Fast Satellite Selection Algorithm Research Articles

A fast satellite selection algorithm for positioning in LEO constellation

In the near future, a much larger scale of low earth orbit (LEO) satellites will be deployed, and satellite selection is an effective way to overcome challenges to processing capability and channel limitation of receivers caused by superabundant satellites in view. However, existing algorithms do not take into account the fact that a large-scale LEO constellation satellite selection subset can be closer to the theoretical optimal configuration than the traditional brute force search and recursive algorithm computational overhead is too large for end devices. In this study, the solution of the minimum optimization problem of the GDOP (Geometric Dilution of Precision) under the condition of limited elevation angle is first analyzed to determine the optimal ratio configuration scheme of top and bottom satellites under the optimal geometric satellite positioning problem. Subsequently, a fast satellite selection algorithm is proposed, which selects a subset of satellites that meet the different numbers of observation satellites and is close to the optimal geometric configuration by geometric method. The comparative simulation shows that, in the 1586 LEO constellation system, the proposed algorithm has an average GDOP of 1.52 when the satellite subset size is 10 and 0.95 when the subset size is 30. Compared with the recursive, the Rotating Partition algorithm and the Quasi-optimal algorithm, the proposed algorithm achieves better accuracy when its size is not close to the total number of observable satellites.

Fast satellite selection algorithm for GNSS multi-system based on Sherman–Morrison formula

Efficient use of global navigation satellite system (GNSS) observations improves when applying rational satellite selection algorithms. By combining the Sherman–Morrison formula and singular value decomposition, a smaller-GDOP (geometric dilution of precision)-value method is proven for an increasing number of visible satellites. By combining this smaller-GDOP-value method with the maximum-volume-tetrahedron method, a new rapid satellite selection algorithm based on the Sherman–Morrison formula for GNSS multi-systems is proposed. The basic idea of the algorithm is as follows: First, the maximum-volume-tetrahedron method is used to obtain four initial visible satellites. Then, the other visible satellites are selected by using the smaller-GDOP-value method to reduce the GDOP value and improve the accuracy of the overall algorithm. When the number of included satellites reaches a certain value, the rate of GDOP decrease tends to approach zero. Considering the algorithm precision and the computation efficiency, reasonable thresholds and end of calculation condition equation are given, which can make the proposed algorithm autonomous. The reasonable thresholds and the end of calculation parameters are suggested by means of experiments. Under the thresholds and the end of calculation parameters, the algorithm has an adaptive functionality. Furthermore, the GDOP values of the algorithm are less than 2, indicating that this algorithm can meet one of the requirements of high-precision navigation. Moreover, compared with the computation complexity values of the optimal GDOP estimation method, which includes all visible satellites, the values of the new algorithm are about half, indicating that this algorithm has a rapid performance. These findings verify that the proposed satellite selection algorithm based on the Sherman–Morrison formula provides autonomous functionality, high-performance computing, and high-accuracy results.

A fast GNSS satellite selection algorithm for continuous real-time positioning

A fast GNSS satellite selection algorithm for continuous real-time positioning

A fast satellite selection algorithm for multi-GNSS marine positioning based on improved particle swarm optimisation

This paper introduces an improved particle swarm optimisation algorithm (IPSO), to select satellites rapidly in multi-GNSS marine positioning. The traditional particle swarm optimisation (PSO) may be trapped into local optimisation. To avoid the disadvantage, the proposed algorithm uses linear inertia weight factor and two functions of the immune system, i.e. the memory function and the self-regulatory function. Several experiments are carried out by adopting real survey data collected by the SiNan receiver that is installed on the Snow Dragon scientific research ship during the 9th China Arctic expedition. Compared with the minimum Geometric dilution of precision (GDOP) method, PSO and IPSO significantly reduce the computing time (96.25% and 95.61%). The variance of IPSO is 0.063, which is much lower than that of PSO (0.087). As for the positioning accuracy, the IPSO can reach the centimetre level in the kinematics condition.

Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications

Satellite selection is an effective way to overcome the challenges for the processing capability and channel limitation of the receivers due to superabundant satellites in view. The satellite selection strategies have been widely investigated to construct the subset with high accuracy but deserve further studies when applied to safety-critical applications such as the receiver autonomous integrity monitoring (RAIM) technique. In this study, the impacts of subset size on the accuracy and integrity of the subset and computation load are analyzed at first to confirm the importance of the satellite selection strategy for the RAIM process. Then the integrated performance impact of a single satellite on the current subset is evaluated according to the performance requirement of the flight phase. Subsequently, a performance-requirement-driven fast satellite selection algorithm is proposed based on the impact evaluation to construct a relatively small subset that satisfies the accuracy and integrity requirements. Comparison simulations show that the proposed algorithm can keep similar accuracy and better integrity performances than the geometric algorithm and the downdate algorithm when the subset size is fixed to 12, and can achieve an average 1.0 to 2.0 satellites smaller subset in the Lateral Navigation (LNAV) and approach procedures with vertical guidance (APV-I) horizontal requirement trial. Thus, it is suitable for real-time RAIM applications and low-cost navigation devices.

Research on satellite selection algorithm in ship positioning based on both geometry and geometric dilution of precision contribution

With the networking of four Global Navigation Satellite Systems, the combination of multi-constellation applications has become an inevitable trend, and there will be more and more visible satellites that can be participated in ship positioning. However, the computational complexity increases sharply, which greatly improves the load capacity of the receiver’s data processor and reduces the output frequency of the positioning result. To achieve the balance between positioning accuracy and computational complexity, a new fast satellite selection algorithm based on both of geometry and geometric dilution of precision contribution is proposed. Firstly, this article analyzes the geometry characteristics of the least visible satellites has minimum geometric dilution of precision that meet the positioning requirements and makes clear the layout of their elevation angles and azimuth angles. In addition, it derives the relationship of geometric dilution of precision and the visible satellites layout and gets geometric dilution of precision contribution of each satellite. Finally, based on the observation data of JFNG tracking station of the Multi-Global Navigation Satellite System (GNSS) Experiment trial network, the positioning error and the elapsed time of GPS/Beidou Satellite Navigation System and GPS/Beidou Satellite Navigation System/Russian Global Orbiting Navigation Satellite System (GLOANSS) are compared. Simulation results show that the algorithm solves the problem that there are a lot of matrix multiplications and matrix inversions in the traditional satellite selection algorithm, and the new algorithm can reduce computational complexity and increase receiver processing speed.

A fast satellite selection algorithm with floating high cut-off elevation angle based on ADOP for instantaneous multi-GNSS single-frequency relative positioning

With the Global Navigation Satellite System (GNSS) developing, the single-frequency single-epoch multiple GNSSs (multi-GNSS) relative positioning has become feasible. Since a larger number of the observed satellites make the instantaneous (single-epoch) positioning time-consuming, a proper satellite selection is necessary. Among the present methods, the satellite selection with a fixed high cut-off elevation angle (CEA) is least time-consuming. However, there is no criterion how large a fixed high CEA should be to achieve a high success rate and less time consumption. Besides, a fixed high CEA makes the number of visible satellites largely variable, which affects the success rate. Hence, a satellite selection strategy based on ambiguity dilution of precision (ADOP) is proposed. Firstly, the theoretical proof that the ADOP increases the least when removing satellites are all low-elevation-angle satellites is given, which is important to achieve the fast positioning with a high success rate. Then, the threshold β is calculated for a different number of satellites and a given ADOP. The satellites are selected based on their elevation angles from high to low until β of the selected satellites becomes smaller than the corresponding threshold; this method is called the extended floating CEA multi-GNSS (EF-multi-GNSS). The comparison of the single-frequency single-epoch positioning performance of the EF-multi-GNSS with the satellite selections based on a fixed low CEA (L-multi-GNSS) and a fixed high CEA (H-multi-GNSS) via the relative positioning experiments shows that: (1) the EF-multi-GNSS with a minimal number of satellites can achieve the fast positioning and a high success rate close to 100%. It can greatly reduce the time consumption of the L-multi-GNSS, by about 64.0%, by selecting 12.6 satellites of 23.4 satellites; (2) the floating CEA of EF-multi-GNSS eliminates the consideration how large a fixed high CEA should be, and a CEA larger than the fixed high CEA of the H-multi-GNSS makes it more suitable for different conditions.

Research of Fast Satellite Selection Algorithm for Multi‐constellation

We propose a fast satellite selection algorithm for multi-constellation, which is based on both Newton's identities for Geometric dilution of precision (GDOP) fast computation and optimal satellite geometric distribution for less cycle participation. An effective closed-form formula is proposed for GDOP approximation, avoiding conventional matrix inversion and complexity. We reduce computational cycles with auxiliary optimal satellite geometric distribution, which improves the efficiency of realtime positioning under the combined action of both. Simulation results indicate that we can save more than 44% and 99% computational complexity when selecting 7 satellites from 10 and 30 visible satellites respectively. The GDOP of proposed method is very close to optimal result, better than that of Quasi-Optimal algorithm.

GNSS reliability and positioning accuracy enhancement based on fast satellite selection algorithm and RAIM in multiconstellation

In order to improve the performance of positioning reliability, accuracy, and robustness in Global Navigation Satellite System (GNSS), several special actions, including receiver autonomous integrity monitoring (RAIM), weighted matrix estimation in weighted least squares (WLS), geometric dilution of precision (GDOP) in satellite geometry distribution, need to be taken into consideration in multiconstellation. With the extensive application and popularization of GPS, it makes remarkable contributions to scientific applications and engineering services. Currently, two emerging constellations (BDS, Galileo) as well as the recovery of GLONASS, the multiconstellation GNSS is undergoing dramatic development with better performance. Once the four systems are fully deployed, about 120 satellites will be available for GNSS users [1]. If all the visible satellites are used for position resolving, it will burden the receiver processor significantly. Meanwhile, there may be larger pseudorange measurement errors when the satellites have low elevation for the transmission of ionospheric and tropospheric delay. In addition, we cannot neglect simultaneous multiple outliers in multiconstellation GNSS compared with conventional RAIM. With respect to the weighted matrix factor in WLS, i.e., the uncertainty of the pseudorange measurements, estimation error is often used for weighted matrix estimation, without concerning real-time transmission error and signal quality, which somewhat enlarges the positioning accuracy. Thus, there is a need to deal with the above concerns.

A Fast Satellite Selection Algorithm: Beyond Four Satellites

Satellite selection is an important executive logic to prevent unnecessary navigation signals in the first place for Global Positioning System (GPS) receivers with limited number of channels and real-time processing power, such as those used in mobile phones, cars, and space crafts. In this paper, we propose a fast satellite selection algorithm to select more than four satellites based on the optimal geometries, which can obtain the smallest geometric dilution of precision (GDOP) values. The main idea of this fast algorithm is to select a subset of all satellites in view whose geometry is the most similar to the optimal geometry. Computer simulation shows that the consumed time of this algorithm is very close to that of the quasi-optimal satellite selection algorithm and obviously lower than that of the traditional optimal satellite selection algorithm to minimize GDOP factor, but the increased GDOP values relative to the minimal GDOP values are much smaller than those of the quasi-optimal satellite selection algorithm.

A Fast Satellite Selection Algorithm for Combined GPS and GLONASS Receivers

This paper derives a fast satellite selection algorithm; that is, an algorithm to select the best subset of satellites to use for positioning in circumstances whereby a satellite navigation receiver is not capable of using all satellites in view. The algorithm adopts the weighting factor concept to determine the contribution of each satellite to the overall positioning accuracy. Selection by this algorithm is shown to be especially efficient when the number of available satellites is large, so it is likely to be particularly useful for receivers capable of making combined measurements to GPS and GLONASS satellites.