A Satellite Selection Algorithm Based on PSO for a Integrated Navigation Receiver

The number of visible satellites has significantly increased, which provides a possibility for high-precision positioning of global navigation satellite systems (GNSSs). However, GNSS receivers with higher computing power will be required owing to the usage of a large number of visible satellites, and selection of partial satellites with better spatial geometry is required for obtaining high positioning precision schemes for a variety of applications. The geometric dilution of precision (GDOP) is related to the geometrical arrangement of the user and the satellites and computationally intensive, involving matrix multiplication and inversion operations. Various intelligent algorithms for satellite selection have been suggested recently, such as genetic algorithms (GAs). the computation number of GDOP was reduced, owing to the adoption and optimization of these algorithms. However, the algorithms suffer from two issues: 1) the computation process is complex, and significant parametric tuning is required. 2) the algorithms may fall into local optima, instead of the global optimum, owing to the stochasticity of the optimization process. In the paper, we have proposed a novel algorithm for satellite selection, based on chaotic particle swarm optimization (CPSO), which has the advantages of faster convergence, a smaller number of adjustable parameters, and stronger adaptability, compared with the existing algorithms. Firstly, the visible satellites received by the GNSS receiver are continuously encoded and randomly grouped, form the initial population. Afterward, the positions of the particles are updated by the PSO’s velocitydisplacement model. Finally, the CPSO-based algorithm for selection of satellites is designed to replace poor-performing particles with particles in the initial population, which helps to effectively avoid sub-optimal solutions. The proposed algorithm was compared with the traversal method. The simulation results demonstrated that if more than five satellites are selected, the satellite selection time amounts to 37.5% of the time required by the traversal method, and the GDOP error is in the 0.7 range. The proposed algorithm was further validated on the BDS/GPS integrated navigation satellite selection, suggesting the effectiveness of the application in the multiple GNSSs integrated navigation.

A binary discrete particle swarm optimization satellite selection algorithm with a queen informant for Multi-GNSS continuous positioning

Code phase GNSS receivers convert the measured satellite pseudoranges into estimates of the position and clock offset of the receiver, typically via an iterative, linearized least squares method. Since the pseudoranges themselves are noisy, the resulting estimates of position and time are random variables. To describe the accuracy of this solution, it is common to describe it statistically via the error covariance matrix. Rather than considering the individual elements of this covariance matrix, users frequently reduce it to a scalar performance indicator; the most common of these is the Geometric Dilution of Precision (GDOP). It is well known that the GDOP is a function of the satellite geometry; with only a few visible satellites in poor locations, the GDOP can become quite large. However, for a future with multiple, fully occupied GNSS constellations it is expected that receivers would select those satellites to track so as to achieve the best possible performance. Hence, an understanding of both how small the GDOP can be as a function of the number of satellites visible and the characteristics of the constellations that meet that bound are of value. Further, once identified, a receiver could exploit those constellation characteristics in selecting a subset of satellites. Investigating the best possible GNSS satellite constellation with respect to the GDOP is not a new problem. Recently, these authors developed achievable lower bounds to the GDOP as a function of the number of satellites; the bounds were also extended to non-zero mask angle and to multiple GNSS constellations. Further, using actual GPS satellite ephemeris data, it was shown by example that good GDOP performance resulted from constellations similar to the “best” constellations resulting from the bounds. This paper examines augmentation of the GNSS pseudoragnes with data from non-GNSS sensors; specifically, ranges. While integration of GNSS and non-GNSS sensors is not novel, the perspective in the paper is how such external sensors impact potential receiver performance (i.e. minimum GDOP) and what role they play in satellite selection. Specifically, tight lower bounds to GDOP when the GNSS is augmented by this additional measurement (barometric altimeter or a DME slant range) are presented; achievability of the bounds is also examined.

With the continuous development and improvement of global navigation satellite systems (GNSS), over a hundred navigation satellites now provide precise and reliable positioning services to various types of users. Most existing satellite selection methods adopt geometric dilution of precision (GDOP) as the selection criterion, which considers only the satellite geometric configuration while neglecting the quality of satellite observations. Consequently, the selected satellite set does not always yield ideal positioning results. To address this issue, we propose a satellite selection criterion that combines the near real-time accuracy of satellite observations with geometric configuration. The near real-time accuracy is estimated based on recent short-term observation data. This method estimates the near real-time accuracy of satellite observations using the Melbourne-Wübbena (MW) combination observation and uses this accuracy to weight and compute the weighted geometric dilution of precision (WGDOP-NRT) for satellite selection. Preliminary experimental results demonstrate that, compared to other criteria, the satellite sets selected using WGDOP-NRT achieve shorter convergence times and higher convergence accuracy in the standard precise point positioning (PPP) model. However, the frequent changes in satellite sets between epochs may negatively affect the positioning performance. To mitigate this issue, we propose an improved satellite selection strategy. If the WGDOP-NRT of the satellite set selected in the previous epoch remains below a predefined threshold in the current epoch, the same satellite set is retained. The method for determining the threshold has also been further discussed. This strategy maintains the geometric configuration and observation quality of the satellite set while enhancing its time correlation in satellite selection. Further experimental results indicate that using WGDOP-NRT and the proposed strategy improves PPP performance in both static and dynamic scenarios.

The navigation performance of Iridium satellite is simulated and analyzed based on STK from two aspects—the number of visible satellites and geometric dilution of precision (GDOP). Meanwhile we compare it with GPS and GALILEO system to demonstrate its performance. Experiment shows that the Iridium satellite system has a good coverage merit at two poles, which is even better than that of GPS and GALILEO. However, its global coverage is unstable and GDOP changes sharply from high latitude area to low latitude area which means Iridium satellite cannot orientate or navigate around the equator well. Then we explore the application fields of the Iridium satellite system.

Navigation satellite selection using neural networks

In mobile sensing for target localization and tracking, the geometric relationship between sensor nodes and a target may affect the positioning accuracy, of which the effect may be indicated by geometric dilution of precision (GDOP). In the paper, a target localization scheme based on GDOP for time-difference of arrival mobile sensing systems is presented. To attain the best sensor formation for maintaining good GDOP values, a time-varying function based on the GDOP curved surface is defined. Based on the derived function, path planning and relocation of mobile sensor nodes (MSNs) at each time cycle are discussed. Simulation results show that location accuracy improves when the MSNs steadily move toward the best deployment for tracking the target.

With the development of the Global Navigation Satellite System (GNSS), the integrated system including two or more individual constellations will be the main navigation mode in the future. For integrated system, the Geometric Dilution of Precision (GDOP) is widely used for the selection of satellites and the evaluation of positioning accuracy. The change of GDOP is related with the number of the satellites. In this paper, the impact of the number of satellites on the change of GDOP is theoretically derived. The result shows that, adding satellites from the existing tracked constellations into the integrated system, the GDOP always decreases when the number of satellites increases.

BackgroundGene selection is one of the critical steps in the course of the classification of microarray data. Since particle swarm optimization has no complicated evolutionary operators and fewer parameters need to be adjusted, it has been used increasingly as an effective technique for gene selection. Since particle swarm optimization is apt to converge to local minima which lead to premature convergence, some particle swarm optimization based gene selection methods may select non-optimal genes with high probability. To select predictive genes with low redundancy as well as not filtering out key genes is still a challenge.ResultsTo obtain predictive genes with lower redundancy as well as overcome the deficiencies of traditional particle swarm optimization based gene selection methods, a hybrid gene selection method based on gene scoring strategy and improved particle swarm optimization is proposed in this paper. To select the genes highly related to out samples’ classes, a gene scoring strategy based on randomization and extreme learning machine is proposed to filter much irrelevant genes. With the third-level gene pool established by multiple filter strategy, an improved particle swarm optimization is proposed to perform gene selection. In the improved particle swarm optimization, to decrease the likelihood of the premature of the swarm the Metropolis criterion of simulated annealing algorithm is introduced to update the particles, and the half of the swarm are reinitialized when the swarm is trapped into local minima.ConclusionsCombining the gene scoring strategy with the improved particle swarm optimization, the new method could select functional gene subsets which are significantly sensitive to the samples’ classes. With the few discriminative genes selected by the proposed method, extreme learning machine and support vector machine classifiers achieve much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.

Optimisation of fixture layout is critical to reduce geometric and form error of the workpiece during the machining process. In this paper the optimal placement of fixture elements (locator and clamp locations) under dynamic conditions is investigated using evolutionary techniques. The application of the newly developed particle swarm optimisation (PSO) algorithm and widely used genetic algorithm (GA) is presented to minimise elastic deformation of the workpiece considering its dynamic response. To improve the performances of GA and PSO, an improved GA (IGA) obtained by basic GA (GA) with sharing and adaptive mutation and an improved PSO (IPSO) obtained by basic PSO (PSO) incorporated into adaptive mutation are developed. ANSYS parametric design language (APDL) of finite element analysis is employed to compute the objective function for a given fixture layout. Three layout optimisation cases derived from the high speed slot milling case are used to test the effectiveness of the GA, IGA, PSO and IPSO based approaches. The comparisons of computational results show that IPSO seems superior to GA, IGA and PSO approaches with respect to the trade-off between global optimisation capability and convergence speed for the presented type problems.

Efficient multiprocessor scheduling is essentially the problem of allocating a set of computational jobs to a set of processors to minimize the overall execution time. The main issue is how jobs are partitioned in which total finishing time and waiting time is minimized. Minimization of these two criteria simultaneously, is a multi objective optimization problem. There are many variations of this problem, most of which are NP-hard problem, so we must rely on heuristics to solve the problem instances. Many heuristic-based approaches have been applied to finding schedules that minimize the execution time of computing tasks on parallel processors. Particle swarm optimization (PSO) is currently employed in several optimization and search problems due to its ease and ability to find solutions successfully. A variant of PSO, called as improved particle swarm optimization (ImPSO) has been developed in this paper and is hybridized with the ant colony optimization (ACO) to achieve better solutions. The proposed hybrid algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. In addition hybrid algorithm using improved particle swarm optimization (ImPSO) with artificial immune system (AIS) is also implemented for the same set of problems to compare with the proposed hybrid algorithm (ImPSO with ACO). It was observed that the proposed hybrid approach (Improved PSO with ACO) gives better results in experiments and reduces finishing and waiting time simultaneously. Key words: Particle swarm optimization (PSO), improved particle swarm optimization (ImPSO), ant colony optimization (ACO), job scheduling, finishing time, waiting time.

Low Earth Orbit (LEO) satellites utilizing Doppler measurements can be an effective supplementary positioning solution when Global Navigation Satellite System (GNSS) signals are unavailable. LEO satellites pose challenges to the efficiency and stability of real-time satellite selection algorithms due to their high dynamic and large number. The traditional satellite selection algorithms have the problems of high computational complexity and significant hardware dependence. In contrast, the intelligent optimization algorithm significantly improves the accuracy and real-time performance of satellite selection through global search and efficient processing. According to the characteristics of LEO satellites, a Multi-Strategy Fusion Grey Wolf Optimization (MSFGWO) algorithm is proposed for satellite selection. The experimental results show that when six satellites are selected, the average Doppler Geometric Dilution of Precision (DGDOP) value of the MSFGWO algorithm is 222.08. Compared with the DGDOP ratio of the traversal method, it is 1.03. The three-dimensional positioning accuracy is 192.86 m, and the positioning error is improved by 54.43% compared with the positioning accuracy of the traditional GWO algorithm. The longest continuous observation was achieved for 45 s, during which no switching of six satellites occurred at adjacent moments. The calculation time of the algorithm was only 0.0174 s, and the efficiency was improved by 93.43%. The MSFGWO algorithm proposed in this paper not only improves the overall optimization ability of the Grey Wolf Optimizer (GWO) algorithm and effectively reduces the DGDOP value but also significantly reduces the satellite switching number and prolongates the continuous observation time, thus improving the stability and accuracy of the positioning solution.

Due to its complexity and non-deterministic polynomial-time hard characteristic, multirobot task allocation problem remains a challenging issue in the field of cooperative robotics. Thanks to its easy implementation and promising convergence speed, the particle swarm optimization method has recently aroused increasing research interest in the area of multirobot task allocation problem. However, the efficiency of the standard particle swarm optimization is hindered by several deficiencies such as the inefficient capabilities in balancing exploration and exploitation, as well as the high likelihood of plunging into stagnation. Aiming at enhancing the performance of particle swarm optimization via remedying these two drawbacks, this paper proposes an improved particle swarm optimization method, which integrates standard particle swarm optimization 2011 with evolutionary game theory. To prevent particles being locked into stagnation, particles in the proposed particle swarm optimization first adopt the updating rules of standard particle swarm optimization 2011 to undertake their movements. Subsequently, attempting to well trade off the exploration and exploitation capabilities of particles, a novel self-adaptive strategy, which is determined by the evolutionary stable strategies of evolutionary game theory and the iteration number of particle swarm optimization, is presented to adaptively adjust the main control parameters of particles in the proposed particle swarm optimization. Since the convergence of particle swarm optimization remains paramount and dramatically affects the performance of particle swarm optimization, this paper also analytically investigates the convergence of the proposed particle swarm optimization and provides a convergence-guaranteed parameter selection principle for the proposed method. Finally, leveraging the development of the proposed particle swarm optimization, this paper completes the design of a new particle swarm optimization–based multirobot task allocation method. The performance of the new particle swarm optimization–based multirobot task allocation method is tested through three different allocation cases against four well-known evolutionary methods. Experimental results confirm that the proposed method generally outperforms its contenders in terms of the solution quality. Moreover, the proposed method performs slightly better than the majority of its peers as far as the computation time is concerned.

Global Positioning System (GPS) satellites signal processing to obtain all in view satellite measurements and to use them to find a solution and to do integrity monitoring forms a major component of the load on the receiver's processing element. If processing capability is limited there is restriction on the number of measurements which can be obtained and processed. Alternatively, the number of measurements can be restricted and the resulting saving in load on the processor can be used to offer more spare processing time which can be used for other user specific requirements. Thus if m visible satellites can provide measurements only n measurements can be used (n < m). The arrangement and the number of GPS satellites influence measurement accuracy. Dilution of Precision (DOP) is an index evaluating the arrangement of satellites. Geometric DOP (GDOP) is, in effect, the amplification factor of pseudo-range measurement errors into user errors due to the effect of satellite geometry. The GDOP approximation is an essential feature in determining the performance of a positioning system. In this paper, knowledge-based methods such as neural networks and evolutionary adaptive filters are presented for optimum approximation of GDOP. Without matrix inversion required, the knowledge-based approaches are capable of evaluating all subsets of satellites and hence reduce the computational burden. This would enable the use of a high-integrity navigation solution without the delay required for many matrix inversions. Models validity is verified with test data. The results are highly effective techniques for GDOP approximation.

In the field of satellite navigation and positioning, the selection of satellites is crucial for enhancing positioning accuracy, reducing computational load, and optimizing resource utilization. With the operation of the four global navigation satellite systems, the number of visible satellites has increased dramatically. Under the influence of multipath errors and non-line-of-sight reception, the quality of satellite signals becomes uneven in complex environments. Selecting the optimal set of satellites from a large number of visible ones with varying quality, in order to achieve the best balance between positioning accuracy and real-time performance, is a challenging task. To address this issue, the paper conducts a detailed analysis of the factors influencing positioning accuracy. It develops an evaluation model for positioning accuracy contributions that incorporates both satellite observation quality and geometric configuration, referred to as the AWGDOP (Adaptive Weighted Geometric Dilution of Precision) contribution. This evaluation model allows for the assessment of each satellite's contribution to the overall positioning accuracy. Furthermore, a fast satellite selection algorithm, based on positioning accuracy contribution, is proposed. It achieves rapid satellite selection by iteratively eliminating the satellite with the smallest contribution. To verify the effectiveness of the fast satellite selection algorithm proposed in this paper, tests are conducted in both open and obstructed environments. The test results indicate that the proposed algorithm can reduce the positioning error by 25% to 35% in terms of accuracy and decrease computational time by 5% to 20% in terms of real-time performance, compared to the full-constellation solution based on an empirical model.