Performance Improvement of Spaceborne SAR Using Antenna Pattern Synthesis Based on Quantum-Behaved Particle Swarm Optimization

Won, Young-Jin; Lee, Kyun Ho; Lee, Jae-Hyun

doi:10.1155/2017/6928970

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…Also, we can detect whether the algorithm is in convergence stagnation by certain means. When the test results are positive, it is known from the literature [16] that it is a good solution to increase population diversity through mutation.…”

Section: Quantum Bit Status Updatementioning

confidence: 99%

“…Compared with uniform array synthesis, the optimization of nonuniform array placement has always been a difficult problem. To solve this problem, many synthesis methods have been proposed, such as dynamic programming [11], fractional Legendre transform [12], simulated annealing [13], particle swarm optimization [14][15][16], and genetic algorithm [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…Aiming at the first problem, the improved genetic algorithm is used in the literature [17,18,20] to optimize the sparse array element spacing and amplitude weighting. In reference [15,16], an improved particle swarm optimization algorithm is used to obtain a better numerical solution. In order to solve the second problem, a new optimization model is proposed in reference [17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis

Wang

Xia

Tang

et al. 2020

International Journal of Antennas and Propagation

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The aim of the research is to propose a new optimization method for the multiconstrained optimization of sparse linear arrays (including the constraints of the number of elements, the aperture of arrays, and the minimum distance between adjacent elements). The new method is a modified wolf pack optimization algorithm based on the quantum theory. In the new method, wolves are coded by Bloch spherical coordinates of quantum bits, updated by quantum revolving gates, and selectively adaptively mutated when performing poorly. Because of the three-coordinate characteristics of the sphere, the number of global optimum solutions is greatly expanded and ultimately can be searched with a higher probability. Selective mutation enhances the robustness of the algorithm and improves the search speed. Furthermore, because the size of each dimension of Bloch spherical coordinates is always [−1, 1], the variables transformed by solution space must satisfy the constraints of the aperture of arrays and the minimum distance between adjacent elements, which effectively avoids infallible solutions in the process of updating and mutating the position of the wolf group, reduces the judgment steps, and improves the efficiency of optimization. The validity and robustness of the proposed method are verified by the simulation of two typical examples, and the optimization efficiency of the proposed method is higher than the existing methods.

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Section: Quantum Bit Status Updatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis

Wang

Xia

Tang

et al. 2020

International Journal of Antennas and Propagation

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“…Quantum-behaved particle swarm optimization (QPSO) algorithm [10,11], compared with the particle swarm optimization algorithm, has many advantages, such as faster convergence rate, fewer control parameters, and better global convergence. It has attracted much attention of scholars [12,13]. QPSO algorithm is a quantum mechanics based optimization algorithm.…”

Section: Qpsomentioning

confidence: 99%

MOQPSO‐D/S for Air and Missile Defense WTA Problem under Uncertainty

Qing-hua

Zhenhao

2017

Mathematical Problems in Engineering

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Aiming at the shortcomings of single objective optimization for solving weapon target assignment (WTA) and the existing multiobjective optimization based WTA method having problems being applied in air and missile defense combat under uncertainty, a fuzzy multiobjective programming based WTA method was proposed to enhance the adaptability of WTA decision to the changes of battlefield situation. Firstly, a multiobjective quantum-behaved particle swarm optimization with double/single-well (MOQPSO-D/S) algorithm was proposed by adopting the double/single-well based position update method, the hybrid random mutation method, and the two-stage based guider particles selection method. Secondly, a fuzzy multiobjective programming WTA model was constructed with consideration of air and missile defense combat's characteristics. And, the uncertain WTA model was equivalently clarified based on the necessity degree principle of uncertainty theory. Thirdly, with particles encoding and illegal particles adjusting, the MOQPSO-D/S algorithm was adopted to solve the fuzzy multiobjective programming based WTA model. Finally, example simulation was conducted, and the result shows that the WTA model constructed is rational and MOQPSO-D/S algorithm is efficient.

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“…However, PSO may easily converge prematurely. Similar to other swarm intelligence optimisation algorithms, PSO has the disadvantages of slow convergence speed, low optimisation accuracy, and ease of falling into local optima [23] when solving complex optimisation problems.…”

Section: Introductionmentioning

confidence: 99%

Radar Working State Recognition Based on Improved HPSO-BP

Wang

et al. 2021

International Journal of Antennas and Propagation

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In this paper, a recognition model based on the improved hybrid particle swarm optimisation (HPSO) optimised backpropagation network (BP) is proposed to improve the efficiency of radar working state recognition. First, the model improves the HPSO algorithm through the nonlinear decreasing inertia weight by adding the deceleration factor and asynchronous learning factor. Then, the BP neural network’s initial weights and thresholds are optimised to overcome the shortcomings of slow convergence rate and falling into local optima. In the simulation experiment, improved HPSO-BP recognition models were established based on the datasets for three radar types, and these models were subsequently compared to other recognition models. The results reveal that the improved HPSO-BP recognition model has better prediction accuracy and convergence rate. The recognition accuracy of different radar types exceeded 97%, which demonstrates the feasibility and generalisation of the model applied to radar working state recognition.

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Performance Improvement of Spaceborne SAR Using Antenna Pattern Synthesis Based on Quantum-Behaved Particle Swarm Optimization

Cited by 7 publications

References 12 publications

A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis

A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis

MOQPSO‐D/S for Air and Missile Defense WTA Problem under Uncertainty

Radar Working State Recognition Based on Improved HPSO-BP

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