidence: 99%

Correction Algorithm for the Navigation System of an Autonomous Unmanned Underwater Vehicle

Chen

Neusypin

Selezneva

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…In the above-mentioned studies, geodetic-grade GNSS receivers were used with tactical-grade IMUs to provide precise positioning and attitude solutions. The cost issue of the integrated system was addressed through the use of a single-frequency (SF) GNSS chipset along with a consumer-grade IMU [19] as well as through the implementation of a reduced inertial sensor system (RISS), as opposed to a full IMU [20]. In [19], a loosely coupled (LC) SF GNSS PPP/INS integrated system was assessed for land vehicular navigation.…”

Section: Introductionmentioning

confidence: 99%

“…The cost issue of the integrated system was addressed through the use of a single-frequency (SF) GNSS chipset along with a consumer-grade IMU [19] as well as through the implementation of a reduced inertial sensor system (RISS), as opposed to a full IMU [20]. In [19], a loosely coupled (LC) SF GNSS PPP/INS integrated system was assessed for land vehicular navigation. Their system used the low-cost u-blox EVK-8MT SF GNSS chipset along with the LSM6DSL consumer-grade IMU.…”

Section: Introductionmentioning

confidence: 99%

Ultra-Low-Cost Tightly Coupled Triple-Constellation GNSS PPP/MEMS-Based INS Integration for Land Vehicular Applications

Elmezayen

El-Rabbany

2021

Geomatics

View full text Add to dashboard Cite

The rapid rise of ultra-low-cost dual-frequency GNSS chipsets and micro-electronic-mechanical-system (MEMS) inertial sensors makes it possible to develop low-cost navigation systems, which meet the requirements for many applications, including self-driving cars. This study proposes the use of a dual-frequency u-blox F9P GNSS receiver with xsens MTi670 industrial-grade MEMS IMU to develop an ultra-low-cost tightly coupled (TC) triple-constellation GNSS PPP/INS integrated system for precise land vehicular applications. The performance of the proposed system is assessed through comparison with three different TC GNSS PPP/INS integrated systems. The first system uses the Trimble R9s geodetic-grade receiver with the tactical-grade Stim300 IMU, the second system uses the u-blox F9P receiver with the Stim300 IMU, while the third system uses the Trimble R9s receiver with the xsens MTi670 IMU. An improved robust adaptive Kalman filter is adopted and used in this study due to its ability to reduce the effect of measurement outliers and dynamic model errors on the obtained positioning and attitude accuracy. Real-time precise ephemeris and clock products from the Centre National d’Etudes Spatials (CNES) are used to mitigate the effects of orbital and satellite clock errors. Three land vehicular field trials were carried out to assess the performance of the proposed system under both open-sky and challenging environments. It is shown that the tracking capability of the GNSS receiver is the dominant factor that limits the positioning accuracy, while the IMU grade represents the dominant factor for the attitude accuracy. The proposed TC triple-constellation GNSS PPP/INS integrated system achieves sub-meter-level positioning accuracy in both of the north and up directions, while it achieves meter-level positioning accuracy in the east direction. Sub-meter-level positioning accuracy is achieved when the Stim300 IMU is used with the u-blox F9P GNSS receiver. In contrast, decimeter-level positioning accuracy is consistently achieved through TC GNSS PPP/INS integration when a geodetic-grade GNSS receiver is used, regardless of whether a tactical- or an industrial-grade IMU is used. The root mean square (RMS) errors of the proposed system’s attitude are about 0.878°, 0.804°, and 2.905° for the pitch, roll, and azimuth angles, respectively. The RMS errors of the attitude are significantly improved to reach about 0.034°, 0.038°, and 0.280° for the pitch, roll, and azimuth angles, respectively, when a tactical-grade IMU is used, regardless of whether a geodetic- or low-cost GNSS receiver is used.

show abstract

“…At present, the commonly used indoor positioning technologies are laser, inertial navigation system, infrared, and wireless local area network (WLAN), but these indoor navigation technologies cannot be widely used because of problems such as allele accuracy and cost. With the rapid development of machine vision and computer technology, the performance of small industrial cameras and the microelectromechanical system (MEMS) inertial devices was continuously improved [2][3][4]. The technology of a visual-inertial odometer (VIO) was gradually realized in engineering applications at the theoretical verification stage.…”

Section: Introductionmentioning

confidence: 99%

Improving Positioning Accuracy via Map Matching Algorithm for Visual–Inertial Odometer

Meng

Ren

Wang

et al. 2020

Sensors

View full text Add to dashboard Cite

A visual–inertial odometer is used to fuse the image information obtained by a vision sensor with the data measured by an inertial sensor and recover the motion track online in a global frame. However, in an indoor environment, geometric transformation, sparse features, illumination changes, blurring, and noise will occur, which will either cause a reduction in or failure of the positioning accuracy. To solve this problem, a map matching algorithm based on an indoor plane structure map is proposed to improve the positioning accuracy of the system; this algorithm was implemented using a conditional random field model. The output of the attitude information from the visual–inertial odometer was used as the input of the conditional random field model. The feature function between the attitude information and the expected value was established, and the maximum probabilistic value of the attitude was estimated. Finally, the closed-loop feedback correction of the visual–inertial system was carried out with the probabilistic attitude value. A number of experiments were designed to verify the feasibility and reliability of the positioning method proposed in this paper.

show abstract

Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles

Cited by 39 publications

References 25 publications

Correction Algorithm for the Navigation System of an Autonomous Unmanned Underwater Vehicle

Correction Algorithm for the Navigation System of an Autonomous Unmanned Underwater Vehicle

Ultra-Low-Cost Tightly Coupled Triple-Constellation GNSS PPP/MEMS-Based INS Integration for Land Vehicular Applications

Improving Positioning Accuracy via Map Matching Algorithm for Visual–Inertial Odometer

Contact Info

Product

Resources

About