Kesong Chen

Synthesis of Sparse Planar Arrays Using Modified Real Genetic Algorithm

In array design, the positions of sparse array elements is an important concern for optimal performance in terms of its ability to achieve minimum peak sidelobe level (SLL). This paper proposes a modified real genetic algorithm (MGA) based on resetting of chromosome for the element position optimization of sparse planar arrays with rectangular boundary. And here the multiple optimization constraints include the number of elements, the aperture and the minimum element spacing. By simplifying the space between the elements from the actual distance to Chebychev distance, the MGA searches a smaller solution space by means of indirect description of individual, and it can avoid infeasible solution during the optimization process by two novel genetic operators. Finally, the simulation results confirming the great efficiency and the robustness of the proposed method are shown in this paper

A modified real GA for the sparse linear array synthesis with multiple constraints

This paper describes a modified real genetic algorithm (MGA) for the synthesis of sparse linear arrays. The MGA has been utilized to optimize the element positions to reduce the peak sidelobe level (PSLL) of the array. And here the multiple optimization constraints include the number of elements, the aperture and the minimum element spacing. Unlike standard GA using fixed corresponding relationship between the gene variables and their coding, the MGA utilized the coding resetting of gene variables to avoid infeasible solution during the optimization process. Also, the proposed approach has reduced the size of the searching area of the GA by means of indirect description of individual. The simulated results confirming the great efficiency and the robustness of this algorithm are provided in this paper.

Design of 2-Dimension Sparse Arrays using an Improved Genetic Algorithm

For the element position synthesis of 2-dimension sparse rectangular arrays with the design constraints of the number of elements, the aperture and the minimum element spacing, an improved genetic algorithm (IGA) based on chromosome element resetting is presented in this paper. Compared with the synthesis method of thinned arrays, this new approach could exploit more degree of freedom of elements to control the characters of the sparse arrays. When the aperture, the number of elements and the minimum element spacing are fixed identical, the new approach can make the sparse plane array produce lower peak sidelobe level (PSLL). The simulated results show that the fitness function value of thinned plane arrays with 108 elements mentioned by Haupt can reduce 5.626 dB when the minimum element spacing of half wavelength was introduced into the optimization. And as well, the great efficiency and the robustness of IGA are shown in this paper

Design of Linear Sparse Antenna Arrays for Performance Improvement

An effective method for optimizing the performance of a fixed current distribution, uniformly spaced antenna array has been to adjust its element positions to provide performance improvement. Additionally, with a goal to reduce the peak sidelobe level (PSLL) of the array pattern, this method based on a modified real genetic algorithms (MGA) provides practical advantages such as taking the design constraints of the number of elements, the aperture and the minimum element spacing into account. The new method advanced in this paper reduces the size of the searching area of GA by means of indirect description of individual and avoids infeasible solution during the optimization process by designing the new genetic operators. The elementary steps of MGA are presented in this paper, and the simulated results confirming the great efficiency and the robustness of this algorithm are also provided here

Synthesis of thinned conical arrays using simulated annealing algorithm

the peak side lobe level (PSLL) is an important factor of conformal antenna arrays. This paper thins the basic conformal arrays-conical arrays using simulated annealing algorithm (SA). The simulation results indicate that SA can minimize the PSLL effectively, and attain the satisfactory results.

The Analysis of the Influence of Random Array Error to Wideband Signal DOA Estimation Reiterative Algorithm

This paper proposed a new approach, which is based on minimum mean-square error (MMSE) criterion, for wideband signal spatial direction-of-arrival (DOA) estimation when there is array error, and the impact of random array error to the new algorithm is analyzed in this paper. Pass the wideband signal mixed with array error through a bank of narrowband fllters to obtain narrowband signals, then recover the sparse representation of the narrowband signals by re-iterative method in the MMSE frameworks, and estimate the number and DOA of sources from the sparse representation. The new method does not require the number of sources for direction flnding, furthermore, it can estimate the DOA of coherent signals and the robustness of new algorithm to array error is better than coherent subspace algorithms. The simulated results conflrmed the efiectiveness and robustness of the new method.

Sparse Circular Array Optimization Using Genetic Algorithm

An Improved Genetic Algorithm is presented in this paper to solve the problem of optimum element position design of sparse circular arrays with multiple constraints. The initial feasible solutions for genetic algorithm (GA) which meet multiple design constraints are produced from the framework concerning element position of uniform concentric circular arrays. And let these solutions act as the thinning chromosome, which is used to describe the element distribution of the sparse circular arrays. By utilizing the IGA, a smaller searching space can be achieved, and the freedom of the element can be exploited. Finally, the simulation is done and the numerical results confirm the great efficiency and the robustness of the new algorithm.

Correction to “A Modified Real GA for the Sparse Linear Array Synthesis With Multiple Constraints” [Jul 06 2169-2173]

In the above titled paper (ibid., vol. 54, no. 7, pp. 2169-2173, Jul. 06), there was an error in production for the flowchart (Fig. 2). A corrected figure is presented here.

Aperture release model of thinned arrays

For the synthesis of symmetrical linear thinned arrays with a given thinning rate, it would have almost same MSLL solution between taking only segmental aperture nearby both ends of the aperture into account and taking the all aperture into account, at the same time, considering only segmental aperture can reduce the complex computing task remarkably. In this paper, the element distribution characteristic over the aperture of many optimum thinned arrays are studied firstly, then a aperture release model is founded statistically to synthesize the thinned arrays, utilizing this model can improve the optimization efficiency with scarcely any sacrifice of the result array performance. Finally, some analyses of the aperture release model are presented.

Correction to "Synthesis of sparse planar arrays using modified real genetic algorithm"

In array design, the positions of sparse array elements is an important concern for optimal performance in terms of its ability to achieve minimum peak sidelobe level (SLL). This paper proposes a modified real genetic algorithm (MGA) based on resetting of chromosome for the element position optimization of sparse planar arrays with rectangular boundary. And here the multiple optimization constraints include the number of elements, the aperture and the minimum element spacing. By simplifying the space between the elements from the actual distance to Chebychev distance, the MGA searches a smaller solution space by means of indirect description of individual, and it can avoid infeasible solution during the optimization process by two novel genetic operators. Finally, the simulation results confirming the great efficiency and the robustness of the proposed method are shown in this paper