A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

Abstract

The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM), which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM) is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM). In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM.

Genetic Programming Method for Satellite System Topology and Parameter Optimization

“…Jilla et al [2] proposed a multidisciplinary design optimization method that combines the life cycle cost model with a sensitivity analysis tool and applied it to the conceptual design of a distributed satellite system. Dong et al [3] proposed an optimization method based on double clustering and principal component analysis and applied it to the optimization design of an earth observation satellite. Richie et al [4] introduced an efficient, structured approach for the optimization of satellite attitude and orbit control as well as power subsystem.…”

A Review of Uncertainty-Based Multidisciplinary Design Optimization Methods Based on Intelligent Strategies

“…By introducing kernel functions (such as Gaussian kernel k(x, x ) = exp( −||x − x || 2 /2σ 2 )) that measure the distances between data points, the PCA algorithm can be extended to efficient nonlinear dimensionality reduction. In view of the advantages of PCA in dimensionality reduction, it has been widely applied to optimize the design of aircraft structures [153,154]. Nonlinear manifold learning: Manifold learning is based on the assumption of embedding high-dimensional data into low-dimensional nonlinear manifolds.…”

Orbital Maneuver Optimization of Earth Observation Satellites Using an Adaptive Differential Evolution Algorithm

“…According to the latitude and longitude information of the FOV, the latitude and longitude information of the ground target, the positions of the satellite at each moment, as well as the right ascension of Greenwich at the initial moment, we can obtain the key orbit performance indices of a satellite [39], such as the response time [32,40]. The response time is defined as the time required from when a request is received to observe a ground target until the satellite can observe it.…”

“…In the algorithm, an ant departs from the virtual node v 1 and travels through four nodes in the remaining four levels. At the l-th level, all probabilities for choosing arcs connecting starting node v l with all nodes in the next level are calculated based on Equation (39) and recorded in P l (line 3). Then, roulette wheel selection (i.e., RouletteWheel()) is adopted to choose an arc j that determines the node v l+1 (i.e., end node of arc j) at the (l + 1)-th level (lines 4-5).…”