Haining Yang

Pattern synthesis for large-scale spherical array antennas (SAA) has traditionally been computationally intensive. To enable flexible beam pattern control in SAAs, a real-time pattern synthesis method integrating the fast spherical Fourier transform (FSFT) with a lightweight deep neural network (LDNN) is proposed in this paper. The framework comprises three phases: sample database generation, neural network training, and online prediction. To efficiently collect training data, the direct and inverse Fourier transform relationship between the excitation distribution and the array pattern of a SAA is established using spherical harmonics, thereby extending the traditional fast Fourier transform (FFT) pattern synthesis algorithm, which is typically applied only to planar arrays, to SAAs. Numerical simulation results demonstrate that the proposed method based on the FSFT achieves significantly lower computational complexity compared with existing methods. In the LDNN training phase, an end-to-end model is introduced to approximate the input-output mapping of the FSFT-based method. An iterative training-pruning mechanism is employed to minimize the redundancy of the neural network. Simulation results show that the obtained LDNN achieves a computational complexity reduction of over 50% in the online prediction process. For a SAA with 1196 elements, the focused beam patterns are synthesized in just 35 μs.

A novel non-fully filled 1-bit amplitude-encoded sparse phased array (ESPA) is proposed to enhance both boresight efficiency and wide-angle scanning performance. An ideal subarray gain degradation model was established, and power divider non-idealities were incorporated to evaluate the performance of different excitation schemes. The [1,1,0,0] “two-on–two-off” configuration was identified as the optimal structure, offering a simplified feed network while compensating for boresight gain loss through parasitic element coupling. Six excitation states were defined as “encoding blocks” and used to construct array topologies. An Encoding Autocorrelation Function (EAF) was introduced to quantify spatial regularity and suppress grating lobes. Sparse layouts were optimized using a discrete simulated annealing algorithm. Full-wave simulations of an 8×8 array with parasitic patches placed at inactive positions showed that the proposed 1-bit ESPA improves boresight gain by approximately 1.0 dB compared to existing wide-angle sparse subarray schemes, with more than 1.5 dB enhancement at θ = 45°, while maintaining good polarization purity and wide scan angles. These results validate the proposed approach, demonstrating its advantages in both efficiency and wide-angle performance.

In this study, a 108‐element L‐band multiple beamforming digital phased array with transceiver shared aperture for LEO satellite communication is presented. A digital beam multiplexing and demultiplexing strategy is proposed to optimize elemental digital phased array architecture, which reduces the required numbers of ADCs and DACs to 33%. To enhance calibration efficiency and feasibility, a channel automatic calibration approach is proposed by incorporating transmit and receive calibration channels along with an internal reference signal generator. The array is capable to generate 56 beams based on 12 frequency multiplexing. Experimental results demonstrate that the beam coverage reaches as high as 99.91% at 25 dBW signal effective isotropic radiated power, and achieves up to 99.93% coverage at −7.8 dB/K, the ratio of gain‐to‐noise temperature.

In this letter, an FIR filter based wideband digital array mutual coupling parameter estimation method (FBWMCE) is proposed. FBWMCE establishes a frequency-dependent model of mutual coupling parameters using FIR filters. By estimating the coefficients of FIR filters associated with the mutual coupling parameters and obtaining accurate estimates at any frequency within the passband, FBWMCE effectively mitigates the impact of mutual coupling on wideband beamforming performance. Simulation results demonstrate that FBWMCE achieves a rootmean-square error of 0.18 in mutual coupling estimation under an SNR of 10 dB. Furthermore, after applying FBWMCE for decoupling, there is a remarkable reduction in wideband multibeam pointing error from 3° to 0.3°.

In this work, an outage analysis-based transceiver digital beamforming method is proposed in the presence of deep-fading in high-speed railway millimeter-wave (mmW) communication. Facing the communication outage arising from deep-fading, which is produced by the superposition of phase-reversed two-ray signals, a two-ray mmW channel is formulated to minimize the outage probability by jointly optimizing the transmitter and receiver beamforming. Unlike the conventional diversity method, the proposed method exploits the prior two-ray mmW channel state information for the adaptive beamforming of the transmitter and receiver for much more effective deep-fading suppression. The performance of the proposed method is evaluated in multiple perspectives, the receiving powers, outage probabilities in different ranges, and complexity analysis, finding out that the proposed method could decrease the outage probability from 0.95 to 0.19 within the range of 2000 m, and the proposed method could converge within maximally 30 iterations, showcasing the promised performance of the proposed method.

This paper introduces a novel single-layer, dual-band circularly polarized reflectarray (RA) with the unique ability to generate three beams. Incorporated within the design approach are the variable rotation technique (VRT) and the element size variation technique (ESVT), both optimized to yield superior radiation performance. The fabricated prototype exhibits beams at elevation angles of 9° for right-hand circular polarization (RHCP), and 15° for left-hand circular polarization (LHCP) within the Ka-band (27.5-30.5 GHz). Moreover, in the K-band (18.7-20.2 GHz), an additional beam for RHCP can be generated at an elevation angle of 12°. A meticulous optimization process was conducted across all element configurations to minimize cross-polar reflection. Based on comprehensive simulation and precise measurement results, the reflect-array proves to suppress cross-polarization efficiently below 19.0 dB, while simultaneously attaining a 1-dB gain bandwidth exceeding 8.2% in both bands. These features enhance the application potential in wireless communication systems, particularly in dual-band and beam control scenarios.

In this letter, a waterflow velocity detection method with a 24 GHz millimetre-wave radar is presented based on a modified matrix-pencil (MP) method to alleviate the issue of stochastic waterflow surface fluctuation-induced Doppler shifts aliases in the received echo. Initially, by leveraging the echo superposition characteristics of scattering point targets, the millimetre-wave radar echo exponential polynomial model is established. Subsequently, the MP method is modified to find the low-rank approximation of the received echo for waterflow velocity estimation. The results of the 24 GHz millimetre-wave radar experiments show that the mutual difference between the modified MP-detected waterflow velocities in two azimuths is 0.036 m/s, which demonstrate the better validity and efficacy of the proposed method compared with the traditional compressive sensing and singular value decomposition methods.

The drift compensation of gas sensor systems is an important topic in the artificial olfactory community. The drift is arisen by multiple factors, i.e., the change of temperature and moisture, poison effect, manufacture repeatability, etc. It would arise the bias, distortion and offset of data, and lead it to be unrecognized. Conventionally, the calibration methods in hardware level are laborious and ineffective, and many proposed baseline software methods are not effective enough due to loss of structure information. In this work, a classifier-level nonlinear manifold transfer extreme learning machine (NMT-ELM) is proposed, which intends to learn the manifold information embedded into the source domain data, and accomplish the structure information preservation in a nonlinear style. In addition, the domain adaptation between source and target domain is adopted as well, which is utilized for effective distribution alignment. The output matrix of extreme learning machine (ELM) is determined with supervised learning, and the generalization is enhanced effectively as well. Moreover, a novel intelligent optimization algorithm entitled Cuckoo Searching (CS) is introduced, in order to explore the optimal parameters of NMT-ELM rapidly comparing with the conventional grid searching. The validation is proved by experiments on different open-set E-nose data, and the classification accuracies comparison demonstrates its effectiveness on drift compensation of E-nose.

In this paper, a spurious peak suppression direction-of-arrival (DOA) estimation method based on test of orthogonality of projected subspaces (SPS-TOPS) is proposed for wideband digital phased arrays in the present of mutual coupling. In contrast to the frequency focusing approach of coherent signal subspace method (CSSM), SPS-TOPS, based on the original TOPS, estimates DOA by evaluating the orthogenicity between rotated signal subspace and noise subspace at the corresponding frequency subband. In particular, the proposed method considers the impact of mutual coupling among array elements on subspace-based methods, and incorporates auxiliary array elements to avoid changes in the array manifold. Further, a detailed analysis of the spurious peaks in the original TOPS algorithm is conducted, and a spurious peak suppression function is constructed to eliminate spurious peaks in the DOA estimation spectrum. This improvement enhances both accuracy and reliability of the DOA estimation spectrum. Meanwhile, SPS-TOPS optimizes the metric calculation by utilizing Frobenius norm instead of singular value inverse, eliminating the need for SVD operation and significantly enhancing algorithmic efficiency. The comprehensive simulation results demonstrate that the DOA estimation root mean square error of SPS-TOPS is 0.1 $^\circ$ lower than CSSM in the present of mutual coupling while signal noise ratio is 10 dB. Compared with TOPS, the calculation time of SPS-TOPS is reduced from 28.7 s to 7 s when the number of elements is 32 and the spurious peaks are completely eliminated.

In this paper, an adaptive gradient projection correction (AGPC) method for resistivity array imaging logging is proposed. Due to the irregular spin and swing of the subsurface resistivity array instrument, the time-varying nonuniform sampling is induced between the electrodes, which reduces the resolution of the wellbore image. In the proposed method, the nonuniform sampled image is reconstructed via the proposed adaptive transform determined by gradient domain minimization constraint for image correction and least-square constraint for loss minimization. The imaging correction capabilities of AGPC are theoretically simulated and experimentally demonstrated in multiple scenes. The indexes of edges correction error and average gradient difference are devised and provided to assess the AGPC performance in simulations and field experiments. The results show that AGPC achieves better balance between image loss and resolution compared with traditional methods.