GAs using varied and fixed binary chromosome lengths and real chromosomes for low sidelobe spherical-circular array pattern synthesis

Beam synthesis remains one of the most difficult problems for an antenna array designer, this task being made more difficult when one limits the number of elements to a handful. Pattern synthesis in planar arrays is often achieved by the sampling of a continuous aperture distribution at the element locations. In the case of arrays of limited numbers of elements this produces errors that can unacceptably distort the radiation pattern. When one also then considers the effects of mutual coupling between the antenna elements, the application of ideal element weights derived in this way are no longer valid. In the paper a technique is presented, using the concept of retrodirective beams, that can achieve a low sidelobe beam from a continuous distribution on a circular aperture. The theory is then extended to include a planar array of antenna elements. This is achieved by the addition of retrodirective beams to the uniform beam pattern of the array. Measured data is presented for a small planar array antenna using the derived method. A method for correction of the element weights, to minimise the effects of mutual coupling, producing low sidelobe radiation patterns is then applied. No knowledge of the antenna array's coupling coefficients are required.

This paper presents a pattern synthesis method of a planar array of isotropic antennas using a Genetic Algorithm (GA). Two different beam patterns are generated, one is a pencil beam pattern, and another is a cosec2 pattern. An optimum set of elements amplitude and phases are used to achieve lower Peak Side Lobe Level (PSLL) and ripple. These optimum excitations, which are computed using GA, are discrete in nature, and it's also beneficial to lower the Dynamic Range Ratio (DRR). As the DRR is low, the design complexity of the feed network is reduced.

In this paper, a novel algorithm is devised to reduce the peak side lobe level (PSLL) of the thinned planar antenna (TPA) array at antenna boresight as well as over the desired scan volume without occurrence of grating lobes when the beam is electronically scanned away from the boresight. Randomized symmetrical thinning is incorporated in the proposed algorithm to find the best combination of 'ON' and 'OFF' elements. Modified binary coded genetic algorithm (MBC-GA) is employed in the present study to solve this optimization problem. A 12×12-element TPA array is numerically examined to access the performance of proposed method. Results obtained through this study show that array under consideration renders maximum possible reduction in PSLL and also demonstrate the absence of grating lobes when the beam is scanned to 20 deg. and 40 deg. away from the boresight.

In this paper, we present a novel scheme to train a deep neural network (DNN) for a cosecant-squared (csc2) radiation pattern synthesis speedily and accurately without relying on numerical optimization methods. Our approach is to train a DNN using a physics-informed loss function that minimizes the deviation between the desired shaped beam pattern and the actual one. The physics-informed deep neural network (PIDNN) makes array synthesis effective because it can efficiently achieve two desired features, namely, low sidelobe level (SLL) and small deviation (ripples) in the shaped beam region. To obtain a desired csc2 pattern with the SLL constrained, PIDNN optimizes the excitation amplitude and phase weights of the array elements. To illustrate the effectiveness of the proposed method, the beam pattern with specified characteristics is obtained for the same array by using genetic algorithm (GA) and particle swarm optimization (PSO).

Series FACTS such as series capacitors or phase shifters, by controlling the power flows in a network, can help reduce the flows in heavily loaded lines, resulting in an increased loadability of the network and a reduced cost of production. The influence of multiple phase shifters on the network flows is complex, because they interact on one another, and the problem of the optimal location is essential. This paper proposes an index for measuring the benefit of a given set of phase shifters. The best location for a set of phase shifters is found by a genetic algorithm for a 36 line test case and for the French network.

This paper presents a new scheme based on Weight vector ORthogonal Decomposition (WORD) to control the array response at a given direction and a novel WORD-based approach to pattern synthesis for arbitrary arrays. The central concept of the proposed methods stems from the adaptive array theory. More precisely, it is found that the inverse of the noise-plus-interference covariance matrix in adaptive beamforming can be regarded as a linear combination of two orthogonal projection matrices, and, accordingly, the optimal weight vector is a linear combination of two orthogonal vectors. With such an observation, the WORD scheme is developed to design the desired weight vector. It is shown that the array response at a given direction can be precisely adjusted to an arbitrary level, by simply determining appropriate combination coefficients for those two orthogonal vectors. Furthermore, a closed-form expression of the weight vector can be achieved by introducing a new cost function that measures pattern variation. By employing the WORD scheme successively, a novel approach to pattern synthesis for arbitrary arrays is devised. At each implementation step of this approach, the array pattern is adjusted in a point-by-point manner by successively modifying the weight vector. As such, both the sidelobe and mainlobe regions can be flexibly synthesized. Numerical examples are provided to demonstrate the effectiveness and flexibility of the WORD scheme in array response control at a single direction as well as pattern synthesis.

Pattern synthesis of non-uniform elliptical antenna arrays is presented in this paper. Only the element positions of the antenna arrays are optimized by the combination of differential evolution (DE) and invasive weed optimization (IWO) to reduce the peak side lobe level (PSLL) of the radiation pattern. In order to avoid the overlap of the array elements, the minimum spacing of the adjacent elements is constrained. Also, the beam width of the radiation pattern can be constrained effectively. Three elliptical antenna arrays that have 8, 12, and 20 elements are investigated. The synthesis results show that the introduced method can present a good side lobe reduction for the radiation pattern. Compared with other optimization methods, the method proposed in this paper can obtain better performance.

Radiation pattern of linear arrays of microstrip rectangular antennas, on uniaxial anisotropic substrate is presented with Galerkin method. An optimization procedure based on the genetic algorithm (GA) method is introduced to reduce the side lobe level (SLL), by acting on the law of current amplitude of feed, spacing and phase of a linear array antennas. The results of optimisation are verified shows of narrow beamwidth are achieved.

Array antennas offer a wide range of opportunities in the variation of their directivity patterns through amplitude and phase control. Peak sidelobe levels may be reduced via amplitude control or weighting across the array aperture. Several authors have made significant contributions in detailing processes for synthesizing these aperture amplitude distributions for the purpose of sidelobe level control. One of the basic trade-offs when implementing amplitude weighting functions is that a trade between low sidelobe levels and a loss in main beam directivity always results. Some of the commercially available pattern calculation programs that can implement sidelobe level control do not provide calculations of the aperture efficiencies given different amplitude weightings. Calculation of the aperture efficiency can be somewhat confusing, particularly with regards to the difference between tapering via attenuation versus redistribution. The purpose of this paper is to define these terms, to provide a review of the proper normalization technique that is important in obtaining accurate aperture efficiency estimation. Descriptions of the amplitude tapers and their utility will be presented. A design example will be presented which will compare theoretical efficiencies with those obtained via finite element method simulation.

In this paper, we consider the problem of waveform design for multiple-input multiple-output (MIMO) radars via a peak sidelobe level (PSL) minimization approach. We derive the optimization problem as the PSL and weighted PSL (WPSL) minimization problem. To solve the optimization problem, we consider a suitable product manifold within the unimodular sequence space and develop numerical manifold-based algorithms such as Riemannian gradient descent (RGD), limited memory Riemannian Broyden, Fletcher, Goldfarb, and Shanno (LRBFGS), and Riemannian trust region (RTR) on this manifold. We investigate the performance of the proposed algorithms based on the achieved PSL and WPSL values and the convergence speed. Numerical results show that the proposed algorithms converge faster than other state-of-the-art counterparts and the sequences produced by the proposed algorithms achieve lower (weighted) peak sidelobe levels.

This paper presents a new pattern synthesis algorithm with high radiation efficiency for the transmission antenna array. The synthesis algorithm uses adaptive array theory. By incorporating a quadratic constraint, the desired pattern characteristics, such as low sidelobe level, with high radiation efficiency can be achieved. With the sidelobe level fixed, a different value of Lagrangian multiplier corresponds to a different radiation efficiency.

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

As active electronically steerable arrays become more prominent, a new paradigm of assigning bits to a set of digital phase shifters, necessary for analog and digital beamforming, is proposed. It is well known that a modern day digital phase shifter which is associated with each transmit/receive module typically relies on three to seven bits. Each of these phase shifters provide a desired resolution of 45° to 2.8125°, respectively. When an array is steered with a fixed precision set of phase shifters, there are some angles where results are significantly better than others. For example, six bit phase shifters can provide exact 11.25° differential phases in a linear array, but not 15°, and subsequent quantization errors lead to reduced pointing accuracy and increased sidelobe levels. However, these errors can be minimized if the set of shifters operate collectively instead of independently with “blind” quantization. In particular this study seeks the solution for the following problem: for a set of N phase shifters, each defined by B bits, determine the optimal way to distribute the N×B bits so that the array performance is maximized.

A simple and flexible genetic algorithm (GA) for pattern synthesis of antenna array with arbitrary geometric configuration is presented. Unlike conventional GA using binary coding and binary crossover, this approach directly represents the array excitation weighting vectors as complex number chromosomes and uses decimal linear crossover without a crossover site. Compared with conventional GAs, this approach has a few advantages: giving a clearer and simpler representation of the problem, simplifying chromosome construction, and totally avoiding binary encoding and decoding so as to simplify software programming and to reduce CPU time. This method also allows us to impose constraints on phases and magnitudes of complex excitation coefficients for preferable implementation in practice using digital phase shifters and digital attenuators. Successful applications show that the approach can be used as a general tool for pattern synthesis of arbitrary arrays.

A method of forming a pattern with a deep null by a couple of phases is proposed. The proposed method yields a pattern with a null at a prescribed direction by modifying phases of elements. It has been found that, in order to steer the nulls or deep nulls, it is necessary to adjust a couple of phases. It is pointed out that the pattern modified by a couple of phases cannot form deep nulls in some directions. In that case, the pattern with deep nulls can be steered by adjusting more couples of phases. >