Vectorial structure of a hard-edged-diffracted four-petal Gaussian beam in the far field

Based on the vector angular spectrum method and the stationary phase method and the fact that a circular aperture function can be expanded into a finite sum of complex Gaussian functions, the analytical vectorial structure of a four-petal Gaussian beam (FPGB) diffracted by a circular aperture is derived in the far field. The energy flux distributions and the diffraction effect introduced by the aperture are studied and illustrated graphically. Moreover, the influence of the f-parameter and the truncation parameter on the nonparaxiality is demonstrated in detail. In addition, the analytical formulas obtained in this paper can degenerate into un-apertured case when the truncation parameter tends to infinity. This work is beneficial to strengthen the understanding of vectorial properties of the FPGB diffracted by a circular aperture

Analytical vectorial structure of non-paraxial four-petal Gaussian beams in the far field

The analytical vectorial structure of non-paraxial four-petal Gaussian beams(FPGBs) in the far field has been studied based on vector angular spectrum method and stationary phase method. In terms of analytical electromagnetic representations of the TE and TM terms, the energy flux distributions of the TE term, the TM term, and the whole beam are derived in the far field, respectively. According to our investigation, the FPGBs can evolve into a number of small petals in the far field. The number of the petals is determined by the order of input beam. The physical pictures of the FPGBs are well illustrated from the vectorial structure, which is beneficial to strengthen the understanding of vectorial properties of the FPGBs

Bulk Incorporation with 4‐Methylphenethylammonium Chloride for Efficient and Stable Methylammonium‐Free Perovskite and Perovskite‐Silicon Tandem Solar Cells

Methylammonium (MA)-free perovskite solar cells have the potential for better thermal stability than their MA-containing counterparts. However, the efficiency of MA-free perovskite solar cells lags behind due to inferior bulk quality. In this work, 4-methylphenethylammonium chloride (4M-PEACl) is added into a MA-free perovskite precursor, which results in greatly enhanced bulk quality. The perovskite crystal grains are significantly enlarged, and defects are suppressed by a factor of four upon the incorporation of an optimal concentration of 4M-PEACl. Quasi-2D perovskites are formed and passivate defects at the grain boundaries of the perovskite crystals. Furthermore, the perovskite surface chemistry is modified, resulting in surface energies more favorable for hole extraction. This facile approach leads to a steady state efficiency of 23.7% (24.2% in reverse scan, 23.0% in forward scan) for MA-free perovskite solar cells. The devices also show excellent light stability, retaining more than 93% of the initial efficiency after 1000 h of constant illumination in a nitrogen environment. In addition, a four-terminal mechanically stacked perovskite-silicon tandem solar cell with champion efficiency of 30.3% is obtained using this MA-free composition. The encapsulated tandem devices show excellent operational stability, retaining more than 98% of the initial performance after 42 day/night cycles in an ambient atmosphere

Space Time Adaptive Processing Technique for Airborne Radar: An Overview of Its Development and Prospects

Used to suppress strong clutter and jamming in airborne radar data, Space Time Adaptive Processing (STAP) is a multidimensional adaptive filtering technique that simultaneously combines signals from elements of an antenna array and multiple pulses of coherent radar waveforms. As a key technology for improving the performance of airborne radar, it has attracted much attention in the field of radar research and from powerful military nations in recent years. In this paper, the research and development status of STAP technology is reviewed including methodologies, experimental systems, and applications and we focus on the key technical problems encountered during its development. Then, the application of STAP technology in equipment is introduced. Finally, the next development trends, future directions, and areas worthy of further research are presented

Cross Beam STAP for Nonstationary Clutter Suppression in Airborne Radar

A novel space-time adaptive processing (STAP) method for nonstationary clutter suppression is proposed. The developed method forms a multibeam along the cross line to participate in adaptive processing, which sufficiently utilizes the spatial information both in azimuth and elevation and guarantees the least system degrees of freedom (DOFs). The characteristics of this structure help to suppress the short-range clutter which is the primary component of nonstationary clutter. Therefore, this method provides favorable clutter suppression performance when clutter range dependence exists. Approach analysis and simulation results are given to demonstrate the effectiveness of the method

Clutter Mitigation in Space-based Early Warning Radar Using a Convolutional Neural Network

Moving target indication using space-based early warning radar is important in military applications. For the space-based early warning radar, complicated non-stationary clutter characteristics are induced due to the high-speed movement of the radar platform and Earth’s rotation, and more serious clutter non-homogeneity than the airborne radar scenario is caused by the large beam illumination region. Consequently, traditional Space-Time Adaptive Processing (STAP) methods, which have been widely used in airborne early warning radar, cannot be applied directly to the space-based early warning radar. In this study, we analyze the characteristic of clutter distribution and build a novel STAP framework, where high-resolution clutter spectra used to construct the adaptive weights is estimated via a Convolutional Neural Network (CNN). First, clutter data sets were randomly simulated with different ranges of bin, latitude, spatial error, internal clutter motion, and coefficients of surface scattering, where the radar and satellite parameters were utilized as a priori knowledge. Then, we designed a two-dimensional CNN with five layers that converted a low-resolution clutter spectrum into a high-resolution spectrum. Finally, a space-time adaptive filter was calculated using the estimated high-resolution space-time spectrum and employed for clutter suppression and target detection. The simulation results show that the proposed CNN STAP can achieve sub-optimal performance under limited sample conditions, and a smaller computational load compared with a state-of-the-art sparse recovery STAP method. Therefore, this framework is suitable for practical application in space-based early warning radar

Fast Tensor-based Three-dimensional Sparse Bayesian Learning Space-Time Adaptive Processing Method

When airborne radar is applied to the non-side-looking mode, moving target detection performance considerably degrades because of the nonstationary clutter. Conventional three-dimensional (3D) Space-Time Adaptive Processing (STAP) can effectively eliminate the nonstationary clutter via adaptively constructing an elevation-azimuth-Doppler 3D filter. However, large system degrees of freedom lead to a shortage of training samples in a heterogeneous environment. Although introducing the Sparse Recovery (SR) technology substantially reduces the sample requirement, the practical application of this technology is limited by computational complexities. To solve the above problems, this paper proposes a fast 3D sparse Bayesian learning STAP, based on the third-order tensor structure of echo data. In the proposed method, large-scale matrix calculation is decomposed into small-scale matrix calculation using a low-complexity tensor-based operation, thus considerably reducing the computational load. Exhaustive numerical experiments verify that the proposed method directly reduces the computational load by several orders of magnitude compared with that of the existing SR-STAP algorithms, while maintaining the SR-STAP performance. Therefore, the tensor-based method is a superior processing method than the vector-based method in engineering

Robust Adaptive Beamforming Based on a Convolutional Neural Network

To address the advancements in jamming technology, it is imperative to consider robust adaptive beamforming (RBF) methods with finite snapshots and gain/phase (G/P) errors. This paper introduces an end-to-end RBF approach that utilizes a two-stage convolutional neural network. The first stage includes convolutional blocks and residual blocks without downsampling; the blocks assess the covariance matrix precisely using finite snapshots. The second stage maps the first stage’s output to an adaptive weight vector employing a similar structure to the first stage. The two stages are pre-trained with different datasets and fine-tuned as end-to-end networks, simplifying the network training process. The two-stage structure enables the network to possess practical physical meaning, allowing for satisfying performance even with a few snapshots in the presence of array G/P errors. We demonstrate the resulting beamformer’s performance with numerical examples and compare it to various other adaptive beamformers