Research on Improved RAIM Algorithm Based on Parity Vector Method
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It's a realistic difficult problem about how to improve the positioning accuracy and the reliability of RAIM algorithms, on the situation that there are not enough available satellites. This dissertation puts forward RAIM algorithms with the elevation information in the areas as marine navigation. Through this method, the information redundancy can be increased. With only 3 satellites, we can realize positioning calculating. With 5 satellites, we can identify one-faulty satellite. With 6 satellites, we can identify simultaneous two-faulty satellites. According to the simulation analysis, this method can effectively improve the availability of fault detection of RAIM algorithms for one-faulty satellite and two-faulty satellites. In practice, it has profound guiding significance for the improvement of the positioning accuracy and the reliability of the receiver.In integrated navigation system, several navigation systems work follow certain rules and errors can hardly be avoided in one of the systems. A great error will be caused by using the wrong information to calculate the position of carrier. This article takes GPS for an example, an Autonomous Integrity Monitoring method of satellite navigation receiver was put forward in integrated navigation system. It not needs to send special information. This is a simple and feasible method.
Navigation System
Radio navigation
Dead reckoning
Area navigation
Mobile Robot Navigation
Air navigation
GNSS augmentation
Wind triangle
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This paper deals with the tight integration of Inertial Measurement Units (IMU), Global Navigation Satellite Systems (GNSS) and the concept of Advanced Receiver Autonomous Integrity Monitoring (ARAIM). While the integration of an IMU and GNSS to an integrated GNSS/Inertial system is well known and widespread, the use of the ARAIM concept in inertial systems is a new and promising approach. In safety critical applications such as aviation, GNSS receivers as well as integrated GNSS/Inertial systems have to be equipped with a Fault Detection and Exclusion (FDE) function. Single frequency L1 GPS receivers with Receiver Autonomous Integrity Monitoring (RAIM) were the answer for decades. The second generation of GNSS offers more satellite systems and more frequencies for navigation. The visibility and accuracy of Multi-Frequency and Multi- Constellation (MFMC) receivers are significantly improved. ARAIM transfers these improvements into aviation. MFMC receivers with ARAIM can provide protection levels for challenging Alert Limits, for instance VAL = 35 m, with reasonable availability. Therefore, ARAIM has the potential for LPV-200 (Localizer performance with vertical guidance, decision height 200 feet) approaches. By including an IMU, it is possible to increase this potential. The added value of using an IMU and the difficulties of integration are hardly mentioned in literature. ARAIM ensures integrity by comparing the GNSS position solution with all satellites in view to solutions of subsets (fault-tolerant solutions) that exclude certain satellites. A transfer of this concept into a tightly integrated GNSS/Inertial system seems straightforward - replace the GNSS position solutions and subsets by integrated GNSS/Inertial position and subsets. On the other hand, ARAIM needs to evaluate hundreds of subsets, which creates a considerable computational load, especially in the case of GNSS/Inertial integration. A challenge for GNSS/Inertial designs is the ARAIM specific ranging model, which includes ranging bias, accuracy and integrity. Carrier smoothed ranges are used in ARAIM. These smoothed signals contradict an optimal GNSS/Inertial integration. In addition, the integration design has to consider time correlations of ranging signals, neglectable for ARAIM. In this paper, we address the mentioned design issues of a tight GNSS/Inertial integration, which uses the concept of ARAIM. We also describe our simulation procedure to determine availability. The availability serves as performance measure for the integration designs. Benefits and effects of reducing the number of subsets, interpretation and implementation of ranging model, correlation time constants, as well as different IMU classes are investigated by means of simulation results.
GNSS augmentation
Air navigation
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Advanced Receiver Autonomous Integrity Monitoring (Advanced RAIM, ARAIM) is the next generation of RAIM which is widely used in air transport. Based on tight integration of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS), the GNSS/INS ARAIM attracts wide attention for its high performance with no additional cost. However, both of GNSS alone ARAIM and GNSS/INS ARAIM have to compute fault-tolerant position solutions of a large number of subsets, which results in a huge computational load. In this paper, a no-subset method for GNSS/INS ARAIM is proposed to conduct the fault detection test and to obtain the integrity and accuracy performances by computing a tight upper bound of the fault detection statistics and a tight bound of standard deviations of position errors, instead of the fault detection statistics and standard deviation of each subset. The computational load of the proposed algorithm is about 1% of that of the current GNSS/INS ARAIM, which facilitates its application in the airborne navigation system. Moreover, the simulation results show that the integrity and accuracy performances of the proposed GNSS/INS ARAIM are able to meet the corresponding requirements of CATI with global coverage.
Galileo (satellite navigation)
GNSS augmentation
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This paper describes the design of INS Aided Receiver Integrity Monitoring (ARIM) algorithm for GNSS integrated navigation systems. Inertial navigation systems typically use navigation grade accelerometers and gyroscopes for high reliability applications such as launch vehicles. Usage of GNSS receivers along with low cost IMUs with their measurements instances synchronized provides feasibility of realizing low-cost integrated navigation systems without compromising in performance or reliability. While the usage of GNSS receiver helps to overcome the inaccuracies caused by low cost IMU, the output of the inertial navigation system can also be used for validating the measurements taken by the GNSS receiver especially during spoofing scenarios. Validation of pseudorange measurements in this manner gives computational advantage over the conventional RAIM (Receiver Autonomous Integrity Monitoring) algorithm. The test results are a good indication of the usefulness of the algorithm in different scenarios where measurement errors are possible. In summary, ARIM algorithm presented in this paper provides an effective scheme for validating GNSS pseudorange and deltarange measurements under different possible error scenarios including spoofing and improves the overall reliability of the GNSS integrated navigation system.
Pseudorange
GNSS augmentation
Spoofing attack
Navigation System
Air navigation
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The 13 papers int his issue fall into three main areas, namely aided Global Navigation Satellite Systems (GNSS) positioning, non-GNSS geolocalization technologies, and robust algorithms for filtering and positioning in severe environments.
GNSS augmentation
Precise Point Positioning
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Citations (2)
GNSS augmentation
Galileo (satellite navigation)
SIGNAL (programming language)
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Citations (3)
With the rapid development of the GNSS(Global Navigation Satellite System),the interoperability of multi-systems is widely focused in recent years.It's expressed that the development of GNSS,then took the comparison between the sole navigation satellite system(BDS)and multi-system as an example,making the simulation of the navigation and position performance in our homeland with the two indices:observed satellite number and DOP.The result of the simulation shows that the performance of the navigation and positioning is distinctly improved by multi-system interoperability.
Navigation System
Area navigation
Position (finance)
Air navigation
Precise Point Positioning
GNSS augmentation
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Positioning, timing and navigation (PNT) with Satellite Navigation Systems like GPS, GLONASS, Beidou and Galileo are more and more widespread and used in very different types of applications including core telemetry applications.Global Navigation Satellite Systems (GNSS) receivers in Safety Critical Applications such as landing airplanes, Critical Infrastructure or Remote Sensing missions e.g.Earth Observing Satellites need to be very robust and reliable under a variety of environmental conditions, sometimes very harsh ones outside the atmosphere.Timing is critical in communication between satellite ground stations and the satellites, e.g.telemetry tracking and command, or communication protocols using Time Division Multiple Access.Thus, it is crucial to test GNSS receivers thoroughly under a variety of different conditions, also extreme conditions like very high dynamics or unhealthy GNSS satellites.For this purpose GNSS RF signal simulators are a versatile and flexible tool.The tests can be repeated as many times as necessary with identical conditions.Besides this a simulator offers complete control across the scenario, every single detail can be controlled and changed.In RF simulators comprehensive error models are available for satellite signals and clocks, satellite orbits and health flags, obscuration and multipath, atmospheric conditions, antenna characteristics, vehicle dynamics, leap seconds, jamming and aiding Inertial sensors.Each of them can be controlled individually.For some tests, like unhealthy satellites or future constellations RF, simulators are the only way for testing, as future signals, for example, are not available in live sky test.In this paper, we will introduce the capabilities of GNSS simulators with a wide range of different error conditions and show several telemetry use cases.
GNSS augmentation
GLONASS
Galileo (satellite navigation)
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Barometer
GNSS augmentation
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This paper gives an overview on communications system based and global navigation satellite system (GNSS) based navigation. Starting from the physical layer model of a mobile communications or navigation receiver, we highlight similarities and differences between the two receivers. We explain navigation principles for timing measurements and present the possible navigation accuracies of different communications systems and GNSSs. Finally, we examine synergies of combined receivers and systems.
GNSS augmentation
Air navigation
Radio navigation
Navigation System
Communications satellite
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Citations (8)