Nonuniform Linear Antenna Arrays Research Articles

Sparse Non-Uniform Linear Array-Based Propagator Method for Direction of Arrival Estimation

A novel approach that does not require the number of sources as a priori is proposed to estimate the direction of arrival (DOA) based on a sparse non-uniform linear antenna array. To ensure the identifiability of the DOA, a specific configuration scheme of sparse array is designed. Based on this specific sparse array, firstly the fourth-order cumulant (FOC) is adopted to eliminate the impact imposed by Gaussian noise. Secondly, to circumvent eigenvalue decomposition or singular value decomposition, a propagator is constructed by using a Hermitian FOC matrix and a hyperparameter. Finally, a projection onto an irregular Toeplitz set is proposed to further improve estimation accuracy.

Pattern synthesis of nonuniform linear antenna array based on FFDM

To optimize the number, spacing and excitation of nonuniform linear array at the same time, a Fourier basis filter diagonalization method (FFDM) is proposed in this paper. By constructing a set of Hamiltonian operator functions that can be decomposed by orthogonal eigenvectors, the synthesis of nonuniform linear arrays is transformed into a generalized eigenvalue decomposition under Fourier basis. The introduction of Fourier basis can improve the computational rate. The least square method (LSM) is used to solve the array element excitation, which improves the matching accuracy. A number of numerical simulations shown that the proposed algorithm can improve the accuracy of the array factor (AF) pattern of many types of practical arrays reconstruction.

Application of Differential Geometry to the Array Manifolds of Linear Arrays in Antenna Array Processing

This article deals with the application of differential geometry to the array manifolds of non-uniform linear antenna array (NULA) when estimating the direction of arrival (DOA) of multiple sources present in an environment using far field approximation. In order to resolve this issue, we utilized a doublet linear antenna array (DLA) comprising two individual NULAs, along with a proposed algorithm that chooses correct directions of the impinging sources with the help of the prior knowledge of the ambiguous directions calculated with the application of differential geometry to the manifold curves of each NULA. The algorithm checks the correlation of the estimated direction of arrival (DOAs) by both the individual NULA with its corresponding ambiguous set of directions and chooses the output of the NULA, which has a minimum correlation between their estimated DOAs and corresponding ambiguous DOAs. DLA is designed such that the intersection of all the ambiguous set of DOAs among the individual NULAs are null sets. DOA of sources, which imping signals from different directions on the DLA, are estimated using three direction finding (DF) techniques, such as, genetic algorithm (GA), pattern search (PS), and a hybrid technique that utilizes both GA and PS at the same time. As compared to the existing techniques of ambiguity resolution, the proposed algorithm improves the estimation accuracy. Simulation results for all the three DF techniques utilizing the DLA along with the proposed algorithm are presented using MATLAB. As compared to the genetic algorithm and pattern search, the intelligent hybrid technique, such that, GA–PS, had better estimation accuracy in choosing corrected DOAs, despite the fact that the impinging DOAs were from ambiguous directions.

RFDOA-Net: An Efficient ConvNet for RF-Based DOA Estimation in UAV Surveillance Systems

This paper presents a convolution neural network (CNN)-based direction of arrival (DOA) estimation method for radio frequency (RF) signals acquired by a nonuniform linear antenna array (NULA) in unmanned aerial vehicle (UAV) localization systems. The proposed deep CNN, namely RFDOA-Net, is designed with three primary processing modules, such as collective feature extraction, multi-scaling feature processing, and complexity-accuracy trade-off, to learn the multi-scale intrinsic characteristics for multi-class angle classification. In several specific modules, the regular convolutional and grouped convolutional layers are leveraged with different filter sizes to enrich diversified features and reduce network complexity besides adopting residual connection to prevent vanishing gradient. For performance evaluation, we generate a synthetic signal dataset for DOA estimation under the multipath propagation channel with the presence of additive noise, propagation attenuation and delay. In simulations, the effectiveness of RFDOA-Net is investigated comprehensively with various processing modules and antenna configurations. Compared with several state-of-the-art deep learning-based models, RFDOA-Net shows the superiority in terms of accuracy with over 94% accuracy at 5 dB signal-to-noise ratio (SNR) with cost-efficiency.

Multi-population and dynamic-iterative cuckoo search algorithm for linear antenna array synthesis

Nature inspired algorithms have served as the backbone of modern computing technology and over the past three decades, the field has grown enormously. CS is a recent addition to optimization computing and suffers from problem of local optima stagnation and poor exploration. This paper presents a new version of CS namely extended CS (ECS) by employing the concept of multi-population adaptation, adaptive switching, dynamic iterative search, and GWO inspired global search phase, for solving benchmark problems and real-world design problem of linear antenna array (LAA) optimization. The algorithm aims at providing a better framework for optimization problems by keeping the original structure of CS intact. For performance evaluation, the algorithm has been firstly applied on two highly challenging datasets namely CEC 2015 and CEC 2017 benchmark problems and performance evaluation is done in comparison with other algorithms. Further to test the ECS algorithm on real world application, it is applied for synthesis of uniform and non-uniform LAA. Experimentally, the ECS algorithm is found to provide better performance in comparison to basic CS and others state-of-the-art algorithms. Statistical tests and radiation patterns further validate the results.

On the improvement in grey wolf optimization

Grey wolf optimization (GWO) is a recently developed nature-inspired global optimization method which mimics the social behaviour and hunting mechanism of grey wolves. Though the algorithm is very competitive and has been applied to various fields of research, it has poor exploration capability and suffers from local optima stagnation. So, in order to improve the explorative abilities of GWO, an extended version of grey wolf optimization (GWO-E) algorithm is presented. This newly proposed algorithm consists of two modifications: Firstly, it is able to explore new areas in the search space because of diverse positions assigned to the leaders. This helps in increasing the exploration and avoids local optima stagnation problem. Secondly, an opposition-based learning method has been used in the initial half of iterations to provide diversity among the search agents. The proposed approach has been tested on standard benchmarking functions for different population and dimension sizes to prove its effectiveness over other state-of-the-art algorithms. Experimental results show that the GWO-E algorithm performs better than GWO, bat algorithm, bat flower pollinator, chicken swarm optimization, differential evolution, firefly algorithm, flower pollination algorithm (FPA) and grasshopper optimization algorithm. Statistical testing of GWO-E has been done to prove its significance over other popular algorithms. Further, as a real-world application, the GWO-E is used to design non-uniform linear antenna array (LAA) for minimum possible sidelobe level and null control. Performance of GWO-E for the synthesis of LAA is evaluated by considering the several different case studies of LAA that exists in the literature, and the results are compared with the results of other popular meta-heuristic algorithms like genetic algorithm, ant lion algorithm, FPA, cat swarm optimization, GWO and many more. Numerical results further show the superior performance of GWO-E over original GWO and other popular algorithms.

Optimization of Nonuniform Linear Antenna Array Topology

This paper deals with the use of a Nonuniform Linear Antenna array (NLA) for determining the Directions of Arrival (DOA) of a signal in 2450MHz frequency band. First, the principle of the DOA estimation method is described for the case of the MUSIC method. This paper also discusses the possibilities of optimizing the position of antenna elements in the NLA configuration, which are performed in analytical solutions and simulations. The simulation results are compared to the analytical results to obtain optimal NLA configurations for determining the signal DOA. Simulation results show that the probability of resolution and accuracy in determining the signal DOA are dependent on the antenna array aperture essentially. Furthermore, the realized NLA configuration was verified by an experimental measurement. The obtained experiment results demonstrate that the applied MUSIC method for NLA configuration is suitable and also highly accurate in determining the signal DOA, which was verified.

A Crossover Improved Genetic Algorithm and Its Application in Non-Uniform Linear Antenna Arrays

Genetic Algorithm (GA) is a widely used optimization technique with multitudinous applications. Improving the performance of the GA would further augment its functionality. This paper presents a Crossover Improved GA (CIGA) that emulates the motion of fireflies employed in the Firefly Algorithm (FA). By employing this mimicked crossover operation, the overall performance of the GA is greatly enhanced. The CIGA is tested on 14 benchmark functions conjointly with the other existing optimization techniques to establish its superiority. Finally, the CIGA is applied to the practical optimization problem of synthesizing non-uniform linear antenna arrays with low side lobe levels (SLL) and low beam width, both requirements being incompatible. However, the proposed CIGA applied for the synthesis of a 12 element array yields an SLL of [Formula: see text]29.2[Formula: see text]dB and a reduced beam width of 19.1[Formula: see text].

Analysis of Linear Antenna Array for minimum Side Lobe Level, Half Power Beamwidth, and Nulls control using PSO

This paper presents the optimization performance of non-uniform linear antenna array with optimized inter-element spacing and excitation amplitude using Particle Swarm Optimization (PSO). The aim of the proposed algorithm is to obtain the optimum values for inter-element spacing and excitation amplitude for a linear antenna array in a give n radiation pattern with suppressed Side Lobe Level (SLL), minimum Half Power Beamwidth (HPBW), improved directivity and placement of nulls in the desired direction. A vari ety of design examples are considered and the obtained results using PSO are validated by benchmarking with results obtained using other nature-inspired meta-heuristic algorithms such as the Real-coded Genetic Algorithm (RGA) and the Biogeographic Based Optimization (BBO) algorithm. The comparative results are show n that optimization of linear antenna array using the PSO provides considerable enhancement in the SLL, the HPBW, the directivity and the null control in the desired direction.

Pattern Synthesis of Linear Antenna Arrays Using Enhanced Flower Pollination Algorithm

In this paper, a new variant of flower pollination algorithm (FPA), namely, enhanced flower pollination algorithm (EFPA), has been proposed for the pattern synthesis of nonuniform linear antenna arrays (LAA). The proposed algorithm uses the concept of Cauchy mutation in global pollination and enhanced local search to improve the exploration and exploitation tendencies of FPA. It also uses dynamic switching to control the rate of exploration and exploitation. The algorithm is tested on standard benchmark problems and has been compared statistically with state of the art to prove its worthiness. LAA design is a tricky and difficult electromagnetic problem. Hence to check the efficacy of the proposed algorithm it has been used for synthesis of four different LAA with different sizes. Experimental results show that EFPA algorithm provides enhanced performance in terms of side lobe suppression and null control compared to FPA and other popular algorithms.

Non-Uniform Linear Antenna Array Design and Optimization for Millimeter-Wave Communications

In this paper, we investigate the optimization of non-uniform linear antenna arrays (NULAs) for millimeter wave (mmWave) line-of-sight (LoS) multiple-input multiple-output (MIMO) channels. Our focus is on the maximization of the system effective multiplexing gain (EMG), by optimizing the individual antenna positions in the transmit/receive NULAs. Here the EMG is defined as the number of signal streams that are practically supported by the channel at a finite SNR. We first derive analytical expressions for the asymptotic channel eigenvalues with arbitrarily deployed NULAs when, asymptotically, the end-to-end distance is sufficiently large compared to the aperture sizes of the transmit/receive NULAs. Based on the derived expressions, we prove that, the asymptotically optimal NULA deployment that maximizes the achievable EMG should follow the groupwise Fekete-point distribution. Specifically, the antennas should be physically grouped into K separate uniform linear antenna arrays (ULAs) with the minimum feasible antenna spacing within each ULA, where K is the target EMG to be achieved; in addition, the centers of these K ULAs follow the Fekete-point distribution. We numerically verify the asymptotic optimality of such an NULA deployment and extend it to a groupwise projected arch type (PAT) NULA deployment, which provides a more practical option for mmWave LoS MIMO systems with realistic non-asymptotic configurations. Numerical examples are provided to demonstrate a significant capacity gain of the optimized NULAs over traditional ULAs.

Optimum design of non-uniform symmetrical linear antenna arrays using a novel modified invasive weeds optimization

Abstract This paper presents a new modified method for the synthesis of non-uniform linear antenna arrays. Based on the recently developed invasive weeds optimization technique (IWO), the modified invasive weeds optimization method (MIWO) uses the mutation process for the calculation of standard deviation (SD). Since the good choice of SD is particularly important in such algorithm, MIWO uses new values of this parameter to optimize the spacing between the array elements, which can improve the overall efficiency of the classical IWO method in terms of side lobe level (SLL) suppression and nulls control. Numerical examples are presented and compared to the existing array designs found in the literature, such as ant colony optimization (ACO), particle swarm optimization (PSO), and comprehensive learning PSO (CLPSO). Results show that MIWO method can be a good alternative in the design of non-uniform linear antenna array.

Performance Optimization of DOA Estimation in Non-Uniform Linear Antenna Array by PSO Algorithm

In order to estimate the coherent source, a modified multiple signal classification (MUSIC) algorithm is introduced. And a novel arrangement method for the non-uniform linear array by particle swarm optimization (PSO) algorithm is proposed. This method needs merely a signal source whose direction-of-arrival (DOA) has been exactly known. The proposed method has a simple processing and a strong stabilization. It could be applied to optimized arbitrary array configuration. The simulation verifies that the performance of DOA estimation is improved effectively, which has proved the validity of the proposed method.

Synthesis of Non-Uniform Amplitude equally Spaced Antenna Arrays Using PSO and DE Algorithms

In recent years Evolutionary computation has its growth to extent. Synthesis of non-uniform linear antenna arrays is one of the most important electromagnetic optimization problems of the current interest. In this paper, the design of non-uniform linear antenna arrays with optimum side lobe level reduction is investigated. Two global evolutionary optimization methods are Particle Swarm Optimization algorithm(PSO) and Differential Evolution algorithm(DE ) are used to determine an optimum set of weights and positions that provide a radiation pattern with optimum side lobe level having uniform progressive phase between the elements. Simulation results show considerable enhancements in array performances using the global optimizers. The paper finally illustrates a comparative evaluation of the two proposed algorithms regarding their applicability as numerical optimization techniques.

Design of Non-Uniform Antenna Arrays Using Genetic Algorithm

The performance of a single-element antenna is somewhat limited. To obtain high directivity, narrow beamwidth, low side-lobes, point-to-point and preferred-coverage pattern characteristics, etc., antenna arrays are used. An antenna array is an assembly of individual radiating antennas in an electrical and geometrical configuration. Nowadays, antenna arrays appear in wireless terminals and smart antennas, so robust and efficient array design is increasingly becoming necessary. In antenna array design, it is frequently desirable to achieve both a narrow beamwidth and a low side-lobe level. In linear antenna arrays, a uniform array (having uniform inter-element spacing and uniform amplitude excitation) yields the smallest beamwidth and hence the highest directivity. It is followed, in order, by the Dolph-Chebyshev and Binomial arrays. In contrast, Binomial arrays usually possess the smallest side-lobes followed, in order, by the Dolph-Chebyshev and uniform arrays. Binomial and Dolph-Chebyshev arrays are typical examples of non-uniform arrays. Non-uniform linear antenna arrays have uniform inter-element spacing and non-uniform amplitude distributions. In this paper we deal only with linear arrays and it is shown that using genetic algorithm it is possible to design a non-uniform array that approximates the beamwidth of a uniform array and having smaller side-lobe level than the Dolph-Chebyshev array. The result is that the designed antenna array exhibits the largest directivity as compared to the uniform, Binomial and Dolph-Chebyshev arrays. In the design, the genetic algorithm is employed to generate the excitation amplitudes of the antenna array.

Virtual Antenna Array Analysis for MIMO Synthetic Aperture Radars

Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) that employs multiple antennas to transmit orthogonal waveforms and multiple antennas to receive radar echoes is a recently proposed remote sensing concept. It has been shown that MIMO SAR can be used to improve remote sensing system performance. Most of the MIMO SAR research so far focused on signal/data models and corresponding signal processing algorithm. Little work related to MIMO SAR antenna analysis can be found. One of the main advantages of MIMO SAR is that the degrees of freedom can be greatly increased by the concept of virtual antenna array. In this paper, we analyze the virtual antenna array for MIMO SAR high-resolution wide-swath remote sensing applications. The one-dimensional uniform and nonuniform linear antenna arrays are investigated and their application potentials in high-resolution wide-swath remote sensing are introduced. The impacts of nonuniform spatial sampling in the virtual antenna array are analyzed, along with a multichannel filtering-based reconstruction algorithm. Conceptual system and discussions are provided. It is shown that high operation flexibility and reconfigurability can be obtained by utilizing the virtual antenna arrays provided by the MIMO SAR systems, thus enabling a satisfactory remote sensing performance.

Characterisation of ultra-wideband antenna arrays with spacings following a geometric progression

An ultra-wideband (UWB) antenna array is designed to satisfy given constraints in a (large) contiguous frequency range. Space limitations are tight in many applications and it is often infeasible to design the antenna array with preferred element spacings of λ/2 at the centre frequency. In this study, the authors compare uniform linear UWB antenna arrays and non-uniform reduced-aperture UWB antenna arrays with more compact element spacings following a geometric progression. The authors analyse and discuss performance degradations with respect to irregularly spaced monopoles. The manufactured non-uniform linear UWB antenna arrays are characterised by measurements in an anechoic chamber. The results are correlated with the arrayfactor and compared with measured data of uniform linearly spaced monopoles in the time and frequency domains. It is found that a compact non-uniform linear UWB array with element spacings following a geometric progression can be designed such that its main lobe performs very close to that of a uniform linear array, with an antenna array size reduction of 23.6% for a seven-element non-uniform linear UWB array.

A NEW METHOD FOR THE SYNTHESIS OF NON-UNIFORM LINEAR ARRAYS WITH SHAPED POWER PATTERNS

Antenna arrays with shaped power patterns have many applications in communications and radars. Many antenna array synthesis techniques for shaped patterns have been developed in the past years, and most of them deal only with uniformly spaced arrays. In this paper, a new method is proposed for the synthesis of nonuniform linear antenna arrays with shaped power patterns. The proposed synthesis method consists of three steps. First, we flnd a satisfactory power pattern for the required radiation characteristics by solving a constrained least-squares problem which is obtained with the help of non-redundant representation of squared magnitude of a linear array factor. Then, we factorize the polynomial associated with the power pattern by using polynomial rooting, and consequently obtain the corresponding fleld patterns. Finally, the forward-backward matrix pencil method is used to obtain a nonuniform linear array with optimized excitation magnitudes, phases and locations for a speciflc choice of fleld patterns. The synthesized array has a smaller number of elements than the one with uniformly spaced elements for the same

Location and Imaging of Moving Targets using Nonuniform Linear Antenna Array SAR

By using a linear antenna array, velocity synthetic aperture radar (VSAR) can detect, focus, and locate slowly moving targets well. However, it may mis-locate fast moving targets in the azimuth (cross-range) direction. In this correspondence, we propose a synthetic aperture radar (SAR) with a nonuniform linear antenna array and give a design of the antenna arrangement. It is shown that our proposed nonuniform linear antenna array SAR (NULA-SAR) can locate both slowly and fast moving targets correctly. An integrated NULA-SAR algorithm for moving target imaging is also presented, and it is verified by some simulations.

Nonuniform linear antenna arrays minimising Cramer–Rao bounds for joint estimation of single source range and direction of arrival

Different antenna placements in nonuniform linear arrays for the joint estimation of the range and direction-of-arrival (DOA) of one source are considered. It is shown that the Cramer-Rao bound (CRB) for the range and DOA estimates can be expressed as a function of the angles at which the source sees the antennas. This new formulation of the CRB easily reveals the geometrical properties that the array should have in order to minimise the CRB. Based on the new representation of the CRB, a result found in the literature (which was concerned with the special case of a number of antennas that is divisible by four) is generalised to include the case of an arbitrary number of antennas. Furthermore, it is shown that symmetric arrays are close to uniformly optimal for different DOAs when the range is much larger than the aperture of the arrays.