Universal multifunctional autonomous navigation system of radar operating principle

Precise timing and precise location information are provided by Global Navigation Satellite Systems (GNSS) and play a crucial role in the positioning, navigation and data acquisition of most Unmanned Aerial Vehicles (UAV). GNSS functions include the following applications in UAVs: time-stamping and geo-referencing of collected data and images, synchronization of swarm flying and follow-me flights, determination of position and attitude in-flight, flight trajectory by following a pre-defined number of waypoints, mission planning, return home automatically without external control, avoidance of obstacles and geo-fencing. 
 Some of these critical operations have implications for the safety of the UAV, the surrounding environment and health and safety of people, for example UAVs threatening to bring down aircrafts at airports, which are no-fly zones for UAVs. The appropriate GNSS based function to avoid this is geo-fencing. Another example is obstacle avoidance to prevent collisions and damages both for the UAV and the obstacle, e.g. anything from a window pane, tree, human being, to a power line.
 In order to ensure health and safety it is thus important to ensure correct function of the navigation and the timing, under a wide variety of circumstances, and in different signal environments. There can be signal disturbances, such as obscurations by buildings or reflected GNSS signals, called multipath. The performance of timing and navigation based on GPS/GNSS can be tested and verified in a controlled and repeatable way in the laboratory with different types of test equipment. We will give an introduction to a wide range of potential threats to GNSS Positioning, navigation and timing and an overview of different test methods. In addition, we are presenting a method for time synchronization of drones to enable safe swarm and follow flights in UAVs.

As part of our recent research to assess the potential of low-cost navigation sensors for Unmanned Aerial Vehicle (UAV) applications, we investigated the potential of carrier-phase Global Navigation Satellite System (GNSS) for attitude determination and control of small size UAVs. Recursive optimal estimation algorithms were developed for combining multiple attitude measurements obtained from different observation points (i.e., antenna locations), and their efficiencies were tested in various dynamic conditions. The proposed algorithms converged rapidly and produced the required output even during high dynamics manoeuvres. Results of theoretical performance analysis and simulation activities are presented in this paper, with emphasis on the advantages of the GNSS interferometric approach in UAV applications (i.e., low cost, high data-rate, low volume/weight, low signal processing requirements, etc.). The simulation activities focussed on the AEROSONDE UAV platform and considered the possible augmentation provided by interferometric GNSS techniques to a low-cost and low-weight/volume integrated navigation system (presented in the first part of this series) which employed a Vision-Based Navigation (VBN) system, a Micro- Electro-Mechanical Sensor (MEMS) based Inertial Measurement Unit (IMU) and code-range GNSS (i.e., GPS and GALILEO) for position and velocity computations. The integrated VBN-IMU-GNSS (VIG) system was augmented using the inteferometric GNSS Attitude Determination (GAD)sensor data and a comparison of the performance achieved with the VIG and VIG/GAD integrated Navigation and Guidance Systems (NGS) is presented in this paper. Finally, the data provided by these NGS are used to optimise the design of a hybrid controller employing Fuzzy Logic and Proportional-Integral-Derivative (PID) techniques for the AEROSONDE UAV.

Drones are becoming increasingly significant for vast applications, such as firefighting, and rescue. While flying in challenging environments, reliable Global Navigation Satellite System (GNSS) measurements cannot be guaranteed all the time, and the Inertial Navigation System (INS) navigation solution will deteriorate dramatically. Although different aiding sensors, such as cameras, are proposed to reduce the effect of these drift errors, the positioning accuracy by using these techniques is still affected by some challenges, such as the lack of the observed features, inconsistent matches, illumination, and environmental conditions. This paper presents an integrated navigation system for Unmanned Aerial Vehicles (UAVs) in GNSS denied environments based on a Radar Odometry (RO) and an enhanced Visual Odometry (VO) to handle such challenges since the radar is immune against these issues. The estimated forward velocities of a vehicle from both the RO and the enhanced VO are fused with the Inertial Measurement Unit (IMU), barometer, and magnetometer measurements via an Extended Kalman Filter (EKF) to enhance the navigation accuracy during GNSS signal outages. The RO and VO are integrated into one integrated system to help overcome their limitations, since the RO measurements are affected while flying over non-flat terrain. Therefore, the integration of the VO is important in such scenarios. The experimental results demonstrate the proposed system’s ability to significantly enhance the 3D positioning accuracy during the GNSS signal outage.

A new integrated navigation system designed for small size Unmanned Aerial Vehicles (UAVs) is presented. The proposed system is based on a number of low-cost avionics sensors, including Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Sensors (VBS). The use of an Aircraft Dynamics Models (ADMs) to provide additional information to compensate for the shortcomings of Vision Based Navigation (VBN) and MEMS-IMU sensors in high-dynamics attitude determination tasks is also considered. Additionally, the research concentrates on the potential of carrier-phase GNSS for Attitude Determination (GAD) using interferometric techniques. The main objective is to design a compact, light and relatively inexpensive system capable of providing the required navigation performance (position and attitude data) in all phases of flight of small UAVs, with a special focus on precision approach and landing, where VBN techniques can be fully exploited in a multi-sensor data fusion architecture. An Extended Kalman Filter (EKF) is developed to integrate the information provided by the different sensors and to provide estimates of position, velocity and attitude of the UAV platform in real-time. Three different integrated navigation system architectures are implemented. The first architecture uses VBN at 20 Hz and GNSS at 1 Hz to augment the MEMS-IMU running at 100 Hz. The second mode also includes the ADM (computations performed at 100 Hz) to provide augmentation of the attitude channel. The third fusion architecture uses GNSS based attitude values. The simulations are carried out on the AEROSONDE UAV performing high-dynamics manoeuvres repre-sentative of the UAV operational flight envelope. Simulation of the VBN-IMU-GNSS (VIG) integrated navigation system shows that the system can attain position, velocity and attitude accuracies complying with Category Two (CAT II) precision approach requirements. Simulation of the VBN-IMU-GNSS-ADM (VIGA) system also shows promising results, since the achieved attitude accuracy is higher using the ADM-VBN-IMU than using VBN-IMU only. However, due to rapid divergence of the ADM virtual sensor, there is a need for frequent re-initialisation of the ADM data module, which is strongly dependent on the UAV flight dynamics and the specific manoeuvring transitions performed. In the simulation of the third integrated navigation system, the VIG system is augmented by employing the GAD, forming the VIG-GAD (VIGGA) system architecture. The performances achieved with the VIG, VIGA and VIGGA integrated Navigation and Guidance System (NGS) are presented and are in line with the International Civil Aviation Organization (ICAO) precision approach requirements.

Global Navigation Satellite Systems (GNSS) has been the primary positioning solution for Unmanned Aerial Vehicles (UAVs) due to their worldwide coverage, high precision and lightweight receivers. However, GNSS is prone to electromagnetic interference and malicious assaults, including jamming or spoofing because of its low signal-to-noise ratio (SNR). To ensure the continuity and protection of UAV operations, using redundant navigation systems is essential. In recent years, the phased array radio system (PARS) has established itself as a local navigation solution. PARS is robust towards malicious assaults because of an much higher SNR than GNSS regarding directed and encrypted transmission. An essential factor of PARS is that the orientation of the radio antenna at a ground station needs to be precisely determined to obtain the correct positioning of UAVs. This paper presents a method for extending a previously proposed calibration algorithm to estimate the ground antenna orientation with an inertial navigation system (INS) aided by redundant positioning sensors (GNSS, PARS or barometer) using a multiplicative extended Kalman filter (MEKF) so that the calibration can be activated during flights whenever GNSS is available. In other words, the proposed navigation system is essentially an aided-INS which switches between two modes depending on the availability of GNSS: calibration and GNSS aiding mode when GNSS is available (Mode 1) and PARS and barometer aiding mode when GNSS is unavailable (Mode 2). Considering that the navigation system needs to include the effect of Earth’s curvature for a long-distance flight, PARS horizontal measurement and the barometer measurement were treated independently, and the navigation equations were propagated in Earth Centred Earth Fixed (ECEF) frame. The independent treatment of barometer measurement, and the propagation in ECEF frame were also beneficial when using multiple ground antennas to have a common reference point and reference frame. The proposed method was validated using data (Inertial Measurement Unit (IMU), GNSS, PARS, Pixhawk autopilot (including barometer) measurements) collected during a field test. In the validation, GNSS was made available at the middle of the flight and the calibration mode was activated for 200s. The proposed navigation system successfully estimated the precise orientation of multiple ground antennas and the navigation solutions were verified using GNSS and Pixhawk autopilot solutions as ground truth.

Most autonomous vehicle navigation systems rely on Global Navigation Satellite System (GNSS) as a primary positioning sensor. However, a standalone GNSS receiver may not be able to meet the required positioning performance in aspects of position accuracy, robustness against signal blockages or signal reflections, and position output rates. Therefore, an integrated navigation system with an Inertial Measurement Unit (IMU) and GNSS receiver is often used to provide a continuous, reliable, high-bandwidth navigation solution. In general, Kalman filter is often used to integrate IMU measurements and GNSS output with predefined characteristic models under stochastic assumptions. If there is a discrepancy between the predefined model and actual sensor behavior or when proper correction on IMU measurements is not possible due to GNSS outages, the positioning performance of the filter will be degraded. For the application having a stringent positioning performance such as autonomous vehicle navigation, a more sophisticated sensor model or methods should be employed. A Recurrent Neural Network (RNN) – Long Short-Term Memory (LSTM) network has been found to have strengths in making a prediction with time-series data and has been applied for various navigation problems from the prior research. Therefore, in this paper, to reflect the non-linearity and time-correlated characteristics of the actual IMU sensor measurement, an RNN-LSTM network is employed for the GNSS-IMU integration. The paper investigates RNN-LSTM based GNSS-IMU integration methods through simulation and experiment and the results showed that the proposed RNN-LSTM network can provide relatively stable positioning performance even in a situation where a GNSS signal can not be received for 200 seconds.

This paper describes an ultra-tight integration of a Global Navigation Satellite System (GNSS) receiver and an Inertial Navigation System (INS) for small Unmanned Aerial Vehicles (UAVs). The system is based on a low-cost and low-weight GNSS Intermediate Frequency (IF) sampler which has been fitted onto a small UAV. The storage of IF samples together with measurements from an Inertial Measurement Unit (IMU) has allowed the authors to process an Ultra-Tightly Coupled (U.T.C.) GNSS/INS solution from real data collected with a small UAV. The focus of this paper has been to investigate the potential benefits in signal tracking during flights below dense foliage and tree canopies by using the developed U.T.C. system. The obtained results have been compared to a scalar GNSS receiver and a high-grade commercial reference receiver and have showed significant improvements in tracking capabilities.

The GNSS NavAer project has been under development since 2017, focusing on creating a comprehensive infrastructure dedicated to monitoring and analyzing space weather phenomena. This project represents a significant investment in scientific research and technology, aimed at enhancing our understanding of various atmospheric and environmental processes. The infrastructure established through the GNSS NavAer project is versatile and can be deployed across several scientific disciplines, including Geodesy, Aeronomy, and Space Weather, among others.One of the primary motivations behind the GNSS NavAer project is the increasing reliance on Global Navigation Satellite Systems (GNSS) for navigation, communication, and positioning applications. As these systems become more integral to modern society, understanding the factors that can disrupt their performance is of great insterest. Space weather events, particularly those affecting the ionosphere, have been identified as significant sources of interference that can degrade the accuracy and reliability of GNSS signals.Ionospheric scintillation, a phenomenon characterized by rapid amplitude and phase fluctuations of GNSS signals, poses a particular challenge. This can result from natural irregularities in the ionosphere, often intensified by solar activity. When these irregularities are present, GNSS signals can experience disruptions that may lead to positioning errors, loss of signal lock, and degraded navigation accuracy. Such disruptions can have serious implications for air navigation, autonomous systems, and any application that relies on precise positioning data.In this presentation, we will focus on the specific use of the GNSS NavAer infrastructure for monitoring space weather, with an emphasis on IS. The infrastructure includes a network of strategically placed GNSS receivers that continuously collect real-time data on ionospheric conditions, which are stored in a databank and can be explored via a dedicated toll specially developed for that (ISMR Query Tool). By integrating advanced data processing techniques and algorithms, researchers can analyze these data to identify and characterize scintillation events as they occur.The significance of this monitoring cannot be overstated. By providing timely information on ionospheric conditions, stakeholders can better prepare for and mitigate the impacts of space weather on GNSS applications. For instance, real-time observations can help aviation authorities make informed decisions regarding flight operations, ensuring that aircraft can navigate safely even in conditions that would typically be challenging due to ionospheric disturbances.Furthermore, the GNSS NavAer project facilitates collaborative efforts among scientists and researchers in various fields. By sharing data and findings, the project promotes a more comprehensive understanding of space weather and its impacts. International cooperation is vital, as space weather events often affect multiple regions simultaneously, making a collaborative approach essential for effective monitoring and response strategies.In conclusion, the GNSS NavAer project is a groundbreaking initiative aimed at enhancing our ability to monitor and understand the complexities of space weather. Through its dedicated infrastructure, it addresses the challenges posed by IS and aims to provide solutions that improve the reliability of GNSS applications. The deterioration in positioning and navigation will be presented, together to some trial to reduce such problem.

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.

Over the few years civilian and military applications are maneuvering with multi-sensor Unmanned Aerial Vehicles (UAV). The aim of this paper is to improve autonomous UAV positioning by using Inertial measurement unit (IMU) and Global Navigation Satellite System (GNSS) is considered. The positional accuracy of UAV is limited to GNSS signal blockage. The advancements in micro electro mechanical systems (MEMS) helps to design low-cost IMU with meter-level accuracy in positioning. The estimation of attitude, position and velocity measurements are determined from a nine degree of freedom Arduino embedded IMU sensor. The Low-cost IMU sensor has been tested under static condition and then a dynamic movement of sensor is observed under moving base-line conditions on February 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> 2020 at Koneru Lakshmaiah Education Foundation (KLEF). In this paper, covariance filters are implemented to eliminate the gyro bias and accelerometer bias for improving attitude determination of IMU navigation system. The positioning from IMU sensor is derived using standard strap-down inertial navigation system (SINS) formulation. The position error from SINS indicates that IMU sensor is able to perform well with maximum position error of 5 meters and 2.5 meters for static and dynamic environments respectively. The outcome of this work would be useful for integration of navigation sensors using kalman filter based advanced techniques for UAV applications.

Currently Global Navigation Satellite Systems (GNSS) are the primary source for the determination of absolute position, velocity and time (PVT) for merchant vessel navigation. On a ship bridge a number of systems like Electronic Chart and Information Systems (ECDIS), automatic track control systems and the Automatic Identification System (AIS) are relying on that PVT provision. Nevertheless it is well known that GNSS performance can strongly degrade due to space weather events, jamming and spoofing. Especially the availability and usage of low cost jammers, so called personal privacy devices (PPD), lead to the question on how a continuous onboard provision of PVT data can be realized in the presence of such a PPD jammer. Generally three possible solutions for that challenge are contemplated: i) the usage of a terrestrial backup system, ii) a jamming resistant GNSS receiver or iii) the integration of GNSS with other onboard self-contained navigation sensors like, e.g. speed log, gyrocompass and inertial sensors (Inertial Measurement Unit - IMU). The paper will focus on option (iii) and addresses the question whether it is possible to bridge real maritime GNSS Jamming attacks by a Kalman Filter (KF) using GNSS, inertial and Doppler velocity log (DVL). The main challenge for the KF here is, that a free inertial integration needs to be performed after a time period with already degraded GNSS signal reception. As KF are known to be optimum estimators only under the assumption of known statistics of the measurements, the question arises, whether the pseudoranges under the influence of jamming still follow a certain statistics. In order answer this question an additional lab experiment was performed and analyzed. Concretely the sensor fusion was realized by using both loosely and tightly coupled architectures which have been implemented using an Unscented Kalman Filter (UKF). The fusion algorithm is formulated in ECEF coordinate frame with quaternion attitude parameterization, where the heading observability is addressed by fusing the baseline measurements from the GNSS compass. In order to evaluate the performance of the proposed sensor fusion scheme under the influence of a real maritime jamming attack we performed an experiment in our civilian maritime jamming testbed in the Baltic, which has been established together with the German Federal Network Agency approximately 6 nmi north of the peninsula Dars. Here a PPD jammer was mounted on the monkey deck of an anchored vessel. A test vessel was maneuvering around the jammer. As a test vessel the multipurpose research and diving vessel Baltic Diver II (length 29 m, beam 6.7 m and draught 2.8 m) was used, which was equipped with three dual frequency GNSS receivers, a fiber-optic gyroscope IMU and a Doppler velocity log. The obtained results confirm that real maritime jamming attacks using PPD Jammers can be bridged by the proposed tightly coupled GNSS-IMU-DVL integration scheme resulting in horizontal position errors of less than 10m only.

Global Navigation Satellite Systems (GNSS) rely on one-way signal travel time measurements from satellites to receivers. To ensure accurate ranging, GNSS must estimate and compensate for clock offsets on at least one end of the transmitter-receiver system. Consequently, clock offsets are highly correlated with GNSS-derived parameters such as station coordinates, tropospheric delay estimates, satellite radial orbit parameters, and apparent geocenter coordinates. Improper handling of clock offsets can lead to their absorption into these parameters, degrading geodetic product accuracy. Traditionally, clocks have been handled within individual techniques without being specially addressed. However, some research efforts and experimental trials have explored using VLBI to synchronize clocks for GNSS and refining clock models to reduce correlations with other estimated parameters in GNSS, thereby improving overall precision.With recent advancements, high-precision optical clocks and fiber-optic time transfer technologies provide new possibilities for enhancing clock synchronization in GNSS. Optical fiber offers a stable, interference-free medium for time and frequency transfer, eliminating atmospheric effects such as ionospheric and tropospheric delays that impact GNSS-based synchronization. Moreover, fiber-optic time transfer is inherently two-way, removing the need for external clock offset estimation. Thus, fiber-optic clock synchronization represents the most precise timing technique currently available. Establishing a common clock by connecting multiple GNSS receivers via optical fiber could significantly reduce the number of estimated clock parameters in GNSS solutions, leading to improved geodetic measurement precision and a more accurate geodetic reference frame.Despite its potential, the approach of implementing a common clock with optical fiber for GNSS receivers remains largely untested. Although optical fiber links have been successfully used in clock synchronization, their integration into GNSS receiver clocking is still in an experimental stage. Notable existing efforts include: (1) the Geodetic Observatory Wettzell, where multiple receivers are connected to a single clock via optical fiber, representing a local baseline setup, and (2) the GFZ experiment, where a receiver is linked to the ultra-stable clock of PTB, representing a regional baseline approach.This study characterizes existing fiber-optic connections among GNSS receivers to identify practical challenges and evaluate their impact on GNSS parameter estimation. We process data from receivers sharing a common clock using Bernese GNSS Software. Initially, PPP will be performed to compare GNSS-derived clock parameters across receivers sharing a common clock, verifying the effectiveness of fiber-optic clock synchronization. Following this validation, we will leverage GNSS-derived clock parameters to evaluate inter-receiver clock differences measured via optical fiber. Subsequently, we will implement a strategy in which only one receiver&amp;#8217;s clock parameters are estimated, using fiber-measured clock differences as a priori constraints for other receivers in the PPP solution. This approach reduces the number of estimated clock parameters and serves as a preliminary test of the feasibility of a GNSS common clock framework.Future developments will focus on refining our analysis framework to enable simultaneous estimation of GNSS receivers operating under a common clock. This research contributes to the broader goal of integrating ultra-stable clocks into global GNSS networks, ultimately enhancing the stability of geodetic reference frames and improving GNSS-derived geodetic products.

In the recent years, the threat of radio frequency interference (RFI) to Global Navigation Satellite Systems (GNSS) receivers has been increasing due to an escalating number of RFI related GNSS issues, and enhanced complexity of jammers and spoofers. This poses significant integrity threats to various systems that depend on GNSS measurements for navigation and guidance. One of the most versatile methods to detect such instances of spoofing is the use of Automatic Gain Control (AGC) measurements provided by GNSS receivers. AGC is indicative of the change in thermal noise levels observed by the frontend of the receiver, thus it can be used to detect presence of RFI. If AGC is coupled with the carrier to noise ratio density (??/????) values of visible GNSS satellites, it is possible to gain insight into which specific constellations or satellites may have been spoofed. Although AGC has been available in some commercial GNSS receivers and customized frontends for some time, the introduction of raw GNSS measurements in Android 7 has recently allowed it to be accessible in some smartphones as well. This study looks at AGC measurements from multiple smartphone models which have different GNSS chipsets, assesses their behavior under RFI, points out the current limitations, and proposes future improvements that would assist in its usage as a GNSS RFI indicator. Furthermore, the paper discusses the future of AGC measurements that would assist in broadening their applications to all GNSS receivers.

This study evaluates the suitability of compact, lowcost Global Navigation Satellite System (GNSS) receiver module in estimating the effects of tropospheric delay on accuracy of GNSS position solution at GNSS laboratory, the University of Burdwan, West Bengal, India with geographical coordinate of 23.2545°N, 87.8467°E, −11.402 m, using compact, low-cost uBlox F9P GNSS module and high cost Leica GR 50 geodetic receiver in March 2023. GNSS observation data were logged by the two receivers for 24 hours at 1Hz dual-frequency and converted into Rinex observation files in GPS-only, GLONASS-only, and GPS+GLONASS hybrid modes. The converted Rinex observation data were uploaded to the Natural Resources of Canada (NRCan) online service to obtain the user fixed values of latitude, longitude and attitude, and the wet and dry tropospheric delay files via email. The converted Rinex observation files were post-processed using GNSS positioning analysis tool that estimate the GNSS positioning accuracy parameters namely Distance Root Mean Square (2DRMS), Circle of Error Probable (CEP), Spherical Error Probable (SEP), and Mean Radian Spherical Error (MRSE) from the users defined known fixed values of latitude, longitude and altitude. The total delay obtained from the two GNSS receivers were compared with their positioning accuracy parameters. The results showed better position solution on observation days with the least total zenith delay in all the constellations considered. The Leica GR 50 shows an average total zenith delay of 2.476 m in GPS mode, 2.486 m in GLONASS mode and 2.4680 m in GPS+GLONASS hybrid mode while the uBlox F9P shows 2.477 m in GPS mode, 2.494 m in GLONASS mode and 2.4684 m in GPS+GLONASS hybrid mode of operation. The GPS+GLONASS hybrid mode of operation reduces the total zenith delay and enhanced the position solution. Hence, there is no distinct difference between the two GNSS receivers, suggesting that compact, low-cost, GNSS receiver modules can be used for tropospheric study.

In this paper, we propose a systematic framework for the autonomous navigation system based on distributed filtering for an Unmanned Aerial Vehicle (UAV). The proposed framework consists of the design and algorithm of the autonomous navigation. Therein, the camera mounted on the UAV functions as a navigation sensor targeted for navigation and positioning. In order to reduce the computational complexity and exclude the risk caused by Global Positioning System (GPS) outage, an autonomous navigation system based on distributed filtering is designed and realized. When GPS is available by monitoring the GPS integrity, sensor information from Strapdown Inertial Navigation System (SINS) and GPS is fused using a 7-state Conventional Kalman Filter (CKF) to estimate the full UAV state (position, velocity and attitude); when GPS is unavailable, sensor information from gyroscopes, accelerometers and magnetometer is fused using a 4-state Extended Kalman Filter (EKF) to estimate the attitude and heading of the UAV, and sensor information from SINS and vision positioning system is fused using a 7-state Incoordinate Interval Kalman Filter (IIKF) to estimate the position and velocity of the UAV. In addition, the second-order vertical channel damping loop is adopted to fuse measurements from a barometer with those of SINS, which suppresses the divergence of the vertical channel error and makes the altitude information calculated by SINS trustable. Both ground and flight experiments of the autonomous navigation system have been carried out. The test results show that the system can provide stabilized attitude information in long durations, and can realize the automatic flight control of UAV.