scholarly journals Research on Time-Correlated Errors Using Allan Variance in a Kalman Filter Applicable to Vector-Tracking-Based GNSS Software-Defined Receiver for Autonomous Ground Vehicle Navigation

2019 ◽  
Vol 11 (9) ◽  
pp. 1026 ◽  
Author(s):  
Luo ◽  
Li ◽  
Yu ◽  
Xu ◽  
Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Precision Agriculture ◽  
Low Cost ◽  
Field Tests ◽  
Satellite System ◽  
Allan Variance ◽  
Correlated Errors ◽  
Ground Vehicle ◽  
Correlated Error ◽  
Vector Tracking

The global navigation satellite system (GNSS) has been applied to many areas, e.g.,the autonomous ground vehicle, unmanned aerial vehicle (UAV), precision agriculture, smart city,and the GNSS-reflectometry (GNSS-R), being of considerable significance over the past few decades.Unfortunately, the GNSS signal performance has the high risk of being reduced by the environmentalinterference. The vector tracking (VT) technique is promising to enhance the robustness in highdynamics as well as improve the sensitivity against the weak environment of the GNSS receiver.However, the time-correlated error coupled in the receiver clock estimations in terms of the VT loopcan decrease the accuracy of the navigation solution. There are few works present dealing with thisissue. In this work, the Allan variance is accordingly exploited to specify a model which is expectedto account for this type of error based on the 1st-order Gauss-Markov (GM) process. Then, it is usedfor proposing an enhanced Kalman filter (KF) by which this error can be suppressed. Furthermore,the proposed system model makes use of the innovation sequence so that the process covariancematrix can be adaptively adjusted and updated. The field tests demonstrate the performance of theproposed adaptive vector-tracking time-correlated error suppressed Kalman filter (A-VTTCES-KF).When compared with the results produced by the ordinary adaptive KF algorithm in terms of the VTloop, the real-time kinematic (RTK) positioning and code-based differential global positioning system(DGPS) positioning accuracies have been improved by 14.17% and 9.73%, respectively. On the otherhand, the RTK positioning performance has been increased by maximum 21.40% when comparedwith the results obtained from the commercial low-cost U-Blox receiver.

scholarly journals Adaptive Filtering on GPS-Aided MEMS-IMU for Optimal Estimation of Ground Vehicle Trajectory

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5357 ◽  
Author(s):  
Haseeb Ahmed ◽  
Ihsan Ullah ◽  
Uzair Khan ◽  
Muhammad Bilal Qureshi ◽  
Sajjad Manzoor ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Autonomous Navigation ◽  
Inertial Sensors ◽  
Low Cost ◽  
Optimal Estimation ◽  
Cost Effective ◽  
Estimation Accuracy ◽  
Ground Vehicle ◽  
Navigation Data ◽  
Mems Imu

Fusion of the Global Positioning System (GPS) and Inertial Navigation System (INS) for navigation of ground vehicles is an extensively researched topic for military and civilian applications. Micro-electro-mechanical-systems-based inertial measurement units (MEMS-IMU) are being widely used in numerous commercial applications due to their low cost; however, they are characterized by relatively poor accuracy when compared with more expensive counterparts. With a sudden boom in research and development of autonomous navigation technology for consumer vehicles, the need to enhance estimation accuracy and reliability has become critical, while aiming to deliver a cost-effective solution. Optimal fusion of commercially available, low-cost MEMS-IMU and the GPS may provide one such solution. Different variants of the Kalman filter have been proposed and implemented for integration of the GPS and the INS. This paper proposes a framework for the fusion of adaptive Kalman filters, based on Sage-Husa and strong tracking filtering algorithms, implemented on MEMS-IMU and the GPS for the case of a ground vehicle. The error models of the inertial sensors have also been implemented to achieve reliable and accurate estimations. Simulations have been carried out on actual navigation data from a test vehicle. Measurements were obtained using commercially available GPS receiver and MEMS-IMU. The solution was shown to enhance navigation accuracy when compared to conventional Kalman filter.

scholarly journals Moving Horizon Estimation for GNSS-IMU sensor fusion

2020 ◽  
pp. 112-122
Author(s):  
Guido Sánchez ◽  
Marina Murillo ◽  
Lucas Genzelis ◽  
Nahuel Deniz ◽  
Leonardo Giovanini
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Satellite System ◽  
Measurement Unit ◽  
Moving Horizon Estimation ◽  
Ground Vehicle ◽  
Global Navigation Satellite ◽  
Time Systems ◽  
True Values

The aim of this work is to develop a Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensor fusion system. To achieve this objective, we introduce a Moving Horizon Estimation (MHE) algorithm to estimate the position, velocity orientation and also the accelerometer and gyroscope bias of a simulated unmanned ground vehicle. The obtained results are compared with the true values of the system and with an Extended Kalman filter (EKF). The use of CasADi and Ipopt provide efficient numerical solvers that can obtain fast solutions. The quality of MHE estimated values enable us to consider MHE as a viable replacement for the popular Kalman Filter, even on real time systems.

scholarly journals A New DGNSS Positioning Infrastructure for Android Smartphones

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 487 ◽  
Author(s):  
Duojie Weng ◽  
Xingli Gan ◽  
Wu Chen ◽  
Shengyue Ji ◽  
Yangwei Lu
Keyword(s):  
Low Cost ◽  
Field Tests ◽  
Ground Truth ◽  
Satellite System ◽  
Reference Station ◽  
Efficient Technology ◽  
Position Correction ◽  
Gnss Measurements ◽  
Global Navigation Satellite ◽  
Server Architecture

One’s position has become an important piece of information for our everyday lives in a smart city. Currently, a position can be obtained easily using smartphones that is equipped with low-cost Global Navigation Satellite System (GNSS) chipsets with accuracy varying from 5 m to 10 m. Differential GNSS (DGNSS) is an efficient technology that removes the majority of GNSS errors with the aid of reference stations installed at known locations. The sub-meter accuracy can be achieved when applying the DGNSS technology on the advanced receivers. In 2016, Android has opened the accesses of raw GNSS measurements to developers. However, most of the mid and low-end smartphones only provide the data using the National Marine Electronics Association (NMEA) protocol. They do not provide the raw measurements, and thus do not support the DGNSS operation either. We proposed a DGNSS infrastructure that correct the standalone GNSS position of smartphones using the corrections from the reference station. In the infrastructure, the position correction is generated considering the GNSS satellite IDs that contribute to the standalone solution in smartphones, and the position obtained is equivalent to the solution of using the range-domain correction directly. To serve a large number of smartphone users, a Client/Server architecture is developed to cope with a mass of DGNSS positioning requests efficiently. The comparison of the proposed infrastructure against the ground truth, for all field tests in open areas, showed that the infrastructure achieves the horizontal positioning accuracy better than 2 m. The improvement in accuracy can reach more than 50% for the test in the afternoon. The infrastructure brings benefits to applications that require more accuracy without requiring any hardware modifications.

Development and Testing of a Low-Cost Portable Apparent Soil Electrical Conductivity Sensor Using a Beaglebone Black

2020 ◽  
Vol 36 (3) ◽  
pp. 341-355
Author(s):  
Daniel M. Queiroz ◽  
Emanoel D. T. S. Sousa ◽  
Won Suk Lee ◽  
John K. Schueller
Keyword(s):  
Electrical Conductivity ◽  
Soil Properties ◽  
Precision Agriculture ◽  
Low Cost ◽  
Satellite System ◽  
Mountainous Area ◽  
Soil Electrical Conductivity ◽  
Resistivity Method ◽  
Conductivity Sensor ◽  
Global Navigation Satellite

Abstract.The adoption of apparent soil electrical conductivity (soil ECa) sensors has increased in precision agricultural systems, especially in systems pulled by vehicles. This work developed a portable soil sensor for measuring soil ECa that could be used without vehicles in mountainous areas and small farms. The developed system was based on the electrical resistivity method. The system measured the electrical conductivity by applying a square wave signal at frequencies defined by the user. The acquired data were georeferenced using a low-cost global navigation satellite system (GNSS) receiver. The sensor system was developed using a BeagleBone Black, a low-cost single-board computer. A user interface was developed in C++, and a touch screen with a resolution of 800×480 pixels was used to display the results. This interface performed statistical analysis, and the results were used to guide the user to identify more field locations to be sampled to increase mapping accuracy. The system was tested in a coffee plantation located in a mountainous area and in a sugarcane plantation in Minas Gerais, Brazil. The system worked well in mapping the soil ECa. The apparent soil electrical conductivities measured using frequencies of 10, 20, 30, and 40 Hz were highly correlated. In the sugarcane field that had more variation in soil texture, a greater number of soil properties presented a significant correlation with the soil ECa. Keywords: Electrical conductivity, Geostatistics, Precision agriculture, Soil properties, Soil sensing, Spatial variability.

scholarly journals GNSS Precise Relative Positioning Using A Priori Relative Position in a GNSS Harsh Environment

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1355
Author(s):  
Euiho Kim
Keyword(s):  
Carrier Phase ◽  
Low Cost ◽  
A Priori ◽  
Field Tests ◽  
Satellite System ◽  
Integer Ambiguity ◽  
Double Difference ◽  
Baseline Length ◽  
Harsh Environment ◽  
Relative Positioning

To enable Global Navigation Satellite System (GNSS)-based precise relative positioning, real-time kinematic (RTK) systems have been widely used. However, an RTK system often suffers from a wrong integer ambiguity fix in the GNSS carrier phase measurements and may take a long initialization time over several minutes, particularly when the number of satellites in view is small. To facilitate a reliable GNSS carrier phase-based relative positioning with a small number of satellites in view, this paper introduces a novel GNSS carrier phase-based precise relative positioning method that uses a fixed baseline length as well as heading measurements in the beginning of the operation, which allows the fixing of integer ambiguities with rounding schemes in a short time. The integer rounding scheme developed in this paper is an iterative process that sequentially resolves integer ambiguities, and the sequential order of the integer ambiguity resolution is based on the required averaging epochs that vary for each satellite depending on the geometry between the baseline and the double difference line-of-sight vectors. The required averaging epochs with respect to various baseline lengths and heading measurement uncertainties were analyzed through simulations. Static and dynamic field tests with low cost GNSS receivers confirmed that the positioning accuracy of the proposed method was better than 10 cm and significantly outperformed a conventional RTK solution in a GNSS harsh environment.

scholarly journals Tight Fusion of a Monocular Camera, MEMS-IMU, and Single-Frequency Multi-GNSS RTK for Precise Navigation in GNSS-Challenged Environments

2019 ◽  
Vol 11 (6) ◽  
pp. 610 ◽  
Author(s):  
Tuan Li ◽  
Hongping Zhang ◽  
Zhouzheng Gao ◽  
Xiaoji Niu ◽  
Naser El-sheimy
Keyword(s):  
Kalman Filter ◽  
Low Cost ◽  
Measurement Model ◽  
Satellite System ◽  
Single Frequency ◽  
Precise Position ◽  
Monocular Camera ◽  
Tightly Coupled ◽  
Self Driving Cars ◽  
Mems Imu

Precise position, velocity, and attitude is essential for self-driving cars and unmanned aerial vehicles (UAVs). The integration of global navigation satellite system (GNSS) real-time kinematics (RTK) and inertial measurement units (IMUs) is able to provide high-accuracy navigation solutions in open-sky conditions, but the accuracy will be degraded severely in GNSS-challenged environments, especially integrated with the low-cost microelectromechanical system (MEMS) IMUs. In order to navigate in GNSS-denied environments, the visual–inertial system has been widely adopted due to its complementary characteristics, but it suffers from error accumulation. In this contribution, we tightly integrate the raw measurements from the single-frequency multi-GNSS RTK, MEMS-IMU, and monocular camera through the extended Kalman filter (EKF) to enhance the navigation performance in terms of accuracy, continuity, and availability. The visual measurement model from the well-known multistate constraint Kalman filter (MSCKF) is combined with the double-differenced GNSS measurement model to update the integration filter. A field vehicular experiment was carried out in GNSS-challenged environments to evaluate the performance of the proposed algorithm. Results indicate that both multi-GNSS and vision contribute significantly to the centimeter-level positioning availability in GNSS-challenged environments. Meanwhile, the velocity and attitude accuracy can be greatly improved by using the tightly-coupled multi-GNSS RTK/INS/Vision integration, especially for the yaw angle.

Development of a Variable-Rate Controller for a Low-Cost Precision Planter

2020 ◽  
Vol 36 (2) ◽  
pp. 233-243
Author(s):  
Andre L.de F. Coelho ◽  
Daniel M. de Queiroz ◽  
Domingos S.M. Valente ◽  
Francisco A. C. Pinto
Keyword(s):  
Angular Velocity ◽  
Low Income ◽  
Precision Agriculture ◽  
Smallholder Farmers ◽  
Low Cost ◽  
Field Tests ◽  
Planting Density ◽  
Variable Rate ◽  
Management Zones ◽  
Seed Metering Device

HighlightsA low-cost controller for variable-rate seeding was developed.The controller successfully identified management zones and changed the angular velocity of the seed metering device.The variable-rate controller maintained the actual seeding rate according to the prescribed seeding map.Abstract. The use of machines for variable-rate applications is becoming popular in modern agriculture. Due to the presence of imported and complex components, the acquisition cost of these machines is high for smallholder farmers. Several studies have been carried out using low-cost components in the development of precision agriculture machines to facilitate their adoption in low-income agriculture. Thus, the objective of this work was to develop a variable-rate controller for a low-cost precision planter. The system was developed and installed on a 1-row manual planter with a horizontal perforated disk distributor. A direct-current electric motor was used to drive the seed metering device. The angular velocity of the electric motor was controlled by a BeagleBone Black single-board computer. A program was written in Python 3.6 language, and a graphical user interface was generated by using PyQt5. Field trials were performed with maize seeds using a 28-hole disk and a prescription seeding map with four management zones. The row spacing was 0.75 m, and the planter ground speed was close to 1.0 m s-1. Field tests showed that the controller was effective at identifying the four management zones and controlling the angular velocity of the motor. By counting the number of plants germinated in the field test, it was verified that the variation in the angular velocity of the motor produced a change in the planting density. At each management zone, the planting density corresponded to the prescribed seeding map. The total cost of the parts used to assemble the controller was US$337.97, characterizing it as low cost. Successful field tests showed the potential for using low-cost components to develop variable-rate machines for smallholder farmers. Keywords: Low-income agriculture, Management zones, Precision agriculture, Single-board computer, Smallholder farmers.

scholarly journals ADS-B Crowd-Sensor Network and Two-Step Kalman Filter for GNSS and ADS-B Cyber-Attack Detection

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4992
Author(s):  
Mauro Leonardi ◽  
Gheorghe Sirbu
Keyword(s):  
Kalman Filter ◽  
Sensor Network ◽  
Traffic Control ◽  
Low Cost ◽  
Real Data ◽  
Satellite System ◽  
Attack Detection ◽  
Probability Of Detection ◽  
Cyber Attack ◽  
Probability Of False Alarm

Automatic Dependent Surveillance-Broadcast is an Air Traffic Control system in which aircraft transmit their own information (identity, position, velocity, etc.) to ground sensors for surveillance purposes. This system has many advantages compared to the classical surveillance radars: easy and low-cost implementation, high accuracy of data, and low renewal time, but also limitations: dependency on the Global Navigation Satellite System, a simple unencrypted and unauthenticated protocol. For these reasons, the system is exposed to attacks like jamming/spoofing of the on-board GNSS receiver or false ADS-B messages’ injection. After a mathematical model derivation of different types of attacks, we propose the use of a crowd sensor network capable of estimating the Time Difference Of Arrival of the ADS-B messages together with a two-step Kalman filter to detect these attacks (on-board GNSS/ADS-B tampering, false ADS-B message injection, GNSS Spoofing/Jamming). Tests with real data and simulations showed that the algorithm can detect all these attacks with a very high probability of detection and low probability of false alarm.

Driving in the Dust: Challenges and Lessons Learned from Field Testing in Degraded Visual Environments

2020 ◽  
Vol 64 (1) ◽  
pp. 1916-1920
Author(s):  
Kayla L. Riegner ◽  
Kelly S. Steelman
Keyword(s):  
Research Program ◽  
Field Tests ◽  
Field Testing ◽  
Driving Performance ◽  
Lessons Learned ◽  
Current Paper ◽  
Ground Vehicle ◽  
Significant Safety ◽  
Dust Clouds

Degraded visual environments (DVEs) pose significant safety and efficiency problems in military ground vehicle operations. As part of a larger research program, two field tests were conducted to evaluate driving aids while indirect driving in DVEs. The current paper presents the results of one of these field tests, and focuses on the challenges and lessons learned in designing a challenging test course and producing consistent dust clouds for assessing Soldier driving performance and workload in degraded visual environments.

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