High Sidelobes Research Articles

Aperiodic optical phased array with wide field of view and high sidelobe suppression ratio based on SiN-on-SOI platform

This paper presents an aperiodic optical phased array (OPA) based on a dual-layer emission grating on a SiN-on-SOI platform. The dual-layer grating offers advantages in terms of integration process feasibility and large process tolerance. A particle swarm optimization algorithm was employed to optimize the emission array. The proposed OPA demonstrates a wide field of view (FOV) and high sidelobe suppression ratio (SLSR). Experimental verification was performed on an 8-inch silicon photonics integration manufacturing platform capable of industrial production, and the device performance matched well with simulation results. The test results indicate that the OPA achieved a FOV of 150° × 16° with a divergence angle of 0.022° × 0.060°. The overall optimal SLSR was 10.3 dB, with a minimum SLSR of 3.7 dB across the entire field of view. Furthermore, the OPA was used in frequency modulation continuous wave system to achieve a distance measurement of 40 m.

High efficiency high gain low side lobe level and slant polarization monopulse antenna using cavity backed slot coupled patch antenna

In this paper, a multilayer monopulse antenna at Ku-Band with high efficiency, high power handling capability, high gain, 45° linear polarization and low sidelobe is presented. A new slot antenna is proposed as a radiating element based on a cavity-backed slot-coupled patch antenna. Using an enclosed cavity structure reduces coupling between antenna elements, thus increasing the antenna efficiency. The sub-array antenna has been designed to reduce the feed network complexity. A cavity power divider and feeding slots are inserted between the layers of the sub-array to compact it. An innovative feeding network has been designed with two-dimensional Chebyshev distribution. The simulated impedance bandwidth is 11%. The peak gain is 38.4 dBi and the sidelobe level of lower than 18 dB with a half-power beamwidth of approximately 2.4° for sum patterns at the center frequency and efficiency is more than 86% across the bandwidth. The maximum value of null depth for the difference patterns is − 38 dB. The radiation patterns have good symmetry in both E- and H-planes. The simulation results are compared with the results obtained from the built part of the antenna. the measurements results are in good agreement with the simulation.

Wideband Series-Fed Patch Antenna Array With High Gain and Low Sidelobe: Linearly and Circularly Polarized for 5G V2X Applications

Wideband Series-Fed Patch Antenna Array With High Gain and Low Sidelobe: Linearly and Circularly Polarized for 5G V2X Applications

Integrated lithium niobate Vernier micro-ring filter with wide and fast tuning capabilities

Tunable optical micro-ring filters play significant roles in optical communications, microwave photonics, and photonic neural networks. Typical micro-ring filters are based on either a thermo-optic (TO) effect with microsecond timescales or an electro-optic (EO) effect with a limited tuning range. Here, we report a continuously tunable lithium niobate on insulator (LNOI) Vernier cascaded micro-ring filter with wire-bonded packaging integrated with both TO and EO tuning electrodes, featuring a 40-nm free spectral range (FSR), 2.3 GHz EO bandwidth, and a high sidelobe suppression ratio of 21.7 dB, simultaneously. Our high-performance optical micro-ring filter could become an important element in future LNOI photonic circuits with applications in high-capacity wavelength-division multiplexing (WDM) systems, broadband microwave photonics, fast-tunable external-cavity lasers, and high-speed photonic neural networks.

Half-wavelength-pitch silicon optical phased array with a 180° field of view, high sidelobe suppression ratio, and complex-pattern beamforming

Solid-state optical beam steering devices desire a large field of view (FOV), good beam quality, and reconfigurable beamforming of complex patterns, which are not available in a single system yet. Having not been demonstrated, an active beamformer using an optical phased array (OPA) could potentially fulfill these requirements simultaneously, because it can control both the wavefront and beam pattern. Half-wavelength-pitch OPAs theoretically can achieve the three requirements concurrently, but suffer from crosstalk. Most previous efforts focus on mitigating/avoiding crosstalk. Instead, here we appreciate its existence and propose/demonstrate a programmable architecture to compensate for it. Using a tree of composite variable splitters with a full splitting-ratio range, we achieve arbitrary amplitude/phase modulation to pre-correct scrambled phase/amplitude by crosstalk. With comprehensive stray-light minimization strategies, the sidelobe suppression ratio (SLSR) is significantly improved. Our design achieves a 180∘ FOV, a peak SLSR of 24 dB, and complex-pattern beamforming simultaneously in a half-wavelength-pitch 64-waveguide array. Within the ±60∘ range, a SLSR of >20dB is achieved. Our OPA demonstrates Bayliss difference, pulse-shaped, and asymmetric three-beam patterns with high SLSRs of >20dB, ∼10dB, and >18dB, respectively. These performance metrics are important for various applications in light detection and ranging, imaging, and communication.

Enhancing image reconstruction in photoacoustic imaging using spatial coherence mean-to-standard-deviation factor beamforming.

In photoacoustic imaging (PAI), a delay-and-sum (DAS) beamforming reconstruction algorithm is widely used due to its ease of implementation and fast execution. However, it is plagued by issues such as high sidelobe artifacts and low contrast, that significantly hinder the ability to differentiate various structures in the reconstructed images. In this study, we propose an adaptive weighting factor called spatial coherence mean-to-standard deviation factor (scMSF) in DAS, which is extended into the spatial frequency domain. By combining scMSF with a minimum variance (MV) algorithm, the clutter level is reduced, thereby enhancing the image contrast. Quantitative results obtained from the phantom experiment demonstrate that our proposed method improves contrast ratio (CR) by 30.15 dB and signal-to-noise ratio (SNR) by 8.62 dB compared to DAS while also improving full-width at half maxima (FWHM) by 56%. From the in-vivo experiments, the scMSF-based reconstruction image exhibits a higher generalized contrast-to-noise ratio (gCNR), indicating improved target detectability with a 25.6% enhancement over DAS and a 22.5% improvement over MV.

Algorithm for Designing Waveforms Similar to Linear Frequency Modulation Using Polyphase-Coded Frequency Modulation

Linear frequency modulation (LFM) waveforms have been widely adopted due to their excellent performance characteristics, such as good Doppler tolerance and ease of physical implementation. However, LFM waveforms suffer from high autocorrelation sidelobes (ACSLs) and limited design flexibility. Phase-coded frequency modulation (PCFM) waveforms can be used to design waveforms similar to LFM, offering greater design flexibility to optimize ACSLs. However, it has been found that the initial PCFM waveform experiences spectral expansion during the ACSL optimization process, which reduces its similarity to LFM. Therefore, this article jointly optimizes the ACSLs and spectrum of the initial PCFM waveform, establishes an optimized mathematical model, and then solves it using the heavy-ball gradient descent algorithm. Numerical experiments indicate that the proposed method effectively addresses the problem of waveform similarity degradation caused by spectral expansion while reducing waveform ACSLs. At the same time, a balance between reducing waveform ACSLs and preserving waveform similarity can be achieved by adjusting the parameters.

An enhanced companding algorithm for universal filtered multicarrier system

SummaryThis paper proposes a new companding scheme called three ‐law companding to reduce the peak‐to‐average power ratio (PAPR) in a universal filtered multicarrier system. The proposed scheme is more flexible and provides better PAPR reduction and bit error rate (BER) through the selection of the three companding parameters. However, the proposed scheme suffers from high side lobes and increases the power of the companded signal. Hence, an enhanced three ‐law, which overcomes these limitations, is proposed. Unlike the first scheme, the enhanced scheme does not change the mean power of the companding signal because it incorporates power‐level normalization. Simulation results show that, compared with other companding transforms, the proposed enhanced three ‐law scheme offers better BER characteristics and side‐lobe level suppression. It also provides better PAPR reduction of 7.4 dB and net gain of 6.3 dB. The proposed enhanced three ‐law was evaluated over a multipath fading channel and was more robust than other companding techniques. Finally, the proposed enhanced algorithm was verified in real time using the Wireless Open‐Access Research Platform.

Enhanced Ultrasound Image Formation with Computationally Efficient Cross-Angular Delay Multiply and Sum Beamforming.

Ultrasound imaging is a valuable clinical tool. It is commonly achieved using the delay and sum beamformer algorithm, which takes the signals received by an array of sensors and generates an image estimating the spatial distribution of the signal sources. This algorithm, while computationally efficient, has limited resolution and suffers from high side lobes. Nonlinear processing has proven to be an effective way to enhance the image quality produced by beamforming in a computationally efficient manner. In this work, we describe a new beamforming algorithm called Cross-Angular Delay Multiply and Sum, which takes advantage of nonlinear compounding to enhance contrast and resolution. This is then implemented with a mathematical reformulation to produce images with tighter point spread functions and enhanced contrast at a low computational cost. We tested this new algorithm over a range of in vitro and in vivo scenarios for both conventional B-Mode and amplitude modulation imaging, and for two types of ultrasound contrast agents, demonstrating its potential for clinical settings.

A novel energy-focused slow-time MIMO radar and signal processing scheme

In this paper, we propose a novel energy-focused slow-time MIMO radar and signal processing scheme, aimed at addressing key challenges in slow-time coding and signal processing technology. Conventional slow-time MIMO radar faces issues such as energy waste due to the omnidirectional transmit beampattern of orthogonal coding and the velocity ambiguity problem. To overcome these limitations, the proposed radar system utilizes a method based on Doppler frequency offset diversity (DFOD) for slow-time coding design. This method enables the adjustment of Doppler offset parameters to achieve a rectangular transmit beampattern with any mainlobe width within a single coherent processing interval (CPI), offering the advantage of low computational complexity. Through an analysis of the ambiguity function for DFOD-based coding, we evaluate both Doppler and angular resolution. To further improve Doppler frequency resolution, a slow-time coding design is introduced based on Pulse Random Permutation (PRP). Subsequently, a signal processing scheme based on matched filtering is presented. To tackle the high Doppler sidelobe issue associated with PRP-based coding, we propose a mismatch filter (MMF) design method utilizing convex optimization. Ultimately, the performance enhancement of the proposed slow-time MIMO radar is verified through simulation analysis in comparison to existing technologies.

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.

Sparse decoupling imaging network for wideband two‐dimensional MIMO radar imaging using model‐assisted and attention mechanism

AbstractThe problem of sparse decoupling radar imaging methods based on deep learning is researched. An improved model‐driven learning imaging network with a complex‐valued convolution block attention module plugged into each sub‐network is proposed. This method can solve the high sidelobe and coupling problem in sparse wideband Multiple‐Input Multiple‐Output (MIMO) radar. In addition, it can better focus on the target area and capture target information to boost model representation power. Experimental results verify the validity of the proposed method.

Deblurring of Beamformed Images in the Ocean Acoustic Waveguide Using Deep Learning-Based Deconvolution

A horizontal towed linear coherent hydrophone array is often employed to estimate the spatial intensity distribution of incident plane waves scattered from the geological and biological features in an ocean acoustic waveguide using conventional beamforming. However, due to the physical limitations of the array aperture, the spatial resolution after conventional beamforming is often limited by the fat main lobe and the high sidelobes. Here, we propose a method originated from computer vision deblurring based on deep learning to enhance the spatial resolution of beamformed images. The effect of image blurring after conventional beamforming can be considered a convolution of beam pattern, which acts as a point spread function (PSF), and the original spatial intensity distributions of incident plane waves. A modified U-Net-like network is trained on a simulated dataset. The instantaneous acoustic complex amplitude is assumed following circular complex Gaussian random (CCGR) statistics. Both synthetic data and experimental data collected from the South China Sea Experiment in 2021 are used to illustrate the effectiveness of this approach, showing a maximum 700% reduction in a 3 dB width over conventional beamforming. A lower normalized mean square error (NMSE) is provided compared with other deconvolution-based algorithms, such as the Richardson–Lucy algorithm and the approximate likelihood model-based deconvolution algorithm. The method is applicable in various acoustic imaging applications that employ linear coherent hydrophone arrays with one-dimensional conventional beamforming, such as ocean acoustic waveguide remote sensing (OAWRS).

Low-Sidelobe Imaging Method Utilizing Improved Spatially Variant Apodization for Forward-Looking Sonar

For two-dimensional forward-looking sonar imaging, high sidelobes significantly degrade the quality of sonar images. The cosine window function weighting method is often applied to suppress the sidelobe levels in the angular and range dimensions, at the expense of the main lobe resolutions. Therefore, an improved spatially variant apodization imaging method for forward-looking sonar is proposed, to reduce sidelobes without degrading the main lobe resolution in angular-range dimensions. The proposed method is a nonlinear postprocessing operation in which the raw complex-valued sonar image produced by a conventional beamformer and matched filter is weighted by a spatially variant coefficient. To enhance the robustness of the spatially variant apodization approach, the array magnitude and phase errors are calibrated to prevent the occurrence of beam sidelobe increase prior to beamforming operations. The analyzed results of numerical simulations and a lake experiment demonstrate that the proposed method can greatly reduce the sidelobes to approximately −40 dB, while the main lobe width remains unchanged. Moreover, this method has an extremely simple computational process.

A mutation hill climber for thinning of concentric circular antenna arrays

ABSTRACT Thinned concentric circular antenna arrays (CCAAs) are crucial in modern wireless communication systems. CCAAs are widely used for efficient communication, directivity, and beamforming capabilities. Nevertheless, CCAAs encounter a challenge of radiation patterns characterised by high sidelobe levels (SLL). Designing thinned CCAAs to surmount this limitation for optimal performance can be complex and challenging. This paper employs a mutation hill climber (MHC) algorithm to optimise the CCAA structure. The goal is to attain an optimal design that balances the number of switched-on elements and reduces the maximum SLL. The performance and efficacy of the MHC are examined using two different cases of thinned CCAA design: two-ring and ten-ring configurations. Simulation results reveal the superiority of the MHC over the latest binary metaheuristic techniques in achieving the lowest maximum SLL and the number of switched-on elements. For the two-ring thinned CCAA, the MHC effectively switched off 50 antenna elements, reducing the maximum SLL by 24.92 dB. Meanwhile, for the ten-ring thinned CCAA, the MHC reduced the number of switched-on antenna elements from 440 to 184, reducing the maximum SLL by 34.09 dB.

Low sidelobe planar electrically large sparse array antenna with element number reduction based on genetic algorithm

AbstractConventionally, both electrically larger (EL) arrays and sparse arrays offer the advantage of element number reduction but disadvantage of high sidelobe levels. A new scheme of planar EL sparse array antenna based on a genetic algorithm (GA) to achieve low sidelobe with element number reduction is proposed. To begin with, EL sparse array antenna optimisation models based on GA for both linear and planar arrays are analysed. Then, an EL slot antenna element based on a 3 × 3 substrate integrated waveguide cavity is designed. An 8‐element linear EL sparse array antenna is designed and compared with a uniform array antenna, demonstrating a reduction in the maximum sidelobe level (MSLL) by nearly 4.6 dB. After that, a 4 × 8 element planar EL sparse array antenna is fabricated and measured. Compared to an 8 × 16 element planar EL uniform array antenna, the number of antenna elements is reduced by 75%, while the MSLL is reduced by approximately 3 dB. The measured −10 dB impedance bandwidth ranges from 25.3 to 27.8 GHz. At the central frequency, the radiation pattern achieves a peak gain of 29.6 dBi, exhibiting low sidelobe levels below −15.0 dB.

Frequency Diversity Arc Array with Angle-Distance Two-Dimensional Broadening Null Steering for Sidelobe Suppression

The frequency diversity arc array (FDAA) improves the structure of the traditional frequency diversity array (FDA) from a linear array structure to an arc array structure, so that the FDAA not only has the advantages of the FDA but also has a large angle and omnidirectional scanning capability. However, when it is equivalent to a linear array, this arc-shaped structure will lead to the phenomenon of inverse density weighting, which leads to a higher sidelobe level of the FDAA beam pattern. In order to solve the problem of a high sidelobe level at a certain position of the FDAA, a frequency diversity arc array with angle-distance two-dimensional broadening null steering is proposed for sidelobe suppression. Using a structural model of the FDAA, the problem of the high sidelobe was analyzed. The linear constrained minimum variance (LCMV) method was used to generate a null with a certain width at the position of the fixed strong sidelobe level in the angle domain and the distance domain of the FDAA beam pattern, to reduce the FDAA sidelobe level. Then, the angle domain and distance domain fixed positions of the FDAA were simulated to generate the null beam pattern. The simulation results verified the effectiveness of this method for reducing the sidelobe level.

A Collaborative Control Method of Transmission Amplitude and Phase for Transmitarray Antenna Design

In this paper, a collaborative control method of transmission amplitude and phase for transmitarray antenna (TA) design is proposed. In this proposed method, one of the most popular hypersurface fitting models, Gaussian stochastic process (GP) model, is utilized to construct an accurate surrogate model. Following this implementation, a mapping relationship between structural parameters of TA unit cell and its transmission amplitude and phase is established. The most advantage of this method is its applicability for general TA designs because it is much difficult to control the amplitude and phase of unit cell independently through adjusting separate structural parameters. To verify the high efficiency of the proposed method, three TA antennas with different scanning angles are designed to obtain high sidelobe suppression level. Measured results show that the proposed collaborative control method of amplitude and phase is much promising for high sidelobe suppression level in TA designs.

Cost-effective horn antennas; from E-plane sectionalized to octagonal shapes

ABSTRACT In this paper, low-cost methods of improving horn antennas are studied based on the recently introduced E-plane sectionalized rectangular horn antennas. In the beginning, application of E-plane sectionalizing of the rectangular horn antennas on open-boundary E-plane sectoral horn antennas is reexamined and a simple method for obtaining high gain and low sidelobe levels (SLLs) is introduced. Subsequently, effects of removing the conductive sheets from the modified E-plane sectionalized horn antenna are studied and a simple horn antenna with octagonal aperture is obtained with slightly reduced efficiency. Finally, an ultra-low-cost method of improving simple rectangular horn antennas is introduced based on this study. The purpose is to compensate the destructive effects of high SLLs of reference antennas in the antenna measurements, since a common reference antenna type is the rectangular horn antenna, which has high SLLs on the E-plane. In particular, efficiency improvement and SLL reduction of rectangular horn antennas are achieved by adding only four triangular conductive sheets to construct octagonal horn antennas.

A Novel Multimodal Fusion Framework Based on Point Cloud Registration for Near-Field 3D SAR Perception

This study introduces a pioneering multimodal fusion framework to enhance near-field 3D Synthetic Aperture Radar (SAR) imaging, crucial for applications like radar cross-section measurement and concealed object detection. Traditional near-field 3D SAR imaging struggles with issues like target–background confusion due to clutter and multipath interference, shape distortion from high sidelobes, and lack of color and texture information, all of which impede effective target recognition and scattering diagnosis. The proposed approach presents the first known application of multimodal fusion in near-field 3D SAR imaging, integrating LiDAR and optical camera data to overcome its inherent limitations. The framework comprises data preprocessing, point cloud registration, and data fusion, where registration between multi-sensor data is the core of effective integration. Recognizing the inadequacy of traditional registration methods in handling varying data formats, noise, and resolution differences, particularly between near-field 3D SAR and other sensors, this work introduces a novel three-stage registration process to effectively address these challenges. First, the approach designs a structure–intensity-constrained centroid distance detector, enabling key point extraction that reduces heterogeneity and accelerates the process. Second, a sample consensus initial alignment algorithm with SHOT features and geometric relationship constraints is proposed for enhanced coarse registration. Finally, the fine registration phase employs adaptive thresholding in the iterative closest point algorithm for precise and efficient data alignment. Both visual and quantitative analyses of measured data demonstrate the effectiveness of our method. The experimental results show significant improvements in registration accuracy and efficiency, laying the groundwork for future multimodal fusion advancements in near-field 3D SAR imaging.