Null Placement Research Articles

Edge Element Controlled Null Steering in Beam-Steered Planar Array

This letter investigates two different methods of null placement in uniformly excited beam-steered planar array. Both methods utilize control of excitation amplitude and phase of edge elements placed along the symmetrical axes of the array. The first method considers additional control in the presence of existing complex excitation, whereas the second method employes an additional control by redefining the excitation of edge elements. Effectiveness of the two methods has been illustrated through design examples of $22\,\times \,22$ and $41\,\times \,47$ planar arrays.

Effect of signal to interference ratio on adaptive beamforming techniques

The capability of adaptive antenna array lies in forming higher gain in the user directions and lower gain in the interferer directions. The technique used to produce such radiation pattern by calculating the excitation weights are called the adaptive beamforming (ABF) techniques. It tries to minimize the error between the desired and actual signal and maximize the signal to interference ratio (SIR). But in severe interference environment when the actual signal is weak, the effect of SIR on the radiation pattern needs to be considered. This paper describes the effect of signal to interference ratio on different adaptive beamforming techniques such as non-blind least mean square (LMS), blind constant modulus algorithm (CMA) and evolutionary Particle Swarm Optimization (PSO). The performance and validation of beamforming algorithms are studied through MATLAB simulation by varying SIR parameters for different desired and interference direction. Different weights are obtained using this beamforming algorithm to optimize the radiation pattern. The parameters for comparison are the main beam and null placement keeping signal to noise (SNR) constant for different angles of user and interferer. The mean SLL and directivity are also studied.

Ant Lion Optimization algorithm to control side lobe level and null depths in linear antenna arrays

The aim of this paper is to introduce the Ant Lion Optimization (ALO) algorithm to the electromagnetics and antenna community. ALO is a new nature-inspired algorithm mimicking the hunting behaviour of antlions. It is used to solve unconstrained as well as constrained optimization problems with diverse search spaces. One of the advantages of ALO is that it has very few parameters to tune, making it a flexible algorithm for solving diverse problems. In this paper, ALO has been applied for antenna current as well as antenna position optimization for pattern synthesis of linear antenna arrays. The potential of ALO to perform linear array beamsteering by optimizing the antenna phases is also illustrated. Different design examples are presented that illustrate the use of ALO for linear array optimization so as to obtain an array pattern with minimum side lobe level (SLL) along with null placement in the specified directions. Close-in SLL minimization for optimal pattern synthesis is also presented. The obtained results are validated by benchmarking with results obtained using other state-of-the-art nature-inspired metaheuristic algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), cat swarm optimization (CSO) and biogeography based optimization (BBO). The results suggest that optimization of linear antenna arrays using ALO provides considerable enhancements compared to the uniform array and the synthesis obtained from other optimization techniques. This demonstrates the potential of ALO as a strong candidate to be used for antenna array synthesis and other electromagnetic optimization problems.

Synthesis of unequally‐spaced linear array using modified central force optimisation algorithm

In this study, a modified version of the central force optimisation (MCFO) algorithm is presented. This modified version is based on the idea of combining the ability of social thinking in particle swarm optimisation (PSO) with the search capability of the original CFO, along with the addition of time-varying acceleration coefficients, to effectively control the global search and enhance the CFO convergence capability. The convergence capability of the MCFO approach is compared with that of other recent evolutionary-based algorithms, using 12 benchmark functions grouped into unimodal and multimodal functions. Furthermore, the MCFO algorithm is considered for the synthesis of unequally-spaced linear arrays with minimum sidelobe levels and/or null placement in certain directions as well as a specified maximum null-to-null beamwidth. The comparison of the simulations of different algorithms shows that the MCFO technique is superior to other evolutionary algorithms such as the genetic algorithm, ant colony optimisation, PSO algorithm, and gravitational search algorithm, as well as other improved CFO algorithms.

Linear antenna array optimization using flower pollination algorithm.

Flower pollination algorithm (FPA) is a new nature-inspired evolutionary algorithm used to solve multi-objective optimization problems. The aim of this paper is to introduce FPA to the electromagnetics and antenna community for the optimization of linear antenna arrays. FPA is applied for the first time to linear array so as to obtain optimized antenna positions in order to achieve an array pattern with minimum side lobe level along with placement of deep nulls in desired directions. Various design examples are presented that illustrate the use of FPA for linear antenna array optimization, and subsequently the results are validated by benchmarking along with results obtained using other state-of-the-art, nature-inspired evolutionary algorithms such as particle swarm optimization, ant colony optimization and cat swarm optimization. The results suggest that in most cases, FPA outperforms the other evolutionary algorithms and at times it yields a similar performance.

Optimal Pattern Synthesis of Linear Antenna Array Using Grey Wolf Optimization Algorithm

The aim of this paper is to introduce the grey wolf optimization (GWO) algorithm to the electromagnetics and antenna community. GWO is a new nature-inspired metaheuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. It has potential to exhibit high performance in solving not only unconstrained but also constrained optimization problems. In this work, GWO has been applied to linear antenna arrays for optimal pattern synthesis in the following ways: by optimizing the antenna positions while assuming uniform excitation and by optimizing the antenna current amplitudes while assuming spacing and phase as that of uniform array. GWO is used to achieve an array pattern with minimum side lobe level (SLL) along with null placement in the specified directions. GWO is also applied for the minimization of the first side lobe nearest to the main beam (near side lobe). Various examples are presented that illustrate the application of GWO for linear array optimization and, subsequently, the results are validated by benchmarking with results obtained using other state-of-the-art nature-inspired evolutionary algorithms. The results suggest that optimization of linear antenna arrays using GWO provides considerable enhancements compared to the uniform array and the synthesis obtained from other optimization techniques.

Cognitive null steering in frequency diverse array radars

Null steering has been a challenge in radar communications for the past few decades. In this paper, a novel cognitive null steering technique in frequency diverse array radars using frequency offset selection is presented. The proposed system is a complete implementable framework that provides precise and deep null placement in the range and angle locations of the interference source. The proposed system is cognitive such that the transmitter and receiver are connected via a feedback loop. System extracts interference source location parameters from the radar scene using Multiple Signal Classification, a super resolution direction of arrival estimation technique. Neural networks known for minimum computation time, and good non-linear and non-parametric approximation have been utilized for prediction of next location of the interference source. Simulation results validate the proposed frequency offset selection by demonstrating precise and deep nulls at the desired locations.

QPSO FOR FAILURE CORRECTION OF LINEAR ARRAY ANTENNA INCLUDING WIDE NULL PLACEMENT

In this paper, the authors have proposed a method based on Quantum Particle Swarm Optimization (QPSO) algorithm in the context of radiation pattern correction of a linear array of isotropic antennas corrupted with one or more faulty antenna elements. Care is taken to maintain the values of side lobe level and maximum Wide Null Placement of the corrected pattern to be identical to the values of nondefective radiation pattern. This correction is made possible by altering the beam weights of the remaining elements in the array. The advantage of this method is that the necessity of replacement of the faulty elements is eliminated. Simulation is done on the linear antenna array constructed of individual isotropic elements separated by identical inter-element spacing and the results obtained from the simulation depict the effectiveness of the proposed method. This method can also be extended to other array geometries.

GPS Jamming: Strengthening Anti Jam GPS System with Adaptive Phase Only Nulling Using Cuckoo Search

Global Positioning System (GPS) signals are spread spectrum, modulated with very low average power caused by background noise. Signal received is mostly 165 dB down than thermal noise level. Such signals are easily jammed either through intentional noise sources (jammer) or unintentionally from broadcasting stations harmonics or other out of band sources. This study proposes to reveal how a nulling antenna with adaptive spatial filtering technique can efficiently mitigate intentional and non-intentional interference. A beam forming antenna array is an antennas set whose outputs are weighted by complex values and combined form array output. The complex valued weights effect is to steer the array pattern’s main lobes to desired directions which may be unknown and hence antenna weights are adjusted adaptively till some array performance measure is improved, revealing proper lobe or null placement. This study proposes a Cuckoo Search (CS) algorithm based new optimization method for multiple interference cancellation design that increases gain to desired signal and improves jamming rejection performance.

Correction of Faulty Sensors in Phased Array Radars Using Symmetrical Sensor Failure Technique and Cultural Algorithm with Differential Evolution

Three issues regarding sensor failure at any position in the antenna array are discussed. We assume that sensor position is known. The issues include raise in sidelobe levels, displacement of nulls from their original positions, and diminishing of null depth. The required null depth is achieved by making the weight of symmetrical complement sensor passive. A hybrid method based on memetic computing algorithm is proposed. The hybrid method combines the cultural algorithm with differential evolution (CADE) which is used for the reduction of sidelobe levels and placement of nulls at their original positions. Fitness function is used to minimize the error between the desired and estimated beam patterns along with null constraints. Simulation results for various scenarios have been given to exhibit the validity and performance of the proposed algorithm.

Hybrid evolutionary algorithm based beamforming for smart antenna system

In this paper, a Hybrid evolutionary algorithm is applied to control the smart antenna patterns satisfying certain constrains like steering the main beam towards a signal of interest, placement of deep nulls in the directions of undesired signals, etc. In adaptive beamforming arrays, using Least Mean Square (LMS) algorithm alone suffered the problem of getting stuck at local minima and converging after large number of iterations. A Genetic algorithm (GA) is a global optimisation technique. The combination of LMS and GA can explore the large optimisation function surface than the individual resulting in faster convergence to global minimum. Simulation results are presented to illustrate the performance of the Hybrid approach.

Pattern Synthesis of Nonuniformly Spaced Arrays Based on Unit Circle Representation and Taguchi Method

Unit circle representation techniques such as Schelkunoff null placement method have been extensively used to synthesize uniformly spaced linear arrays. In this work, it is attempted to extend the unit circle representation approach to synthesize nonuniformly spaced linear arrays with some desired null directions and minimum achievable sidelobe level. The problem is formulated as an optimization task upon which Taguchi method is employed to solve for the set of element positions. Illustrative examples are included to demonstrate the design effectiveness and flexibility for modern wireless communication system applications.

Phased arrays in communication system based on Taguchi‐neural networks

SUMMARYPhased antenna array design is one of the most important electromagnetic optimization problems. This research combined the Taguchi method and artificial intelligence methods, used them as the prediction tool in designing parameters for the communication system, and then constructed a set of the optimal parameter analysis flow and steps. In this paper, we present an application of artificial neural networks in the electromagnetic domain. We particularly look at the multilayer perceptron network, which has been the most used of artificial neural networks architectures both in the electromagnetic domain and in the Taguchi optimization technique and describes the Taguchi method to optimize the excitations elements of the linear array to produce a radiation pattern with minimum side lobe level and null placement control. This paper investigates how the implementation of the signal processing in hardware affects the performance of the adaptive array antenna. The investigation is confined to uplink or receive antenna array only. Results of a prototype of antenna array with feeding values designed using the proposed techniques are also presented. Copyright © 2013 John Wiley & Sons, Ltd.

Optimization of Antenna Arrays Using Different Strategies Based on Taguchi Method

In this paper, Taguchi method, which is a relatively novel optimization technique, is employed. Based on the concept of the orthogonal array, Taguchi method effectively reduces the number of tests required in an optimization process. This method has been successfully applied in many fields such as chemical engineering, mechanical engineering, IC manufacturing, power electronics, etc. In this work, the Taguchi method is applied to optimize linear array antennas for null placement and sidelobe level reduction. The optimization process is carried out by controlling the array parameters including specifically the element excitations (amplitude and phase) and positions.

Optimized hermetic transform beam-forming of acoustic arrays via cascaded spatial filter arrangements derived using a chimerical evolutionary genetic algorithm

Hermetic transforms are complex matrices, having particular mathematical properties, that have recently been introduced to the field of acoustic array signal processing. Cascade sequences of Hermetic transform matrices have been shown to have direct utility in accomplishing spatial filtering and beam-forming of data from oversampled arrays. The present work details the adaptation of techniques previously shown to be successful in the processing of radio-wave phased-array antenna systems [Woodsum et al., 16th International Conference on Cognitive and Neural Systems (2012)] to the processing of sampled digital data from acoustic arrays. As in our earlier work, the use of a Chimerical, Evolutionary, Genetic Algorithm, having a “feature seeking” fitness function, is retained, for deriving optimal multiplicative arrangements of non-commuting elemental transform matrices. Each elemental matrix represents a spatial “pole” or “zero,” and cascaded arrangements of these are utilized to create a desired spatial pattern response for the array. In terms of acoustic reception, the technique is especially successful in dealing with null placement in order to mitigate large numbers of interfering signals, and in achieving super-resolution beams for arrays that are “acoustically small.” Experimental results are compared to theoretical predictions of performance.

Optimal synthesis of linear antenna array with composite differential evolution algorithm

Linear antenna array design is one of the most important electromagnetic optimization problems of current interest. This article describes the application of a recently develop metaheuristic algorithm, known as the composite differential evolution (CoDE), to optimize a spacing between the elements of the linear element that provides a radiation pattern with minimum side lobe level and null placement in the specified direction. The CoDE has been used to solve three difficult instances of the design problem, and the optimization goal in each example is easily achieved. The experimental results of the CoDE algorithm have been shown to be better than the recently published results obtained using other state-of–the-art metaheuristics like DE, jDE, SaDE, JADE, BBO, GA, PSO and ES in a statistically meaningful way.

Parameters Analysis to Pattern Null Placement Based on the Cross Entropy Method

Null Placement of array antenna can eliminate certain direction interference. A Cross-Entropy method which uses the importance sampling strategies is presented to create nulls. The population samples of the method are selected from a parameterized probability distribution and updated during the optimization procedure, so the diversity of the samples and samples set is approach to optimization results. Null placement to six elements of array antenna is simulated by the method, and some parameters of CE method are analyzed. Numerical results indicate that it is presented more effective method to array antenna optimum design. Both the premature phenomenon is overcome effectively and the computing speed is accelerated rapidly.

Linear antenna array synthesis with modified invasive weed optimisation algorithm

Linear antenna array design is one of the most important electromagnetic optimisation problems of current interest. This article describes the application of modified version of a recently developed metaheuristic algorithm, known as the invasive weed optimisation (IWO), to optimise the spacing between the elements of the linear array to produce a radiation pattern with minimum side lobe level and null placement control. IWO is a novel ecologically inspired algorithm that mimics the process of weeds colonisation and distribution and is capable of solving general multi-dimensional, linear and non-linear optimisation problems with appreciable efficiency. For this application, we modified the classical IWO by introducing a more explorative routine of changing the mutation step-size with iterations. Three design examples are presented that illustrate the use of the IWO algorithm, and the optimisation goal in each example is easily achieved. The results of the modified IWO algorithm have been shown to meet or beat the results obtained using other state-of-the-art metaheuristics like the classical IWO, genetic algorithm (GA), particle swarm optimisation (PSO), memetic algorithms (MA), and tabu search (TS) in a statistically meaningful way.

Pareto optimal synthesis of the linear array geometry for minimum sidelobe level and null control during beam scanning

In this work, synthesis of the linear array geometry is put forward as a constrained vector optimization problem whose components are to meet the minimum sidelobe level (SLL) and control of the wide/narrow null placement during beam scanning whose range can vary from zero to the wide bands. As these synthesis objectives generally conflict with each other, nondominated solutions are acquired using the Nondominated Sorting Genetic Algorithm- II (NSGA-II) as a fast nondominated genetic sorting algorithm. Then, the Pareto frontiers are obtained using these trade-off solution sets between the maximum SLL, null control, and scanning range to provide a view of all design options. Thus, the pattern features resulted from these Pareto frontiers are valid for any chosen main beam direction within its full prescribed beam scanning range. The same Pareto optimal synthesis procedure can be applied to a thinned linear antenna array. A thinned linear antenna array is obtained by a simple genetic optimization by rounding the excitation amplitudes either to 1 or 0 to minimize the maximum of side lobe level (MSLL) during the beam scanning within the prescribed region as stated in the multiobjective function. Finally, some typical Pareto optimal radiation patterns of the scanning arrays are synthesized with only perturbating the positions of the array elements, and their full electromagnetic wave simulations are also completed to examine the resulted mutual coupling effects between the elements of the arrays. It can be concluded that the Pareto optimal synthesis procedure gives the scanning linear antenna arrays with successful radiation performance. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.

LINEAR ARRAY SYNTHESIS USING BIOGEOGRAPHY BASED OPTIMIZATION

This paper presents a novel optimization technique biogeography based optimization (BBO) for antenna array synthesis. BBO is a relatively new evolutionary global optimization technique based on the science of biogeography. It is capable of solving linear and non-linear problems. In this paper, BBO algorithm is used to determine an optimum set of amplitudes of antenna elements that provide a radiation pattern with maximum side lobe level reduction and/or null placement in the specifled directions. The results obtained show the efiectiveness of the BBO algorithm, and they are better than previous published results.