Sidelobe Effects Research Articles

Radar Target Radar Cross-Section Measurement Based on Enhanced Imaging and Scattering Center Extraction.

Accurate measurement of a Radar Cross-Section (RCS) is a critical technical challenge in assessing the stealth performance and scattering characteristics of radar targets. Traditional RCS measurement methods are limited by high costs, sensitivity to environmental conditions, and difficulties in distinguishing local scattering features of targets. To address these challenges, this paper proposes a novel RCS measurement method based on enhanced imaging and scattering center extraction. This method integrates sub-aperture imaging with image fusion techniques to improve imaging quality and enhance the detail of target scattering characteristics. Additionally, an improved sequence CLEAN algorithm is employed to effectively suppress sidelobe effects and ensure the accuracy of scattering center extraction. Experimental results demonstrate that this method achieves higher precision in RCS measurement of complex targets and is particularly effective in environments with strong interference, where it successfully separates the scattering contributions of the target from those of the interference sources. This method offers a new technological approach for precise RCS measurement of radar stealth targets in the future.

2D spatiotemporal passive cavitation imaging and evaluation during ultrasound thrombolysis based on diagnostic ultrasound platform

Acoustic cavitation plays a critical role in various biomedical applications. However, uncontrolled cavitation can lead to undesired damage to healthy tissues. Therefore, real-time monitoring and quantitative evaluation of cavitation dynamics is essential for understanding underlying mechanisms and optimizing ultrasound treatment efficiency and safety. The current research addressed the limitations of traditionally used cavitation detection methods by developing introduced an adaptive time-division multiplexing passive cavitation imaging (PCI) system integrated into a commercial diagnostic ultrasound platform. This new method combined real-time cavitation monitoring with B-mode imaging, allowing for simultaneous visualization of treatment progress and 2D quantitative evaluation of cavitation dosage within targeted area. An improved delay-and-sum (DAS) algorithm, optimized with a minimum variance (MV) beamformer, is utilized to minimize the side lobe effect and improve the axial resolution typically associated with PCI. In additional to visualize and quantitatively assess the cavitation activities generated under varied acoustic pressures and microbubble concentrations, this system was specifically applied to perform 2D cavitation evaluation for ultrasound thrombolysis mediated by different solutions, e.g., saline, nanodiamond (ND) and nitrogen-annealed nanodiamond (N-AND). This research aims to bridge the gap between laboratory-based research systems and real-time spatiotemporal cavitation evaluation demands in practical uses. Results indicate that this improved 2D cavitation monitoring and evaluation system could offer a useful tool for comprehensive evaluating cavitation-mediated effects (e.g., ultrasound thrombolysis), providing valuable insights into in-depth understanding of cavitation mechanisms and optimization of cavitation applications.

Efficient generation of high-contrast Bessel light sheets: a flexible method for improved imaging.

Light-sheet fluorescence microscopy plays a pivotal role in the field of biological 3D imaging. Among its various implementations, non-diffracting light sheets have garnered significant attention due to their remarkable ability to achieve a favorable balance between field of view and resolution. However, the presence of noticeable side-lobe effects in the non-diffracting light sheets poses challenges, including decreased contrast and an increased risk of phototoxicity. While amplitude modulation-based methods effectively suppress side-lobe influences, their transmission efficiency remains suboptimal. To address these limitations, this article introduces an approach based on phase modulation, facilitating the convenient and flexible generation of light sheets that effectively suppress side-lobe effects while maintaining high transmission efficiency. Importantly, our method enables rapid determination of optimal phase parameters, successfully suppressing the peak intensity of the first and second side lobes to levels exceeding 98% and 99%, respectively. Subsequently, experimental results substantiate the light sheet's exceptional contrast-enhancing capabilities.

Impact of beam far side-lobe knowledge in the presence of foregrounds for LiteBIRD

We present a study of the impact of a beam far side-lobe lack of knowledge on the measurement of the Cosmic Microwave Background B-mode signal at large scale. Beam far side-lobes induce a mismatch in the transfer function of Galactic foregrounds between the dipole and higher multipoles which degrads the performances of component separation methods. This leads to foreground residuals in the CMB map. It is expected to be one of the main source of systematic effects in future CMB polarization observations. Thus, it becomes crucial for all-sky survey missions to take into account the interplays between beam systematic effects and all the data analysis steps. LiteBIRD is the ISAS/JAXA second strategic large-class satellite mission and is dedicated to target the measurement of CMB primordial B modes by reaching a sensitivity on the tensor-to-scalar ratio r of σ(r) ≤ 10-3 assuming r = 0. The primary goal of this paper is to provide the methodology and develop the framework to carry out the end-to-end study of beam far side-lobe effects for a space-borne CMB experiment. We introduce uncertainties in the beam model, and propagate the beam effects through all the steps of the analysis pipeline, most importantly including component separation, up to the cosmological results in the form of a bias δr. As a demonstration of our framework, we derive requirements on the calibration and modeling for the LiteBIRD's beams under given assumptions on design, simulation, component separation method and allocated error budget. In particular, we assume a parametric method of component separation with no mitigation of the far side-lobes effect at any stage of the analysis pipeline. We show that δr is mostly due to the integrated fractional power difference between the estimated beams and the true beams in the far side-lobes region, with little dependence on the actual shape of the beams, for low enough δr. Under our set of assumptions, in particular considering the specific foreground cleaning method we used, we find that the integrated fractional power in the far side-lobes should be known at the level of ∼ 10-4, to achieve the required limit on the bias δr < 1.9 × 10-5. The framework and tools developed for this study can be easily adapted to provide requirements under different design, data analysis frameworks and for other future space-borne experiments, such as PICO or CMB-Bharat. We further discuss the limitations of this framework and potential extensions to circumvent them.

Long-Term Coherent Integration Algorithm for High-Speed Target Detection.

Long-term coherent integration (CI) can effectively improve the radar detection capability for high-speed targets. However, the range walk (RW) effect caused by high-speed motion significantly degrades the detection performance. To improve detection performance, this study proposes an improved algorithm based on the modified Radon inverse Fourier transform (denoted as IMRIFT). The proposed algorithm uses parameter searching for velocity estimation, designs a compensation function based on the relationship between velocity and distance walk and Doppler ambiguity terms, and performs CI based on the compensated signal. IMRIFT can achieve RW correction, avoid the blind-speed sidelobe (BSSL) effect caused by velocity mismatch, and improve detection performance, while ensuring low computational complexity. In addition, considering the relationship between energy concentration regions and bandwidth in the 2D frequency domain, a fast method based on IMIRFT is proposed, which can balance computational cost and detection capacity. Finally, a series of comparative experiments are conducted to demonstrate the effectiveness of the proposed algorithm and the fast method.

Robust automotive radar interference mitigation using multiplicative-adaptive filtering and Hilbert transform

Radar is one of the sensors that have significant attention to be implemented in an autonomous vehicle since its robustness under many possible environmental conditions such as fog, rain, and poor light. However, the implementation risks interference because of transmitting and/or receiving radar signals from/to other vehicles. This interference will increase the floor noise that can mask the target signal. This paper proposes multiplicative-adaptive filtering and Hilbert transform to mitigate the interference effect and maintain the target signal detectability. The method exploited the trade-off between the step-size and sidelobe effect on the least mean square-based adaptive filtering to improve the target detection accuracy, especially in the long-range case. The numerical analysis on the millimeter-wave frequency modulated continuous wave radar with multiple interferers concluded that the proposed method could maintain and enhance the target signal even if the target range is relatively far from the victim radar.

Sound source localization method based time-domain signal feature using deep learning

Deep learning, as the most commonly used machine learning algorithm, is widely used in various fields. In the field of acoustics, deep learning methods are combined with frequency-domain features of signals to locate sound sources. The commonly frequency domain features include microphones array Cross-spectral-Matrix(CSM) and Short Time Fourier Transform(STFT). However, the use of frequency-domain features often leads to the loss of partial signal information and increases the computational complexity. This paper proposed a novel sound source localization algorithm based on time-domain features, which uses convolutional neural network(CNN) as a medium to achieve mapping from time-domain features to sound source locations. This method does not rely on any basic signal processing algorithm, and directly uses time-domain sampling points as network inputs for sound source localization. The application simulation shows that the proposed method can achieve precise localization and low side-lobe effect under different testing conditions. Once the network training is completed, the testing accuracy under different conditions is above 95%, with a maximum of 100%.

Evaluating the impact of 4D seismic data artefacts in data assimilation

Time-lapse or 4D seismic data are important constraints in reservoir studies because they enable monitoring of saturation and pressure changes that result from hydrocarbon production. 4D seismic data have been quantitatively added, along with production data, in history matching or data assimilation procedures to reduce uncertainty and improve production forecasts. Before performing quantitative studies, it is important to ensure that the 4D seismic data are reliable, with minimal artefacts such as side-lobe effects that can disturb the identification of anomalies. In this work, we propose different ways of treating 4D seismic data in data assimilation for a real reservoir. Explicitly, we evaluated the impact on data assimilation results when considering different amounts of 4D information and three treatments to the identified artefacts. The treatments were: ignoring them, excluding them from data assimilation or defining no seismic changes at their locations. The results show that well and seismic matches are improved when 4D seismic data are assimilated, also improving the production predictions. Despite being a thin reservoir, assimilating two single-layer maps allowed us to predict relevant observed dynamic behaviour, such as the evolved gas trapped in the lower interval. Furthermore, when a treatment was applied to the artefacts, they produced better models than using a single two-layer map (with lower production errors and visually closer impedances to the observed data). Our recommendation is the assimilation of well and 4D seismic data, with the exclusion of unreliable information, for better life-cycle decisions.

A Novel Optimization Strategy of Sidelobe Suppression for Pulse Compression Weather Radar

The solid-state transmitters are widely adopted for weather radars, where pulse compression is operated to provide the required sensitivity and range resolution. Therefore, effective sidelobe suppression strategies must be employed, especially for weather observation. Currently, many methods can suppress the sidelobe to a very low level in the case of point targets or uniformly distributed targets. However, in strong convection weather process, the weather echo amplitude lies in a wide dynamic range and the main lobe of weak target is prone to being contaminated by the sidelobe of strong target, causing the degradation of weather fundamental data estimation, even generating artifacts and affecting the quantitative precipitation evaluation. In this paper, we propose a novel strategy which is the further processing of a general pulse compression radar to mitigate the effects of sidelobes. The proposed method is called the predominant component extraction (PCE), in which the re-weighting processing is operated after pulse compression, and then the echo of each bin is optimized and its energy will approach the real targets in each bin. It can improve the estimation of weak signals or even eliminate the artifact at the edge of the scene. Numerical simulation experiments and real-data verifications are implemented to validate the feasibility and superiority. It is noted that the proposed method has no requirement on the transmitted waveform and can be realized only by adding a step after pulse compression in the actual system.

Single-shot super-resolution quantitative phase imaging allowed by coherence gate shaping

Biomedical and metasurface researchers repeatedly reach for quantitative phase imaging (QPI) as their primary imaging technique due to its high-throughput, label-free, quantitative nature. So far, very little progress has been made toward achieving super-resolution in QPI. However, the possible super-resolving QPI would satisfy the need for quantitative observation of previously unresolved biological specimen features and allow unprecedented throughputs in the imaging of dielectric metasurfaces. Here we present a method capable of real-time super-resolution QPI, which we achieve by shaping the coherence gate in the holographic microscope with partially coherent illumination. Our approach is based on the fact that the point spread function (PSF) of such a system is a product of the diffraction-limited spot and the coherence-gating function, which is shaped similarly to the superoscillatory hotspot. The product simultaneously produces the PSF with a super-resolution central peak and minimizes sidelobe effects commonly devaluating the superoscillatory imaging. The minimization of sidelobes and resolution improvement co-occur in the entire field of view. Therefore, for the first time, we achieve a single-shot widefield super-resolution QPI. We demonstrate here resolution improvement on simulated as well as experimental data. A phase resolution target image shows a resolving power improvement of 19%. Finally, we show the practical feasibility by applying the proposed method to the imaging of biological specimens.

Foreground removal of CO intensity mapping using deep learning

ABSTRACTLine intensity mapping (LIM) is a promising probe to study star formation, the large-scale structure of the Universe, and the epoch of reionization (EoR). Since carbon monoxide (CO) is the second most abundant molecule in the Universe except for molecular hydrogen H2, it is suitable as a tracer for LIM surveys. However, just like other LIM surveys, CO intensity mapping also suffers strong foreground contamination that needs to be eliminated for extracting valuable astrophysical and cosmological information. In this work, we take 12CO($\it J$ = 1-0) emission line as an example to investigate whether deep learning method can effectively recover the signal by removing the foregrounds. The CO(1-0) intensity maps are generated by N-body simulations considering CO luminosity and halo mass relation, and we discuss two cases with median and low CO signals by comparing different relations. We add foregrounds generated from real observations, including thermal dust, spinning dust, free–free, synchrotron emission, and cosmic microwave background anisotropy. The beam with sidelobe effect is also considered. Our deep learning model is built upon ResUNet, which combines image generation algorithm UNet with the state-of-the-art architecture of deep learning, ResNet. The principal component analysis (PCA) method is employed to preprocess data before feeding it to the ResUNet. We find that, in the case of low instrumental noise, our UNet can efficiently reconstruct the CO signal map with correct line power spectrum by removing the foregrounds and recovering PCA signal loss and beam effects. Our method also can be applied to other intensity mappings like neutral hydrogen 21-cm surveys.

Radar coherent detection for hypersonic target based on shear transform and dechirp process

For hypersonic target detection, the range migration (RM) and Doppler frequency migration (DFM) often occur during coherent processing time, which would lead to integration loss and degrade detection performance. Aiming at these problems, this paper proposes a coherent integration method based on shear transform (ST) and dechirp process (DP), namely ST-DP. Specifically, the RM is eliminated by shearing the echo data plane, and then the DFM is compensated by dechirp process. Eventually, the Fourier transform (FT) is carried out in the slow time dimension to realize coherent integration. Compared with existed methods, the presented method has a coherent integration and detection performance close to that of GRFT without increasing the computational cost, while there is no blind speed sidelobe (BSSL) effect. Some simulation experiments are given to prove the practicality and efficacy of the presented method.

Design of Integrating Sphere Uniform Light System for Solar Simulator

In the process of designing and adjusting the solar simulator, the side lobe effect and aberration of the optical integrator affect the irradiation uniformity, an integrating sphere is proposed to replace the optical integrator for uniform light and the compound parabolic reflector for beam shaping. First, based on the equal illuminance theorem and the radiation transfer theory, the calculation formula of the energy transmission efficiency of the porous integrating sphere is derived; Then, the light hole illuminance interference factor is introduced, and the effects of the aperture size of the integrating sphere, the radius of the integrating sphere, and the size and position of the baffle on the energy transmission efficiency and irradiation uniformity are analyzed. Finally, an integrating sphere uniform light system with high energy transmission efficiency and uniformity is designed and simulated with LightTools software. The simulation results show that: the average difference of irradiation uniformity and the range difference of irradiation uniformity are closest to 1 in the area where the ratio of the opening size and radius of the integrating sphere is 36%–40%, and the light is evenly distributed; When the baffle position is 0.8 times of integrating sphere radius away from the light entrance hole, irradiation non-uniformity at the outlet hole is 1.99%, which is the best position of the baffle; In the effective irradiation surface of Φ1032 mm, the irradiation non-uniformity is 1.98%, and the maximum irradiance is 0.62 solar constants, the spot simulation with high irradiation uniformity on large irradiation surface is realized.

Principle and Verification of an Improved Algorithm for Cable Fault Location Based on Complex Reflection Coefficient Spectrum

Diagnostic methods for fault location in cable insulation have become increasingly vital with the development of long-distance cables. This article presents an improved method for fault location based on the complex reflection coefficient spectrum (CRCS). First, a mathematical model for a long high-voltage cable line with consideration of a distribution-parameter circuit of cable joints is established in the frequency domain, and the CRCS of the cable with joints is analyzed. Next, based on the principle of frequency domain reflectometry (FDR), an algorithm in time-frequency conversion which uses the CRCS as the analysis spectrum is proposed. And the positioning sensitivity of the method is improved by implementing a data process adopting a Chebyshev window function which is controllable and reduces the sidelobe effect effectively. Furthermore, the improved method is evaluated by performing the simulations and two field verification tests. In a field test for a 5.1-km-long 110 kV cable, the multiple cable joints could be located effectively, and the relative error is between 0.14% and 1.49%. Then, the method was conducted for the positioning of a high-resistance fault and multiple joints on a 2.7-km-long 110 kV cable and the relative error is 0.37%. It is worth noting that the cross-bonding links of both cable tests were artificially replaced with temporary links straight across each joint. For the cross-bonded cable system, the test result shows that an obvious reflection is found at the cross-bonding point, which can reduce the reliability and accuracy of the present method.

A Sidelobe-Aware Small Ship Detection Network for Synthetic Aperture Radar Imagery

Ship detection from synthetic aperture radar (SAR) remote sensing images is essential for monitoring water traffic and marine safety. Numerous methods for ship detection have been developed; however, the detection of small ships presents unique challenges. SAR image characteristics, such as the sidelobe effect and blurred outline induced by the special imaging mechanism, as well as the small ship size, are the primary factors that lower the detection accuracy. This paper provides a sidelobe-aware small ship detection network for synthetic aperture radar imagery. First, considering the sidelobe effect and blurred outline, dual-pooling, i.e., average pooling and max pooling, was utilized to build a feature extraction module that lowered the effects of strong scattering points outside of the ship body and enhanced the ship body information. Second, as the bipartition process of the average pooling and maximum pooling caused some loss of original data information, different feature maps in the network were concatenated to construct a new network structure to compensate for the information lost and enrich the small ship features. Third, because the traditional loss function based on centroid distance and aspect ratio may result in the same loss function value for different prediction box sizes, a novel loss function based on the dual Euclidean distances of the corner point coordinates between the prediction box and the real box was proposed, which could accurately describe various overlapping box situations. Experiments using the Large-Scale SAR Ship Detection Dataset (LS-SSDD), SAR Ship Detection Dataset (SSDD), and AIR-SARShip dataset validated the efficacy and state-of-the-art performance.

Localization of Multiple Directional Transmitters in Cognitive Radio Context

Localization of transmitters has been gaining interest increasingly. Due to the emergence of directional transmitters in future technologies, localization in this manner is particularly considered. However, limited studies consider the localization of multiple directional transmitters. Our proposed system model describes multiple directional emitters by the means of a Uniform Linear Array (ULA). To localize these transmitters, we adapt the classical Direction of Arrival (DoA) localization techniques with our model. The localization accuracy is studied for multiple affecting parameters and compared to the theoretical limit of Cramer Rao Bound (CRB). The results show that a directional transmitter can be localized by the means of its high sidelobe effects. Root MUSIC outperforms other techniques in localizing the users.

SATURN: A Double-Recursive Deconvolution Algorithm for Suppressing Sidelobe Effects in Non-Nyquist SAR and MIMO Imaging Radars

SATURN: A Double-Recursive Deconvolution Algorithm for Suppressing Sidelobe Effects in Non-Nyquist SAR and MIMO Imaging Radars

Bi-Static Radar Cross-Section Test Method by using Historic Marconi Set-up and Time Gating

In this paper, a low-cost, simple, and reliable bi-static radar cross-section (RCS) measurement method by using a historic Marconi set-up is presented. It uses a transmitting (Tx) antenna (located at a constant position, at a reference angle of θ=0∘θ=0∘) and a moving receiver (Rx) antenna. A time gating method is used to extract the information from the reflection in the time domain; applying time filter allows removing the antenna side-lobe effects and other ambient noises. In this method, the Rx antenna (on the movable arm) is used to measure the reflected field in the angular range from 1∘1∘ to 90∘90∘ from the structure (printed circuit board, PCB) and the reference configuration represented by a ground (GND) plane of the same dimension. The time gating method is then applied to each pair of PCB/GND measurements to extract the bi-static RCS pattern of the structure at a given frequency. Here, a comparison of measurement results is carried out at 18 and 32 GHz with simulation ones indicating successful performance of the proposed method. It can be used as a low-cost, reliable, and available option in future measurement and scientificresearch.

Sidelobe Suppression Beamforming Using Tapered Amplitude Distribution for a Microwave Power Transfer System with a Planar Array Antenna

This paper proposes a sidelobe suppression beamforming scheme using tapered amplitude distribution for microwave power transfer (MPT) with a two-dimensional planar array antenna (PAA). To overcome the undesirable effects of sidelobes, the magnitude of the sidelobes can be lowered by manipulating the amplitude distribution of the PAA. By analyzing the array factor of the PAA, we expressed the amplitude distribution as the product of two one-dimensional vectors. Moreover, we developed a method for obtaining these vectors from an expected sidelobe level (SLL). We used the Chebyshev taper, which has a higher directivity than other distributions given an expected SLL, for the amplitude distribution. To verify the proposed method, we designed and implemented a digital beamforming system with a 4 × 4 transmitter (Tx) array and a receiver (Rx) at 5.8 GHz. Compared with a uniform amplitude distribution, the proposed method reduced the SLL by 7.5 dB but the received RF power by only 0.6 dB. We compared the performance of the proposed MPT system with the results of previous studies.

Moon Imaging Technique and Experiments Based on Sanya Incoherent Scatter Radar

This article introduces the experiment design for Moon imaging based on Sanya incoherent scatter radar (SYISR) and algorithm research in data processing. The peak power of SYISR is 2 MW. The transmitted frequency used for Moon imaging experiments is 430 MHz. We conducted Moon imaging experiments using two types of waveforms, 13-bit Barker code, and linear frequency modulation (LFM) chirp. Considering both resolution and signal-to-noise ratio (SNR), the use of an LFM chirp with a bandwidth of 0.3 MHz and a pulsewidth of 2 ms can give higher SNR and resolution for Moon imaging using SYISR. Several key techniques were applied in the experiment design and data processing: 1) for the reliability of the imaging algorithm, the range-Doppler imaging algorithm commonly used in synthetic aperture imaging was applied; 2) to avoid the sidelobe effect of the 13-bit Barker code matched filter, a sidelobe-free filter was used; and 3) to mitigate the problem of “north–south ambiguity,” mosaic imaging of the Doppler northern and southern hemispheres of the nearside of the Moon was adopted. Two types of imaging results are obtained: mosaic images of the northern and southern hemispheres of the Moon and local regional images. The results demonstrate the feasibility and reliability of Moon imaging based on SYISR, which enables potential further lunar geology investigations in the future.