International Journal of Antennas and Propagation

International Journal of Antennas and Propagation / 2020 / Article
Special Issue

Recent Advances in Design and Signal Processing for Antenna Arrays 2020

View this Special Issue

Research Article | Open Access

Volume 2020 |Article ID 2196049 |

Zhikun Chen, Tao Li, Dongliang Peng, Kang Du, "Two-Dimensional Beampattern Synthesis for Polarized Smart Antenna Array and Its Sparse Array Optimization", International Journal of Antennas and Propagation, vol. 2020, Article ID 2196049, 13 pages, 2020.

Two-Dimensional Beampattern Synthesis for Polarized Smart Antenna Array and Its Sparse Array Optimization

Academic Editor: Lei Yu
Received21 Mar 2020
Accepted25 May 2020
Published13 Jun 2020


Polarized smart antenna array has attracted considerable interest due to its capacity of matched reception or interference suppression for active sensing systems. Existing literature does not take full advantage of the combination of polarization isolation and smart antennas and only focuses on uniform linear array (ULA). In this paper, an innovative synthesis two-dimensional beampattern method with a null that has cross-polarization for polarized planar arrays is proposed in the first stage. This method aims to further enhance the capability of interference suppression whose optimization problem can be solved by second-order conic programming. In the second stage, a new sparse array-optimized method for the polarized antenna array is proposed to reduce the high cost caused by the planar array that is composed of polarized dipole antennas. Numerical examples are provided to demonstrate the advantages of the proposed approach over state-of-the-art methods.

1. Introduction

Smart antennas increase the capacity of communication systems by improving signal-to-noise ratio (SNR) in mobile communications [1, 2]. Adaptive array coherently combines multipath components of the desired signal and null interfering signals from different directions of arrival from the desired signal. In terms of the capability to match reception and suppress interference, the adaptive array is also applied in modern radar systems [3, 4]. However, this kind of conventional space-time adaptive technology has its own limitations, especially in intentional interference. Thus, polarization diversity is a potential solution [58]. Polarization diversity not only reflects complete information on electromagnetic waves of targets but also is an additional degree of freedom that can be exploited in response to dynamic environments. Polarization diversity can maximize the received SNR when matching the target polarization and can isolate the interfering signal from the desired signal when cross-polarizing the interfering signal. Considering this advantage, two synthesizing methods are introduced to design an electromagnetic beam with desired power and polarization [5, 9]. According to the literature [5, 9], dipole antennas are suitable for generating arbitrary polarization with a pair of orthogonal far-field electric vectors. Polarized arrays can transmit a beampattern that can be selected freely to design a desired null and polarization around areas of interest. An effective approach to suppress strong interference is based on the principle of polarization mismatch factor; that is, the polarization in the specific region that corresponds to the direction of strong interference is crossed to that of strong interference to isolate the interference signal energy at the receiver as much as possible. Thus, the beamforming for polarized antenna array has become a popular research topic in recent years [10, 11]. However, most existing literature studies on polarized beamforming are only based on a simple uniform linear array (ULA) due to the high-dimensional weight matrix. A Fábry–Perot cavity antenna with a reconfigurable partially reflecting surface is proposed to produce dual-polarized 2D beams [12]. Realizing the compatibility of this kind of antenna with space-time adaptive processing technology based on ULA is difficult, additionally, for the planar array composed of polarized dipole antennas. A planar array generally comprises dozens or even hundreds of dipole antenna elements at the cost of high-precision hardware for its implementation [13]. Thus, sparse array design is essential for satisfying the function of using a finite number of elements to realize the polarization vector beam.

Unlike the conventional phase arrays, the sparse array design for polarized smart array must be a constrained optimization problem [1418]. One particular constraint is polarization matching, which is an inequality constraint. This constraint means that the optimization problem cannot be solved by the single-objective optimization algorithm. Transforming constraint optimization into multiobjective optimization or adding a penalty function in the fitness function is necessary to solve such a constrained optimization problem [19]. Scholars proposed multiobjective algorithms based on a new evolutionary pattern in recent decades. According to [20], the optimization problem with inequality constraint can be transformed into a multiobjective optimization problem. This problem can be solved using the multiobjective differential evolution (MODE) algorithm. In this work, the multioptimization design of a sparse antenna array for polarized smart antennas is addressed following the MODE algorithm.

The rest of this paper is organized as follows. The signal model for the polarized antenna is introduced in Section 2. Second-order cone programming (SOCP) is presented in Section 3 to solve the optimization problem of the 2D polarized beampattern design. Multiobjective differential evolution is applied to solve the multiobjective optimization problem in Section 4. Numerical simulations are presented in Section 5, and the conclusions are presented in Section 6.

2. Signal Model

2.1. Representation of Polarization State

The polarization state of the far-field electric field can be characterized by its polarization ellipse. The polarization ellipse is the most frequently used representation of polarization states. The polarization angle can be defined as the angle between the major axis of the ellipse and a reference vector to orient the ellipse in space. In the ellipse, the polarization state can be defined by its polarization axial ratio and angle. The electric field produces an ellipse over one period when plotted on a 2D plane normal to the propagation direction. The polarization axial ratio is the ratio of the major to minor axes of the ellipse. This ratio also determines the circularity (low axial ratio) and linearity (high axial ratio) of the polarization.

In Figure 1, is called the orientation angle (the angle between the major semiaxis of the ellipse and the H-axis) and is the ellipse angle (the angle measuring the ratio of the two semiaxes). If the amplitude of the electromagnetic wave is ignored, then the polarization state of electromagnetic waves can be characterized by parameter pair . This state is called the geometric descriptor of polarization state. When , the resultant polarization is linear; moreover, provides a horizontal polarization and leads to a vertical polarization. However, for , the resultant polarization is circular for any orientation angle .

The mathematical relationship between electric field and polarization ellipse parameters can be expressed as follows:

In (1), the first item on the right is the rotation matrix, and second one is the ellipticity vector; represents the energy of an electromagnetic wave.

The complex electric field vector can also be defined as follows:

In (2), represents the ratio between the amplitude of vertical and horizontal channel electric fields, is the phase difference between the vertical and the horizontal channel components, , and . Given that the energy information of electromagnetic wave is not considered in this study, the parameter pair is reversible to the polarization state of electromagnetic wave. Thus, the parameter pair can be called the phase descriptor of polarization state of electromagnetic wave. If and , then the relationship between geometric and phase descriptors can be expressed as follows:

2.2. Polarized Vector Array Response

Suppose is a unit vector representing a spatial direction in , which can be expressed as follows:

In (4), is pitch, and is azimuth:where can be described as a right-hand coordinate system, as shown in Figure 2.

If the plane wave is traveling along the -direction, the electric field is orthogonal to and lies in the plane spanned by . Polarized vector antennas comprising orthogonal electric and magnetic dipoles are considered. In this spatial coordinate system , each of the six dipoles has the following responses (ignoring a common constant that is determined by the antenna parameters and the distance to the antenna).

In this paper, we simplified the representation of polarization state in the coordinate system and only considered the electric field and magnetic field along the x direction. Thus, the polarized antennas have the responses as follows (regardless of the antenna parameters and the distance to the antenna):

If the antenna only comprises electric and magnetic dipole elements along , then the response is as follows:

Moreover, for a given antenna response , and are used to denote the response to the H and V channels, respectively, or as a formula.

3. Two-Dimensional Beampattern Synthesis for Polarized Smart Antenna Array

A two-dimensional beamforming method for polarized smart antenna array is proposed in this section. Xiao and Nehorai designed a null and sidelobe polarization for the polarized beampattern [6]. However, the null and polarization controls of the sidelobe are independent, which did not maximize the advantages of polarization isolation and null. SOCP is still adopted to deal with the two-dimensional beamforming for polarized antenna arrays. Different from the previous literature, this section extends it to two-dimensional polarization beamforming. Here, the weight matrix is synthesized to generate a beampattern. Suppose a uniform planar array comprises antennas with an element spacing (half wavelength), as shown in.

According to the array model shown in Figure 3, the weighting matrix is introduced in this section to be the concatenation of all :where

For convenience of calculation, the matrix is transformed into column vectors as follows:

Given the location of actual element , which has the candidate positions, the array response, as a function of spatial direction , can be expressed as follows:where and is the wave number.

Thus, in terms of the vector antenna response , the vector antenna array response is further obtained as follows:

The antenna array response of H and V channels is defined as follows:

The normalized electrical field emitted from the antenna array (ignoring the common carrier and the baseband signal ) can be expressed as follows:where and are used to denote the decomposition of :

Along , the polarization state can be determined by the ratio between and , and the transmitting power can be expressed as :where and are the complex weights of the horizontal and vertical channels, respectively. and are, respectively, defined as follows:

The selection of under maximal sidelobe minimization is one of the problems in achieving the following goals.(1)Maximize the power of the main beam (at direction ) and match polarization parameter pair ; the region of main beam is denoted by )(2)Minimize power of sidelobe (this region is denoted by at direction )(3)A desired null in the directions of interferers (generally located in the sidelobe region and denoted by ), which has cross-polarization constraint

Based on above, the polarized beampattern synthesis problem can be formulated as follows:where is the optimal solution, which measures the beampattern power gain over the sidelobes and does not depend on the main beam polarization. The third constraint directly determines the polarization of notch and its depth (). When is a column vector of , the above optimization mode is also applicable to the synthesis of the polarized beampattern for ULA. This condition is a vector optimization problem that is difficult to solve using an optimization algorithm. Thus, this optimization problem is split into two equivalent scalar optimization problems as follows:

Horizontal channel is

Vertical channel is

In (21) and (22), is called the null concave matrix for polarized smart antenna array and is introduced as follows:where , , and is a constant. The above optimized problem is convex and is also an SOCP problem.

4. Sparse Array Design for Polarized Smart Antenna

Pattern performance and polarization constraint for the polarized smart antenna must be considered in the sparse process of antenna array [21]. The optimization model must be a multiconstraint problem, including the unequal constraints, to accomplish both purposes. Following [22, 23], a two-stage design approach is adopted to deal with the sparse array design for polarized smart antenna. In the first stage, the weight matrix is synthesized to generate a pattern for polarized antenna array with an antenna element, as mentioned in Section 3. In the second stage, the element positions of the full array are treated as candidate positions that are selected by a sparse array with antenna elements.

Mean square error of polarization matching in interest area (PMSE) is defined as an objective function, whereas the peak sidelobe levels (PSLLs) minimization of sparse array design is another objective function [24]. This optimization problem of PMSE can be constructed as follows:where is the number of sampling points in the far-field area for the optimal polarized beampattern.

The optimization problem for a sparse antenna array design aimed at polarization matching to control the designed polarization as desired in the interesting region can be written as follows:where the first three constraints fix the four sides of the antenna aperture, is the tolerance value for PSLLs in the fourth constraint (which is an inequality constraint), the fifth constraint sets the actual number of elements (), and the last constraint realizes the depth and polarization of null ( is the peak of the main lobe). Following the idea of multiobjective optimization, the inequality constraint can be regarded as another objective function in the evolution process [24]. This constraint can be optimized in parallel implementation as follows:where is defined in (23) and . Inspired by [23], the MODE algorithm is suitable for this kind of multiobjective optimization problem and is designed to handle a multiset of solutions in a single iteration. In the multiobjective domain, the MODE aims to identify a set of Pareto optimal solutions to operate the selection of the best individual for the mutation (Appendix A). At the end of the evolutionary search, the nondominated solution archive is passed through a dominance filter to yield the global near-Pareto-optimal frontier (Appendixes B and C) [23, 25]. The individual representation (Initial and Coding) needs to be explained as follows.

4.1. Initial

Let denote the initial value of the parameter in the population at generation , which is shown as follows:where , and is a uniformly distributed random variable within the range , and and are the lower and upper bounds of the variable parameter, respectively. The individual in the population at generation can be obtained in its vector form as follows:

4.2. Coding

The initial value of the antenna position is set as the partition points of a planar array aperture. The random perturbation is controlled by , and binary coded denotes the location of actual elements, which is shown as follows:where denotes the real variables that are sorted by size as integer variables converted into binary codes.

The whole process of sparse array design using MODE algorithm can be summarized as follows (Algorithm 1).

Input: , , , ,
Step 1: initial. ;
Step 2: coding., is defined in (24),
For to do
 Step 3: mutation. Randomly select three distinct individuals, , , and , who are all different from the target individual. Generate a perturbed individual by
  The scaling factor is constant. denotes the best individuals among the three individuals, which is mean that the one has best fitness function value
 Step 4: crossover. The objective function value of each trial vector is compared with that of its corresponding target vector . The vector with the smaller fitness value will be retained in the next generation. Generate a trial individual as follows:
  calculate the fitness value of ,
 Step 5: Pareto dominance
 If ( dominates )
  replace by in the current population , and then add to the advanced population
  add to the advanced population
fittest solutions is select in every fast nondominated sorting and save them in ; is the with the lowest fitness value of

Steps 3–5 evaluate the fitness function at these solutions at each generation , select the fittest solutions via fast nondominated sorting, and store them in the current population . In our approach, fast nondominated sorting is applied to guarantee that the population maintains its original size, and the nondominated solutions in the population are identified at each generation of the evolution process. The nondominated solutions are saved in the advanced population that corresponds to the feasible solution [26]. Otherwise, the infeasible solution is reserved in the current population.

5. Numerical Example

The simulation results are presented in this section to illustrate the effectiveness of the proposed method. Considering the preliminary results reported in [6, 9], the application of the SOCP to polarized beampattern synthesis must be investigated. Thus, the polarized beampattern synthesis is introduced for polarized smart antenna based on ULA in Example 1. This example highlights the continuity and innovation of the proposed method. The cross-polarization is added on the null of the beampattern to improve the capability of interference suppression in the sidelobe region, which is different from [6]. Example 2 synthesizes the 2D polarization beampattern and obtains the corresponding polarization state for the polarized smart antenna array. Example 3 realizes the sparse array design of the 2D polarized smart antenna array.

5.1. Polarized Beampattern with a Null that Has Cross-Polarization Based on ULA

Assume that a strong interference is located at an azimuth angle of , and its polarization state can be depicted with polarization ellipse parameters of . Thus, a desired null () with that is located at must be designed. The entire angle area is with 1° angular spacing (such that ). Assume a strong interference is at the azimuth angle of . The polarization state of this strong interference can be depicted with polarization ellipse parameters of . The desired null () with , which is located at , must be designed.

Figure 4(a) depicts a polarized beampattern with one desired polarization. The result shows that the polarization can be controlled such that the interference of the known source is isolated, and the gain of the main lobe is 16 dB. Figure 4(b) presents that the desired polarization ellipse parameter is a constant in the entire angle region. Figure 5(a) depicts a polarized beampattern with the desired null. The result also shows that the depth of the obtained null can reach −24 dB compared with the maximum peak sidelobe level. However, Figure 5(b) displays that the curve of the polarization ellipse parameter in the entire angle area is not constant, except for the main lobe region; that is, no law exists.

Figure 6(a) shows the beampattern of the proposed method. Figure 6(b) displays that the curve of the polarization ellipse parameter in the entire angle area is not constant; that is, no law exists. However, this curve meets the interests in that region, such as the main lobe and jamming direction. Thus, all these findings justify the efforts to prevent jamming due to polarization mismatch.

5.2. Two-Dimensional Beampattern Synthesis for Polarized Smart Antenna Array

Similar to Example 1, SOCP is applied to optimize the 2D polarized beamform as mentioned in Section 3. The only difference from the previous example is that the variable is no longer a column vector but a matrix. Thus, the weight matrix must be transformed into column vectors again. Assume that a planar array of () is synthesized and satisfies the following constraints: (1) The distance between any pair of elements is equal to . (2) All elements are allocated on a fixed aperture of . (3) The main beam is targeted at and with beam width of and polarization constraint . (4) The null is located at and with the polarization constraint . Therefore, the coordinate vector of the null concave in the sidelobe region is equal to , , and the depth is (). According to the optimization model defined by equations (16) and (17), CVX MOSEKY solver is used to solve the two equivalent scalar problems [27]. The following results are obtained.

For clarity, 3D and contour figures represent the optimized beampattern. A null concave with the average depth of −91.5 dB is located at and in Figure 7. Compared to the maximum peak sidelobe (−16.5 dB), the polarized beampattern obtained a suppression gain with −75 dB. and are also represented by a surface to verify the polarization state of 2D polarized beampattern, as shown in Figure 8. Different from the previous example, the polarization state of interesting area cannot be directly observed from the curved surface. However, if the tangent of the surface is used for representation, then the polarization state values of null concave cannot be fully expressed. Thus, a table is used to express the corresponding polarization state values at several sampling points of interesting areas, as shown in Tables 1 and 2.

Sampling points123456789


Sampling points123456789


Tables 1 and 2 suggest that the polarization parameter pair is consistent with the experimental setting, where and , respectively, refer to the sampling points of the main lobe and null concave, as defined in (18). Similar to Example 1, the constraint of polarization matching cannot guarantee the polarization state outside the region of interest.

5.3. Sparse Array Optimization of Polarized Smart Antenna Using MODE

The MODE algorithm is applied for sparse antenna array design to verify the effectiveness of the method mentioned in Section 4. Given its particularity, this optimization problem is suitable for the multiobjective differential evolution algorithm. Thus, we only apply the multiobjective differential evolution algorithm to its sparse array optimization. Herein, the sparse rate is set as 75%. Thus, antenna elements selected from 100 candidate positions (N = 10 × 10 planar array) are used. Other simulation conditions remain the same as those in Example 2. The parameters of MODE are defined and applied as follows:(1)Population size: (2)Initial range: ,(3)Mutation probability: (4)Crossover probability: (5)Maximum number of iterations:

SOCP is repeated to establish the optimum covariance matrix , and the evolution process continues until the maximum number of iterations for testing convergence is reached. The results are shown as follows.

The red asterisks in Figure 9(a) record all the infeasible solution sets, and the black diamonds represent the feasible solution sets in one run. To balance the performance of both PMSE and PSLL, the solution (PMSE = 0.0086 and PSLL = 0.009) marked by the dotted circle should be the best choice in experience. Figure 9(b) shows the selected antenna positions corresponding to the best solution, and the sparse rate is set to 75%. In Figure 10, it is easy to see that the average PSLL is about −10 dB, and the depth of null obtained is −50 dB. Those performances are worse than those in Figure 7. This finding is due to the decrease in the number of array elements, which leads to increased sidelobe levels. However, the performance of polarization matching in the interesting area and the PSLLs outside the beampattern is balanced. The restriction for the polarization matching in this study is to maintain the best approximation of polarization matching while keeping the sidelobe level as flat as possible, as shown in Figures 9 and 10. Figure 11 shows the surface value of the polarization ellipse parameter that uses the MODE. As previously described, we still cannot see the polarization state of interest region in Figure 11. Thus, the corresponding polarization states are shown in Tables 3 and 4, respectively.

Sampling points123456789


Sampling points123456789


Table 5 shows the chosen PMSE and PSLLs in ten independent runs.

Algorithm: MODEIndex of runs


Table 5 summarizes the results of MODE in 10 runs. The highest PMSE is below 0.0091, whereas all PSLLs slightly fluctuate around 0.009. This finding suggests the stability of PSLLs obtained using MODE. MODE has almost the same running time as DE despite the constraint added by the former to the optimization problem. Moreover, MODE has a simpler algorithm structure than that of DE. These arguments justify efforts to prevent PMSE.

6. Conclusions

A novel two-stage design approach for the sparse antenna array design of 2D polarized smart antenna arrays is proposed in this work. A new model of optimal polarized beampattern optimization problem based on SOCP is formulated in the first stage. A multiobjective optimization problem for sparse arrays, which can be solved by MODE, is then proposed in the second stage. Compared with the existing method in [6], the cross-polarization on the null is constrained to maximize the capability of interferer suppression while retaining the polarization matched reception in the main lobe. This method is extended to the two-dimensional polarized antenna array. Given the substantial hardware cost, the MODE algorithm based on Pareto technique is proposed to obtain the sparse array. In this algorithm, the PMSE in the interest area is presented as another objective function to be optimized. The simulation results reveal that MODE outperforms other algorithms in terms of sparse arrays while maintaining polarization matching performance.

Although only beampattern synthesis and sparse array for polarized smart antenna array are considered, the effect of the matching reception and interference suppression is not evaluated in practical application. The extension of this method to the detection and interference suppression of systems is part of future studies.


A. Opposition-Based Learning

Proof. The opposition-based learning is introduced to generate opposite solutions in the initialization to increase the chances of starting with the fittest solution [20], which can be expressed as follows:

B. Pareto Dominance

Proof. A solution is said to dominate another solution under the following conditions:(1)(2)where is the number of objective functions and is the corresponding fitness function. Any individual that is not dominated by any other member is considered nondominated.

C. Fast Nondominated Sorting

Proof. Assume a Pareto optimal set denoted by . denotes the number of dominated solutions, while is a set of solutions dominated by the solution [20].
For every solution in , both and are calculated. All solutions in the first nondominated front clear their domination count to zero. Afterwards, when , each solution visits each member of its set , and . Any member is saved in a separate list . These members belong to the second nondominated front . Each member of and the third front are identified. This process continues until all fronts have been identified.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this article.


This work was supported by the National Natural Science Foundation of China (Grant nos. 61701148, 61703128, 61703131, and 61703129).


  1. C. B. Dietrich, W. L. Stutzman, B.-Ki Kim et al., “Smart antennas in wireless communications: base-station diversity and handset beamforming,” Antennas and Propagation Magazine IEEE, vol. 42, no. 5, pp. 42–151, 2000. View at: Google Scholar
  2. A. Alexiou and M. Haardt, “Smart antenna technologies for future wireless systems: trends and challenges,” IEEE Communications Magazine, vol. 42, no. 9, pp. 90–97, 2004. View at: Publisher Site | Google Scholar
  3. F. R. . Castella, “Adaptive antenna nulling for a phased array monopulse radar,” in Proceedings of the International Conference of the 1992 IET Radar, Cambridge , UK, October 1992. View at: Google Scholar
  4. V. Tocca, D. Vigilante, L. Timmoneri et al., “Adaptive beamforming algorithms performance evaluation for active array radars,” in Proceedings of the IEEE Radar Conference (RadarConf18), Oklahoma City, OK, USA, April 2018. View at: Google Scholar
  5. N. H. Noordin, W. Zhou, A. O. El-Rayis et al., “Single-feed polarization reconfigurable patch antenna,” Progress in Electromagnetics Research B, vol. 54, no. 54, pp. 1-2, 2012. View at: Google Scholar
  6. J. J. Xiao and A. Nehorai, “Optimal beampattern synthesis of a polarized array. statistical signal processing, SSP ‘07,” in Proceedings of the IEEE/SP 14th Workshop on IEEE, St. Louis, MO, USA, May 2007. View at: Google Scholar
  7. P. Jacob, A. S. Madhukumar, and A. Alphones, “Interference mitigation through cross polarized transmission in femto-macro network,” IEEE Communications Letters, vol. 17, no. 10, pp. 1940–1943, 2013. View at: Publisher Site | Google Scholar
  8. A. Ali, H. Liang, and S. Shetty, “Polarized beamforming for enhanced countermeasure of wireless jamming attacks,” in Proceedings of the IEEE 35th International Performance Computing and Communications Conference (IPCCC), IEEE, Las Vegas, NV, USA, December 2016. View at: Google Scholar
  9. J.-J. Jin-Jun Xiao and A. Nehorai, “Optimal polarized beampattern synthesis using a vector antenna array,” IEEE Transactions on Signal Processing, vol. 57, no. 2, pp. 576–587, 2009. View at: Publisher Site | Google Scholar
  10. S. M. Bowers, A. Safaripour, and A. Hajimiri, “Dynamic polarization control,” IEEE Journal of Solid-State Circuits, vol. 50, no. 5, pp. 1224–1236, 2015. View at: Publisher Site | Google Scholar
  11. Y. Lu, Y. Xu, Y. Huang, and Z. Liu, “Diversely polarised antenna-array-based narrowband/wideband beamforming via polarisational reconstruction matrix inversion,” IET Signal Processing, vol. 12, no. 3, pp. 358–367, 2018. View at: Publisher Site | Google Scholar
  12. P. Xie, g.-m. Wang, H. Li et al., “A dual-polarized two-dimensional beam steering fabry-pérot cavity antenna with a reconfigurable partially reflecting surface,” IEEE Antennas & Wireless Propagation Letters, vol. 16, no. 1, p. 1, 2017. View at: Publisher Site | Google Scholar
  13. M. V. Komandla, “Investigations on dual slant polarized cavity-backed massive MIMO antenna panel with beamforming,” IEEE Transactions on Antennas and Propagation, vol. 65, no. 12, pp. 6794–6799, 2017. View at: Google Scholar
  14. K. Chen, X. Yun, Z. He, and C. Han, “Synthesis of sparse planar arrays using modified real genetic algorithm,” IEEE Transactions on Antennas and Propagation, vol. 55, no. 4, pp. 1067–1073, 2007. View at: Publisher Site | Google Scholar
  15. C. Chuan Lin, A. Anyong Qing, and Q. Quanyuan Feng, “Synthesis of unequally spaced antenna arrays by using differential evolution,” IEEE Transactions on Antennas and Propagation, vol. 58, no. 8, pp. 2553–2561, 2010. View at: Publisher Site | Google Scholar
  16. D. G. Kurup, M. Himdi, A. Rydberg et al., “Synthesis of uniform amplitude unequally spaced antenna arrays using the differential evolution algorithm,” IEEE Transactions on Antennas and Propagation, vol. 51, no. 9, pp. 2210–2217, 2003. View at: Publisher Site | Google Scholar
  17. R. Bhattacharya, T. K. Bhattacharyya, and R. Garg, “Position mutated hierarchical particle swarm optimization and its application in synthesis of unequally spaced antenna arrays,” IEEE Transactions on Antennas and Propagation, vol. 60, no. 7, pp. 3174–3181, 2012. View at: Publisher Site | Google Scholar
  18. D. Caratelli and M. C. Vigano, “A novel deterministic synthesis technique for constrained sparse array design problems,” IEEE Transactions on Antennas and Propagation, vol. 59, no. 11, pp. 4085–4093, 2011. View at: Publisher Site | Google Scholar
  19. R. Farmani and J. A. Wright, “Self-adaptive fitness formulation for constrained optimization,” IEEE Transactions on Evolutionary Computation, vol. 7, no. 5, pp. 445–455, 2003. View at: Publisher Site | Google Scholar
  20. A. Anders, J. Andersson, and J.-O. Aidanpaa, “Constrained optimization based on a multiobjective evolutionary,” in Proceedings of the 2003 Congress on Evolutionary Computation, Canberra, ACT, Australia, December 2003. View at: Google Scholar
  21. A. B. Smolders, S. J. Geluk, and A. C. F. Reniers, “Circularly polarized sparse arrays realized by randomly rotated linearly polarized antennas,” IEEE Antennas & Wireless Propagation Letters, vol. 99, p. 1, 2017. View at: Google Scholar
  22. Z. Cai and Y. Wang, “A multiobjective optimization-based evolutionary algorithm for constrained optimization,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 6, pp. 398–417, 2006. View at: Publisher Site | Google Scholar
  23. Z.-k. Chen, Y. Feng-Gang, Q. Xiao-Lin, and Z. Yi-Nan, “Sparse antenna array design for MIMO radar using multi-objective differential evolution,” International Journal of Antennas and Propagation, vol. 2016, Article ID 1747843, 12 pages, 2016. View at: Google Scholar
  24. F. Xue, A. C. Sanderson, and R. J. Graves, “Pareto-based Multi-objective differential evolution,” in Proceedings Of The 2003 Congress On Evolutionary Computation, Canberra, Australia, December 2003. View at: Google Scholar
  25. M. Ali, P. Siarry, and M. Pant, “An efficient differential evolution based algorithm for solving multi-objective optimization problems,” European Journal of Operational Research, vol. 217, pp. 404–416, 2012. View at: Google Scholar
  26. S. K. Goudos and J. N. Sahalos, “Pareto optimal microwave filter design using multiobjective differential evolution,” IEEE Transactions on Antennas and Propagation, vol. 58, no. 1, pp. 132–144, 2010. View at: Publisher Site | Google Scholar
  27. CVX Research Inc: sedumi_1.3 toolbox, 2014,

Copyright © 2020 Zhikun Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.