scholarly journals A satellite selection algorithm based on adaptive simulated annealing particle swarm optimization for the BeiDou Navigation Satellite System/Global Positioning System receiver

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110317
Author(s):  
Ershen Wang ◽  
Caimiao Sun ◽  
Chuanyun Wang ◽  
Pingping Qu ◽  
Yufeng Huang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Satellite System ◽  
Navigation Satellite ◽  
Selection Algorithm ◽  
Swarm Optimization ◽  
Precision Error ◽  
Dilution Of Precision ◽  
Satellite Selection ◽  
Geometric Dilution Of Precision

In this article, we propose a new particle swarm optimization–based satellite selection algorithm for BeiDou Navigation Satellite System/Global Positioning System receiver, which aims to reduce the computational complexity of receivers under the multi-constellation Global Navigation Satellite System. The influences of the key parameters of the algorithm—such as the inertia weighting factor, acceleration coefficient, and population size—on the performance of the particle swarm optimization satellite selection algorithm are discussed herein. In addition, the algorithm is improved using the adaptive simulated annealing particle swarm optimization (ASAPSO) approach to prevent converging to a local minimum. The new approach takes advantage of the adaptive adjustment of the evolutionary parameters and particle velocity; thus, it improves the ability of the approach to escape local extrema. The theoretical derivations are discussed. The experiments are validated using 3-h real Global Navigation Satellite System observation data. The results show that in terms of the accuracy of the geometric dilution of precision error of the algorithm, the ASAPSO satellite selection algorithm is about 86% smaller than the greedy satellite selection algorithm, and about 80% is less than the geometric dilution of precision error of the particle swarm optimization satellite selection algorithm. In addition, the speed of selecting the minimum geometric dilution of precision value of satellites based on the ASAPSO algorithm is better than that of the traditional traversal algorithm and particle swarm optimization algorithm. Therefore, the proposed ASAPSO algorithm reduces the satellite selection time and improves the geometric dilution of precision using the selected satellite algorithm.

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

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141983024 ◽  
Author(s):  
Pengfei Zhang
Keyword(s):  
Computational Complexity ◽  
Navigation System ◽  
Satellite System ◽  
Navigation Satellite ◽  
Selection Algorithm ◽  
Dilution Of Precision ◽  
Satellite Navigation System ◽  
Satellite Selection ◽  
Geometric Dilution Of Precision ◽  
Global Navigation Satellite

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 Rotating Partition Satellite Selection Algorithm Based on Equal Distribution of Sky

2019 ◽  
Vol 72 (04) ◽  
pp. 1053-1069 ◽  
Author(s):  
Fangchao Li ◽  
Zengke Li ◽  
Jingxiang Gao ◽  
Yifei Yao
Keyword(s):  
Selection Process ◽  
Hypothesis Test ◽  
Elevation Angle ◽  
Satellite System ◽  
Navigation Satellite ◽  
Selection Algorithm ◽  
Equal Distribution ◽  
Dilution Of Precision ◽  
Satellite Selection ◽  
Fast Rotating

To achieve fast satellite selection for a multi-Global Navigation Satellite System (GNSS), thereby reducing the burden on a receiver's processing element and the cost of hardware, and improving the utilisation ratio of receiver signal channels, the relationship between the number of satellites and Geometric Dilution Of Precision (GDOP), the number of satellites selected and the computation time is analysed. A fast rotating partition algorithm for satellite selection based on equal distribution of the sky is proposed. The algorithm divides the satellite selection process into two parts: rough selection and detailed selection. Unhealthy satellites, according to a health identifier, and low elevation angle satellites with a large troposphere delay are eliminated during the rough selection process. During the detailed satellite selection process, the satellite sky is divided and rotated to match satellites based on the average angle distance between the satellite and central partition line. Static data from the International GNSS Service (IGS) station and dynamic data collected at China University of Mining and Technology were used to verify the algorithm, and the results demonstrated that an inverse matrix could be avoided to reduce computation complexity. Additionally, the new satellite selection algorithm has the merit that there is little effect on the computation when the selected satellites and number of satellites in the field increased. A single system of the Global Positioning System (GPS) and double system of GPS/Globalnaya Navigazionnaya Sputnikovaya Sistema (GLONASS) both passed the hypothesis test for each epoch. By including BeiDou Navigation Satellite System (BDS) data, data utilisation increased to more than 95% using the rotating partition algorithm. Also, the GDOP and positioning performance of a rotating partition algorithm and an optimal Dilution Of Precision (DOP) algorithm are compared in this paper, and the analysis result shows that both of the algorithms have only a small difference of GDOP and have comparable positioning performance.

A hybrid feature selection algorithm combining ReliefF and Particle swarm optimization for high-dimensional medical data

2021 ◽  
pp. 1-15
Author(s):  
Zhaozhao Xu ◽  
Derong Shen ◽  
Yue Kou ◽  
Tiezheng Nie
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Random Forest ◽  
Classification Accuracy ◽  
Particle Swarm ◽  
Medical Data ◽  
High Dimensional ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Swarm Optimization

Due to high-dimensional feature and strong correlation of features, the classification accuracy of medical data is not as good enough as expected. feature selection is a common algorithm to solve this problem, and selects effective features by reducing the dimensionality of high-dimensional data. However, traditional feature selection algorithms have the blindness of threshold setting and the search algorithms are liable to fall into a local optimal solution. Based on it, this paper proposes a hybrid feature selection algorithm combining ReliefF and Particle swarm optimization. The algorithm is mainly divided into three parts: Firstly, the ReliefF is used to calculate the feature weight, and the features are ranked by the weight. Then ranking feature is grouped according to the density equalization, where the density of features in each group is the same. Finally, the Particle Swarm Optimization algorithm is used to search the ranking feature groups, and the feature selection is performed according to a new fitness function. Experimental results show that the random forest has the highest classification accuracy on the features selected. More importantly, it has the least number of features. In addition, experimental results on 2 medical datasets show that the average accuracy of random forest reaches 90.20%, which proves that the hybrid algorithm has a certain application value.

Improved Quality View Selection for Analytical Query Performance Enhancement Using Particle Swarm Optimization

2017 ◽  
Vol 24 (06) ◽  
pp. 1740001 ◽  
Author(s):  
Amit Kumar ◽  
T. V. Vijay Kumar
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Response Times ◽  
Particle Swarm ◽  
Strategic Decision ◽  
Experimental Comparison ◽  
Selection Algorithm ◽  
Interaction Operator ◽  
Swarm Optimization ◽  
View Selection

A data warehouse, which is a central repository of the detailed historical data of an enterprise, is designed primarily for supporting high-volume analytical processing in order to support strategic decision-making. Queries for such decision-making are exploratory, long and intricate in nature and involve the summarization and aggregation of data. Furthermore, the rapidly growing volume of data warehouses makes the response times of queries substantially large. The query response times need to be reduced in order to reduce delays in decision-making. Materializing an appropriate subset of views has been found to be an effective alternative for achieving acceptable response times for analytical queries. This problem, being an NP-Complete problem, can be addressed using swarm intelligence techniques. One such technique, i.e., the similarity interaction operator-based particle swarm optimization (SIPSO), has been used to address this problem. Accordingly, a SIPSO-based view selection algorithm (SIPSOVSA), which selects the Top-[Formula: see text] views from a multidimensional lattice, has been proposed in this paper. Experimental comparison with the most fundamental view selection algorithm shows that the former is able to select relatively better quality Top-[Formula: see text] views for materialization. As a result, the views selected using SIPSOVSA improve the performance of analytical queries that lead to greater efficiency in decision-making.

Lower Bounds on DOP

2017 ◽  
Vol 70 (5) ◽  
pp. 1041-1061 ◽  
Author(s):  
Peter F. Swaszek ◽  
Richard J. Hartnett ◽  
Kelly C. Seals
Keyword(s):  
Lower Bounds ◽  
Global Navigation Satellite System ◽  
Satellite System ◽  
Navigation Satellite ◽  
Satellite Constellations ◽  
Dilution Of Precision ◽  
Gnss Positioning ◽  
Satellite Selection ◽  
Global Navigation ◽  
Global Navigation Satellite

Code phase Global Navigation Satellite System (GNSS) positioning performance is often described by the Geometric or Position Dilution of Precision (GDOP or PDOP), functions of the number of satellites employed in the solution and their geometry. This paper develops lower bounds to both metrics solely as functions of the number of satellites, effectively removing the added complexity caused by their locations in the sky, to allow users to assess how well their receivers are performing with respect to the best possible performance. Such bounds will be useful as receivers sub-select from the plethora of satellites available with multiple GNSS constellations. The bounds are initially developed for one constellation assuming that the satellites are at or above the horizon. Satellite constellations that essentially achieve the bounds are discussed, again with value toward the problem of satellite selection. The bounds are then extended to a non-zero mask angle and to multiple constellations.

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