A HYBRID ALGORITHM FOR SYNTHESIZING LINEAR SPARSE ARRAYS

Designing sparse arrays with fewer antenna elements and lower peak sidelobe level (PSLL) is critical and challenging for antenna arrays in terminal devices in Internet of Things (IoT) systems. This article presents a deep learning method based on denoising diffusion probabilistic models (DDPM) for synthesizing sparse arrays. With predefined constraints such as aperture size, number of elements, and minimum element spacing, a training dataset that enables the proposed DDPM is generated to directly learn the distribution characteristics of these sparse arrays. To simplify the network architecture, the backbone network U-Net is replaced by a multilayer perceptron (MLP). In addition, an innovative conditional generation strategy is introduced to enhance the training accuracy, categorizing the training data into diverse cross-conditional labels based on PSLL and sparse ratio. During the training process, two separate regression networks are employed to predict the sparse ratio and the PSLL, and a Pareto-optimal weighting scheme (PWS) is designed to address the trade-off between them. Examples of two planar sparse arrays and a conformal sparse array demonstrate the effectiveness of the proposed DDPM-based method, highlighting its superiority in reducing PSLL and increasing sparse ratio, reducing inference time compared with the existing methods.

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

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.

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.

Phase-coded waveforms, an important class of radar signals, exhibit low peak sidelobe level (PSL) but lack sufficient Doppler tolerance. Inspired by the excellent Doppler properties of chirp-like signals, researchers have developed Piecewise Polynomial Phase-Coded Waveforms (PPPCW), which model phase using quadratic polynomials within sections, employing different coefficients across intervals. This structure combines the Doppler tolerance of chirp-like signals with the enhanced correlation performance of piecewise design, enabling joint optimization of PSL and Doppler tolerance. However, when segment count increases beyond two, existing models fail to maintain the periodic Doppler tolerance characteristic of chirp-like waveforms, causing this desirable property to rapidly deteriorate with increasing frequency offset. In this paper, we overcome this limitation through systematic analysis of the relationships between polynomial coefficients and Doppler tolerance, leading to an optimization model that integrates tolerance metrics with coefficient constraints to preserve Doppler performance regardless of segment count. By approximating PSL using the Log-Sum-Exp (LSE) function and employing automatic differentiation techniques derived from neural networks, we effectively solve this model via gradient descent methods. Experimental results demonstrate that our proposed model yields waveforms with both low PSL and periodic Doppler tolerance regardless of segment count. Notably, the model readily extends to Multiple-Input MultipleOutput (MIMO) radar applications through simple orthogonality constraints.

This paper presents a kind of particle swarm optimization (PSO) algorithm which is based on simulated annealing (SA) for the synthesis of sparse linear arrays. This algorithm is used to optimize the element positions to suppress the peak side lobe level (PSLL) of the array with many constraints. With this method the freedom increase amounts are used as optimization variables, which plus the minimum element spacing constraint is deemed as element spacing of the array. And the method has downsized the searching region by indirect expression of element spacing. The simulated optimization results demonstrate the better suppression effect of PSLL of the proposed algorithm.

A new method of sparse array synthesis based on quantum search algorithm is proposed. The method ensures the constraints include the number of elements, the array aperture and the minimum element spacing can still be satisfied by setting rational parameters of achimedean spiral reasonably when array elements are distributed along archimedean spiral. It turns the optimization problem of sparse plane array into the optimization of asymmetrical linear arrays, which will reduce the complexity of solving problems greatly. The quantum search algorithm (QSA) is a new global optimization methods, which is inspired by quantum mechanics theories, and has excellent global optimization performance. Nowadays, the application of QSA in sparse plane arrays synthesis is few. The method combines archimedean spiral and the QSA, make the sparse plane array produce lower peak sidelobe level (PSLL). The simulated results show that the approach is high efficiency and robust, and can apply to problem of sparse plane antenna array synthesis with the constraint of minimum element spacing and aperture.

Simple pulsed radar is limited in range sensitivity by the average radiation power and, in range resolution by the pulse length. The design of any radar always involves a compromise between the two constraints. Waveform design aims to seek an appropriate harmony that best suits the relevant application. The pulse compression theory has been introduced in order to get a high range resolution as well as a good detection probability. One of the basic types of pulse compression is binary phase coding which encodes the transmitted pulse with information that is compressed (decoded) in the receiver of the radar. The study of the peak sidelobe level (PSL) binary sequences occurs as a classical problem of signal design for digital communication and, in equivalent guise, in analytic number theory. It has also become a notorious problem of combinatorial optimization. For years mathematicians, engineers, physicists and chemists have sought a systematic way to construct long binary sequences with low PSL. In this Chapter, we describe pulse compression technique in radar waveform design. In order to make the presentation self-contained, we start by providing a short summary of waveform design and an introduction to principle behind pulse compression by compiling the basic tools required for analyzing and comparing different radar signals. After that, we talk about binary sequence, its desired properties and general types of methods for finding and generating such waveforms. We keep on by an overview and introducing the existing methods and search routine done. We conclude the chapter with a brief survey of the results exhibited yet for low autocorrelation binary sequences. We mention a table of complete results presented and also introduce a histogram to unscramble them visually and predict the future.

This letter is concentrated on the peak sidelobe level (PSLL) suppression problem of equal amplitude concentric ring array (CRA), which is constrained by the array aperture, the minimum element spacing, and the number of elements. Taking the minimum PSLL as the optimization objective, the joint optimization strategy of CRA aperture and ring array spacing is adopted to reduce the computational complexity. A quantum genetic algorithm based on hybrid coding is used to optimize the CRA. The proposed method has the advantages of fast searching speed and powerful searching ability. The simulation results demonstrate the superiority of the algorithm.

To lower the peak sidelobe level (PSLL) of sparse concentric ring arrays, a method with multiple design constraints that embed a function model into modified real genetic algorithm (MGA) and select the grid ring radii as optimization individual to synthesize sparse concentric ring arrays is proposed. The multiple constraints include the array aperture, the minimum element spacing, and the number of elements. The proposed method dynamically calculates the ratio of element on each ring, and it has a faster convergence rate than other algorithms. The MGA uses real number to code the optimization variable, and it reduces the complexity of coding and improves the search efficiency. Finally, the results demonstrate the accuracy and effectiveness of the algorithm.

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.

Radar Signal Processing is a very important aspect of Radar systems. Pulse Compression Technique is a Signal Processing Technique used in Radar systems to avail the benefits of large range detection capability of long duration pulse and high range resolution capability of short duration pulse. But at the output of Pulse Compression filter, sidelobes are present and these unwanted outputs may mask a weaker target which is present nearer to a stronger target. Hence, these sidelobes affect the performance of the Radar detection system. In this paper, focus is given on which technique achieves less number of sidelobes, narrow mainlobe width, better range resolution and low Peak Sidelobe Level (PSL) under various Doppler shift, multiple target environment and noise conditions,. For these purposes, the three very important Radar Pulse Compression techniques, Polyphase, Biphase and Linear Frequency Modulated (LFM) Codes are discussed in detail. Simulations are carried out to know the effectiveness of the Codes used in the Pulse Compression Signal Processing Techniques of Radar.

A new class of analytical rectangular thinned arrays with low peak sidelobe level (PSL) is introduced. The proposed synthesis technique exploits binary sequences derived from McFarland difference sets to design thinned layouts on a lattice of P × P(P+2) positions for any prime P. The pattern features of the arising massively-thinned arrangements characterized by only P × (P+1) active elements are discussed and the results of an extensive numerical analysis are presented to assess advantages and limitations of the McFarland-based arrays.

Controlling peak side-lobe level (PSL) is of great importance in\nhigh-resolution applications of multiple-input multiple-output (MIMO) radars.\nIn this paper, designing sequences with good autocorrelation properties are\nstudied. The PSL of the autocorrelation is regarded as the main merit and is\noptimized through newly introduced cyclic algorithms, namely; PSL Minimization\nQuadratic Approach (PMQA), PSL Minimization Algorithm, the smallest Rectangular\n(PMAR), and PSL Optimization Cyclic Algorithm (POCA). It is revealed that\nminimizing PSL results in better sequences in terms of autocorrelation\nside-lobes when compared with traditional integrated side-lobe level (ISL)\nminimization. In order to improve the performance of these algorithms,\nfast-randomized Singular Value Decomposition (SVD) is utilized. To achieve\nwaveform design for MIMO radars, this algorithm is applied to the waveform\ngenerated from a modified Bernoulli chaotic system. The numerical experiments\nconfirm the superiority of the newly developed algorithms compared to\nhigh-performance algorithms in mono-static and MIMO radars.\n

This paper addresses a novel rotationally symmetric technique with multiple constraints for sparse conformal array synthesis. The purpose is to synthesis a sparse optimal common element positions on the conformal surface varying multiple patterns of wide angle scanning with the behavior of low sidelobe levels (SLL). The conformal surface aperture is partitioned into several rotationally symmetric sections. The element positions and element numbers of only one section need to be optimized, which contribute to the reduction of optimizing variables and computation resources. We formulate the synthesis problem as a constrained optimization problem, which takes the peak sidelobe level (PSLL) as the fitness function, and sets the total number of array elements, the minimum spacing between two adjacent elements to form multiple constraints. The Brain Storm Optimization (BSO) is further exploited into the synthesis problem with multiple constraints. A set of representative numerical examples are presented to assess the advantages and effectiveness of the proposed method.