Peak Sidelobe Research Articles

An optimised delay multiply and sum ultrasonic imaging algorithm based on sparse arrays and parallel computing

ABSTRACT The Total Focusing Method (TFM) focuses pixels using the Delay and Sum (DAS) beamforming technique, which relies solely on the temporal information of the full matrix capturing dataset while ignoring its spatial information, and the image resolution and contrast achievable with TFM are limited. In this work, a parallel sparse delay multiply and sum (PSDMAS) focusing imaging algorithm based on sparse arrays and parallel computing is proposed to improve contrast resolution and imaging efficiency. A sparse array optimisation method is applied to reduce the amount of data. A ratio of main-lobe width and side-lobe peak was constructed as the fitness function and a genetic algorithm was used to find the optimal solution for the array arrangement. Delay Multiply and Sum (DMAS) was employed to enhance the spatial coherence and suppress the clutter artefacts. Parallel computing strategies were implemented to improve imaging efficiency. To validate the effectiveness of the algorithm, we processed the full matrix data collected from simulations and experiments using PSDMAS, the imaging results of the PSDMAS provided a considerable improvement in Array Performance Indicator (API), and better lateral spatial resolution was also achieved. The computation time of PSDMAS was reduced by 99.9% compared to conventional DMAS.

Simultaneous Multibeam Clustered Phased Arrays Analysis Using Mixed and Multiple Antenna Element Factors.

A novel design strategy for improving the radiative performance of simultaneous multibeam (SMB) phased arrays is addressed. The proposed scheme relies on the adoption of mixed and multiple antenna element factors with a dynamic selection of their radiation patterns whose choice depends on the desired SMB pointing directions. In addition, a Penrose-inspired clustering technique is also employed for reducing the array feed points. Compared with traditional phased arrays based on a single antenna element factor, the novel array architecture allows the scan angle range to be widened by improving the minimum array gain as well as reducing the peak side lobe level (PSLL). The superior radiative performance of the proposed approach with respect to the clustered phased arrays with a single-mode element factor is assessed in SMB scenarios comprising two and three main lobe peaks. The notable SMB radiative improvement has been also confirmed from a statistical point of view by considering up to four and five concurrent main lobes. The remarkable radiative improvements confirm the effectiveness of the proposed solution, which also represents an appealing candidate for its exploitation in multiuser and multibeam communications.

On the use of autocorrelation properties for diffuse reflection by coding metasurfaces

Abstract One-bit coding metasurfaces combine two basic unit cells with out-of-phase responses. Their potential in achieving diffuse scattering has already been demonstrated. These metasurfaces can subsequently be applied to radar-signature control. This paper presents a theoretical analysis linking the scattered field to the autocorrelation of the code that encodes the metasurface. This analysis leads to a focus on Minimum Peak Sidelobes codes with autocorrelation characteristics similar to the unit impulse. Advances in other research areas have greatly enhanced the search for these kind of codes, making them directly usable for coding diffuse scattering metasurfaces. This approach is compared with existing codes, specifically examining how it performs against the optimal code found through exhaustive search in small-scale scenarios. Then, it is shown that this coding strategy facilitates the design of metasurfaces with any and large electrical sizes, achieving results comparable to those obtained through optimization-based approaches, at a significantly reduced computational workload.

A sidelobe level control algorithm for wide-beam power gain pattern synthesis via array antenna

The paper tries to maximize the minimum power gain in a wide-beam mainlobe with a controllable peak sidelobe level (PSLL) by solving the power gain pattern synthesis (PGPS) problem. Since the PGPS problem is nonconvex, it cannot be solved effectively in polynomial time. In this paper, the nonconvex PGPS problem is simplified by converting it into two nonconvex subproblems. The first subproblem's solution space is divided into a finite set of smaller spaces in which the solution has a closed-form, resulting in the subproblem being pseudo-analytically solvable. The second subproblem's solution can be rapidly obtained with a bisection searching strategy. In such a way, the original problem can be solved effectively by iteratively solving these two subproblems. Another advantage of the proposed algorithm is that its convergence is theoretically guaranteed and the PSLL is strictly controllable. Numerical examples with both isotropic element pattern (IEP) array and active element pattern (AEP) array are simulated to validate the effectiveness and superiority of the proposed algorithm by comparing it with the existing algorithms.

Full 2D optimized window coefficients for improved range velocity estimation in 5G joint communication and sensing

Abstract This work presents an approach for optimization of window coefficients for 5G user equipment side sensing, using orthogonal frequency division multiplexing radar-based range and velocity estimation, based on the sounding reference signal (SRS) from the 5G New Radio (NR) standard. The signal configuration and the corresponding waveform are generated in compliance with the 3rd Generation Partnership Project (3GPP) standard for 5G. The limitations of conventional signal processing for resources available for sensing with the SRS are highlighted. The proposed approach, which optimizes the window coefficients to improve the sensing capabilities, is implemented through two methods. The first method employs a decoupled optimization strategy for range and velocity, showing high computational efficiency. Our results demonstrate that this method significantly improves the peak sidelobe level (PSL) of the velocity profile by over $\mathrm{15}\,\mathrm{dB}$ , although it does not address the issue of diagonally located sidelobes, which occur due to non-uniform resource distribution. The second method adopts a comprehensive full 2D optimization technique. While it requires more computational resources and does not improve the PSL beyond the first method’s achievements, it mitigates the diagonally located sidelobes issue. The level of these have been improved by more than $\mathrm{3}\,\mathrm{dB}$ .

Hybrid Sparse Array Design Based on Pseudo-Random Algorithm and Convex Optimization with Wide Beam Steering

In this paper, a hybrid optimization method utilizing a pseudo-random algorithm and convex optimization is proposed to avoid grating lobe and achieve lower side lobe level (SLL) of a planar sparse array when the minimum inter-element distance is one wavelength. The pseudo-random algorithm is utilized to distribute the positions of elements. The convex algorithm is utilized to optimize the excitations of elements. The results show that a planar sparse array with no grating lobe and peak side lobe level (PSLL) of −17 dB can be obtained with a minimum inter-element distance of one wavelength, which indicates the effectiveness of the hybrid optimization method. In addition, beam steering can be achieved within an 80∘ field of view range.

Enhancing accuracy of alignment measurement in lithography using two-dimensional desirable sidelobe convolution window

Suppressing spectral leakage is crucial for achieving high-precision measurements using fringe images. However, existing classical windows are not effective in suppressing spectral leakage owing to their inadequate performance, leading to inaccurate measurements. To enhance the accuracy of alignment measurement for lithography by suppressing the spectral leakage associated with phase extraction of Moiré fringes, we introduce a novel two-dimensional desirable sidelobe convolution window (2D-DSCW), constructed using the maximum sidelobe attenuation window as the basis, followed by multiple self-convolutions. The 2D-DSCW with a reasonable order exhibits outstanding sidelobe behaviors, with the peak sidelobe level and sidelobe attenuation rate proportional to the convolution order. Based on this window, an improved windowed fast Fourier transform algorithm is established. Simulation and experimental results reveal that compared with classical windows, the proposed approach can more effectively suppress spectral leakage, enhancing phase extraction accuracy, and thus enabling lithography alignment with an accuracy of sub-1-nm (0.91 nm).

Improved Sparrow Search Algorithm for Rectangular Planar Array Synthesis

To optimize sparse rectangular planar array antennas under multiple constraints, including array aperture, number of elements, and minimum element spacing, an improved sparrow search algorithm (ISSA) is proposed. Initially, a position distribution matrix is generated using the Blackman window weighting method. The sparrow search algorithm (SSA) is then enhanced by incorporating Kent mapping for population initialization, which improves the initial population’s diversity. Additionally, the strategy for updating the discoverer’s position integrates elements from the sine cosine algorithm (SCA), along with a nonlinear sine learning factor, thereby enhancing global search capability. Finally, a crossover strategy is embedded into the SSA to refine the optimization accuracy by improving the search methodology used by the vigilantes. To verify the efficacy of our approach, we carried out simulation experiments. The results demonstrate that this method significantly enhances the performance of the array antenna by reducing the peak sidelobe level and minimizing the zero-trap depth. These findings validate the reliability and effectiveness of the suggested method.

Non-uniformly Spaced Antenna Arrays with Overlapped Elements Constraint

In the literature, inter-element spacing antenna design methods have been widely discussed and presented as an alternative approach to element excitation amplitude and/or phase control methods that may be relied upon to achieve the required array pattern shapes. However, methods associated with non-uniformly distributed elements suffer from the element overlap problem, where some of the optimized element locations may overlap each other and cause changes in the overall array aperture length. Practically, these element overlaps cannot be implemented, due to the physical antenna element size, without omitting some of them. Consequently, the overall performance of the antenna array is degraded. Further, degradation may occur when considering phased arrays with scanned main beams. In this paper, we first illustrate the effect of the problem of overlapped element locations and then we propose two approaches based on the genetic algorithm to optimize non-uniformly spaced arrays with overlapped element locations, while simultaneously preserving the array's directivity. To solve the problem of overlapping and to determine the physical array element size, the minimum element-spacing constraints are incorporated in a simple way in the proposed approaches. Thus, the time required to perform optimization-related computations is greatly reduced. Simulation results confirm the effectiveness of the two proposed solutions, where the probability of the elements overlapping has been reduced to zero under specific conditions related to the locations of the some of the elements, while the peak sidelobe levels were always kept below -15 dB and directivity was maintained, to the extent possible, at the level of that of standard uniformly spaced arrays.

LPI radar waveform design based on complementary phase and discrete chaotic frequency joint coding

In order to reduce the probability of radar radiation signals being detected by enemy passive detection systems, this paper proposes a phase and frequency joint coding low intercept Radar waveform design method. Based on the linear frequency modulation signal, this method uses complementary binary code and chaotic sequence to phase encode the intra-pulse modulation. Code and frequency coding. The numerical simulation results show that the designed waveform exhibits pseudo-random characteristics in the time-frequency domain, and the low recognition performance is improved; The signal has an extremely low peak sidelobe level after matched filtering, showing excellent low intercept performance; its three-dimensional ambiguity function diagram presents an ideal “graph”. Fishing type”, with good distance, speed resolution and anti-interference characteristics.

Photonics-assisted high-precision free space RF synchronization technology

In this paper, a high-precision frequency synchronization scheme in free space based on microwave photonic architecture is proposed, which can be used between multi-nodes in relative motion or in a motionless state. The reference frequency is modulated to high frequency through microwave photon up-conversion, and the reference signal is recovered at the receiver through photonics self-mixing technology. The experiment over free space in the laboratory in 4m, shows that the performance of Allen variance is s under the noise floor is s, and it is better than that of the electrical generation method with the Allen variance is s. The impact of system signal-to-noise ratio (SNR) on frequency stability is analyzed, and the experimental results are consistent with the theoretical value. At the same time, a functional experiment based on free space RF synchronization is conducted, compared to the direct synchronization method, the phase degradation of this method is only 0.25 rad, and the degradation of peak side lobe ratio (PLSR) of pulse compression loss is 0.29 dB. The good, coherent result further verifies the correctness of this synchronization method.

Orthogonal Waveform Design for MIMO Radar Using Deep Learning

Abstract This paper explores the design of orthogonal waveforms for multiple-input multiple-output (MIMO) radar. We combine the peak sidelobe level (PSL) with the omnidirectional beampattern matching as the optimization objective. To address this challenging non-linear and non-convex problem, we propose an unsupervised optimization network based on residual network and attention mechanism. This approach harnesses the potent nonlinear fitting capabilities of neural networks to address the complex optimization problem. Simulation results indicate that the waveforms designed by the algorithm proposed in this paper exhibit better orthogonality than those designed by the traditional algorithm.

Anti-Spectral Interference Waveform Design Based on High-Order Norm Optimized Autocorrelation Sidelobes Properties

This paper introduces a robust waveform design method aimed at reducing the impact of electromagnetic interference in radar systems, thereby enhancing target detection accuracy. We propose utilizing a high-order p-norm to characterize the peak sidelobe level (PSL) of the waveform. Additionally, the method incorporates spectral zero-trapping within known interfering frequency bands to mitigate interference effects. A unified optimization objective function is developed to ensure optimal correlation properties of waveforms for dual-use in radar and communication systems. By employing the AdamW algorithm for dynamic adjustment of the iteration factor, combined with a gradient descent search, this method refines both the autocorrelation of the waveform and its resilience to known disturbances. Experimental results demonstrate that our approach significantly improves autocorrelation performance over randomly generated initial waveforms. Moreover, the introduction of spectral zero-trapping notably enhances interference suppression in targeted frequency bands, thereby boosting overall signal performance. Our method effectively balances interference rejection with the minimization of sidelobe levels, offering a pragmatic waveform solution for complex radar environments.

Optically Delaying a Radio Frequency–Linear Frequency-Modulated (RF-LFM) Pulse Using Kerr Comb Carriers and Off-the-Shelf Concatenation of a Linearly Chirped Fiber Bragg Grating and a Chirped-and-Sampled Fiber Bragg Grating

We demonstrate a low latency delay of a radio frequency (RF)–linear frequency-modulated (LFM) pulse by modulating it onto optical carriers from a Kerr comb and sending the signal through a concatenation of off-the-shelf linearly chirped fiber Bragg gratings (LC-FBGs) and chirped-and-sampled FBG (CS-FBG). We characterize the frequency response and latency of the LC-FBG and CS-FBG. Then, experimentally, the LFM pulse performance is characterized by measuring the peak sidelobe level (PSL) at the output of the tunable delay system. The experiment, performed with an LFM pulse of 1 GHz bandwidth at a 10 GHz center frequency, shows a PSL better than 34.4 dB, attesting to the high quality of the buffer RF transfer function. Thus, the proposed optical memory buffer architecture, utilizing compact devices based on a Kerr comb and FBGs, offers several benefits for delaying LFM pulses, including (i) a larger tunable delay range, (ii) low latency, (iii) wide bandwidth, and (iv) high PSL.

Non-uniform optical phased array based on dual-adaption genetic algorithm improved by chaos sequence

To address the issue of diminishing peak side lobe levels (PSLL) in non-uniform optical phased array (OPA) as steering angle increases, we introduce a dual-adaption genetic algorithm (DAGA) based on chaos sequence to optimize the antenna array layout. By leveraging chaos mapping traversal and randomness, this approach enhances the diversity of the initial antenna layout, thereby improving the algorithm's capacity to escape local optima. Concurrently, we also reduce background noise during the array steering process to enhance PSLL at large steering angle. In this study, we optimize non-uniform antenna arrays with 64-, 128-, and 256-channels. The results demonstrate that at a steering angle of 80°, the PSLL is improved by 2.18, 2.81, and 3.59 dB, respectively, compared to the conventional genetic algorithm (GA). Notably, for a 256-channel array, the PSLL can be optimized to an impressive -18.46 dB using this method. To our knowledge, this represents the lowest PSLL achieved for an equivalent number of channels. Our research offers valuable insights for the practical implementation of OPAs as transmitters in chip-scale frequency modulated continuous wave (FMCW) light detection and ranging (LiDAR) systems.

A Novel Waveform Optimization Method for Orthogonal-Frequency Multiple-Input Multiple-Output Radar Based on Dual-Channel Neural Networks.

The orthogonal frequency-division multiplexing (OFDM) mode with a linear frequency modulation (LFM) signal as the baseband waveform has been widely studied and applied in multiple-input multiple-output (MIMO) radar systems. However, its high sidelobe levels after pulse compression affect the target detection of radar systems. For this paper, theoretical analysis was performed, to investigate the causes of high sidelobe levels in OFDM-LFM waveforms, and a novel waveform optimization design method based on deep neural networks is proposed. This method utilizes the classic ResNeXt network to construct dual-channel neural networks, and a new loss function is employed to design the phase and bandwidth of the OFDM-LFM waveforms. Meanwhile, the optimization factor is exploited, to address the optimization problem of the peak sidelobe levels (PSLs) and integral sidelobe levels (ISLs). Our numerical results verified the correctness of the theoretical analysis and the effectiveness of the proposed method. The designed OFDM-LFM waveforms exhibited outstanding performance in pulse compression and improved the detection performance of the radar.

Synthesis of sparse rectangular planar arrays with weight function and improved grey wolf optimization algorithm

AbstractThe present article proposes a novel hybrid approach for addressing the synthesis problem of rectangular planar arrays under multiple constraints, through joint optimization of the weight function and the improved grey wolf optimization (IGWO) algorithm. Firstly, the grey wolf optimization (GWO) algorithm is improved by using tent chaotic mapping, nonlinear convergence factor, dominant wolf dynamic belief strategy, and opposition‐based learning strategy to increase the population diversity and the ability to jump out of the local optimum. Secondly, the array elements are weighted using the weight function to generate the position distribution matrix, which reduces the thinned matrix optimization time and improves the optimization efficiency. Finally, the position distribution matrix is used to generate the thinned array, and the IGWO algorithm is applied to perform the sparse optimization with multiple constraints. The effectiveness of the method is verified through numerical simulation and full‐wave simulation experiments, demonstrating its capability to enhance array antenna performance and reduce peak sidelobe level (PSLL). These experimental results hold significant engineering implications and provide valuable references for addressing the array distribution problem under multiple constraints.

Sparse TFM imaging of different-scale phased array based on the DWSO algorithm

ABSTRACT Using sparse matrix for total focusing method (TFM) imaging can improve computational efficiency in non-destructive testing (NDT). In sparse matrix design, binary particle swarm optimisation (BPSO) and genetic algorithm (GA) often fall into local optimal solution, and the computational efficiency decreases significantly with the expansion of array scale. In this paper, a discrete war strategy optimisation (DWSO) is proposed to realise sparse array ultrasonic imaging. This method uses equidistant scatter mapping to real-number encode the array position and limit the search range. Then, the fitness function is constructed with low side lobe peak and narrow main lobe width to obtain the optimal array element distribution. Experiments on standard test block demonstrate the DWSO algorithm has a narrower main lobe width and lower side lobe peaks than BPSO and GA. The imaging time of the sparse array constructed by this algorithm is reduced by more than 65% compared with the full array. As array scale grows, the running time variation of the proposed method is reduced by 73.2% and 77.3% compared with GA and BPSO, which has good adaptability and provides theoretical support for real-time defect detection.

Multi-layered medium ultrasonic phased array sparse TFM imaging based on self-adaptive differential evolution algorithm

Abstract Multilayer Composite material structures have been widely used in modern engineering fields. However, defects within these materials can adversely affect mechanical properties. Ultrasonic phased array total focusing method (TFM) imaging has advantages of high precision and dynamic focusing over the entire range, achieving significant progress in homogeneous medium detection. However, heavy computational burdens of multilayer structures lead to inefficient imaging. To address this issue, a sparse-TFM imaging algorithm using ultrasonic phased arrays suitable for multilayer media is proposed in this paper. This method constructs a fitness function with constraints such as main lobe width and sidelobe peak. Its objective is to obtain the distribution of sparse array element positions using an self-adaptive differential evolution algorithm. Subsequently, the delay time of each array element in multilayer media sparse TFM is calculated using the root mean square (RMS) principle and combined with amplitude weighting, the method corrects the imaging results. Compared with the Ray-based full-matrix capture and TFM method (Ray-based FMC/TFM), the RMS-based full-matrix capture and TFM (RMS-based FMC/TFM), and the phase shift method, the experimental and simulation results demonstrate that the proposed method significantly reduces the imaging data volume, improves computational efficiency, and maintains quantitative errors within 0.2 mm.

Synthesis of Non-Uniform Spiral Antenna with Low Peak Sidelobe Level Using Enhanced Harris Hawks Optimization Algorithm

In this paper, to obtain antenna arrays with grating lobes suppression capability in wideband and achieve a low peak sidelobe level (PSLL), two non-uniform spiral antenna arrays and an enhanced Harris Hawks optimization (EHHO) algorithm are proposed. By controlling the parameters of the spiral line and sampling equidistant on the spiral line, the sampling points that make up the non-uniform array can be arranged in the plane uniformly and non-uniformly. The simulation results indicate that, because of this special arrangement, the non-uniform arrays obtain the capability of grating lobe suppression in wideband or wide spacing arrangement when compared to the classic uniform array. In addition, to obtain lower PSLL, the Harris Hawks optimization (HHO) algorithm is used for array synthesis because of its diversity of search methods. By employing the step-type taper distribution strategy and the migration strategy, the algorithm’s search ability is enhanced, and the simulation results indicate the EHHO algorithm obtains a better solution in terms of the PSLL than other algorithms. A simple patch antenna is designed to build the non-uniform spiral arrays synthesized by the EHHO algorithm. The calculation and simulation results validate the superior performance of the proposed algorithm.