Auxiliary Array Research Articles

Method for DOA Estimation of Coprime Arrays Through Joint Auxiliary Arrays with Atomic Norm Minimization in the Presence of Gain-Phase Errors

Method for DOA Estimation of Coprime Arrays Through Joint Auxiliary Arrays with Atomic Norm Minimization in the Presence of Gain-Phase Errors

Applying auxiliary arrays for mainlobe blanking to counter mainlobe repeater jamming

Mainlobe repeater jamming exploits mainlobe gain of radar antenna and signal-processing gain to significantly disrupt the target detection performance of radar. The combination of multiple auxiliary radars to form a distributed array radar improves the radar system’s angle resolution, enabling the realization of traditional spatial jamming cancellation. However, the jamming cancellation method struggle to effectively suppress multiple mainlobe repeater jamming signals when their number exceeds that of auxiliary radars, resulting in pattern distortion and subsequent loss of target energy. To address this issue, the mainlobe blanking method with auxiliary radars is proposed based on the specific characteristics of the jamming signals being distributed in multiple discontinuous peaks at the same angle. This approach leverages the superior angular resolution to accurately differentiate between target and jamming signals based on their distance-angle characteristics. Moreover, specific jamming suppression methods are employed for each jamming signal based on whether it overlaps with the target in the time domain, minimizing the loss of target energy. Furthermore, the proposed method is capable of suppressing jamming even when the target and jamming signals overlap in the time domain, and is not constrained by the number of auxiliary radars available. Simulations and experiments are used to validate the effectiveness and correctness of the proposed method in suppressing mainlobe repeater jamming.

Analysis and Design of Auxiliary Array With Intelligent Optimization for Interference Suppression of Vehicular Communication

Analysis and Design of Auxiliary Array With Intelligent Optimization for Interference Suppression of Vehicular Communication

Enhanced Quantum Control of Individual Ultracold Molecules Using Optical Tweezer Arrays

Control over the quantum states of individual molecules is crucial in the quest to harness their rich internal structure and dipolar interactions for applications in quantum science. In this paper, we develop a toolbox of techniques for the control and readout of individually trapped polar molecules in an array of optical tweezers. Starting with arrays of up to eight Rb and eight Cs atoms, we assemble arrays of RbCs molecules in their rovibrational and hyperfine ground state with an overall efficiency of 48(2)%. We demonstrate global microwave control of multiple rotational states of the molecules and use an auxiliary tweezer array to implement site-resolved addressing and state control. We show how the rotational state of the molecule can be mapped onto the position of Rb atoms and use this capability to readout multiple rotational states in a single experimental run. Further, using a scheme for the midsequence detection of molecule formation errors, we perform rearrangement of assembled molecules to prepare small defect-free arrays. Finally, we discuss a feasible route to scaling to larger arrays of molecules. Published by the American Physical Society 2024

Synthesis of Elliptically Shaped Aperiodic Antenna Arrays Subject to Complex Design Constraints

AbstractState‐of‐the‐art conformal array pattern synthesis techniques typically suffer from a significant computational burden. This article builds upon the auxiliary array pattern synthesis technique, allowing the technique to work on elliptical contours. This deterministic method is both computationally efficient and capable of handling complex design cases with additional design constraints. In order to highlight the versatility and applicability, the method has been applied to an elliptical arc array comprised of Rampart antennas and an elliptical ring array using dipole elements. The results have been validated through full‐wave electromagnetic simulations, as well as physical measurements. Both arrays presented show good agreement with the proposed synthesis technique. Highlighting the ability to satisfy additional design constraints, such as enforcing a minimal element spacing or having a controlled beamformer‐network complexity.

A Robust Sidelobe Cancellation Algorithm Based on Beamforming Vector Norm Constraint

Sidelobe cancellation (SLC) is a well-established beamforming technique for mitigating interference, particularly in the context of satellite communication (SATCOM). However, traditional SLC suffers from the issue of partially canceling the desired signal at high signal-to-noise ratio (SNR), primarily due to unconstrained beamforming processing. Extensive research has been conducted to address this problem; however, existing algorithms have limitations such as dependence on knowledge of signal array vectors or number of interferers and involve high computational complexity. In this paper, we propose a robust SLC algorithm based on beamforming vector norm constraint. Our proposal offers a practical solution by only requiring knowledge of the earth station antenna gain and maximum auxiliary array gain to the desired signal, both of which are fully known. Furthermore, compared to traditional SLC, our proposed method introduces additional computational complexity that only scales linearly with the size of the auxiliary array. Simulation results demonstrate comparable performance between our proposed method and existing techniques such as diagonal loading and spatial degrees-of-freedom control-based algorithms.

Mainlobe and sidelobe jamming suppression method based on distributed array radar with eigen‐projection matrix processing and null constraints

AbstractThe jamming in electronic warfare environment significantly degrades the performance of the radar system, especially the mainlobe jamming. The distributed array radar can suppress the mainlobe jamming by increasing the array aperture with auxiliary arrays. However, when the aperture of an auxiliary array is smaller than that of the main array, it results in residual jamming in auxiliary arrays after beamforming and affects the performance of the radar system. To tackle this issue, a mainlobe and sidelobe jamming joint suppression method with eigen‐projection matrix processing (EMP) and null constraints is proposed. In the proposed method, firstly, the adaptive beamforming with EMP is performed in main and auxiliary arrays to suppress the sidelobe jamming with mainlobe maintenance. Then, the null constraints based on the Capon spectrum of main array are applied to suppress the residual jamming in auxiliary arrays. Finally, the minimum mean square error (MMSE) beamforming is utilised to cancel the mainlobe jamming. Simulation results demonstrate the jamming suppression performance of the proposed method.

Direction-of-arrival estimation for a nested array with gain-phase error and impulse noise

To solve direction-finding problems for nested arrays under conditions of impulse noise and gain–phase errors, this paper proposes a direction-of-arrival (DOA) estimation method. First, a correntropy with an adaptive kernel length combined with an infinite norm of a variable weight value is proposed to suppress impulse noise. Then, the auxiliary array elements are used to realize the initial calibration of the amplitude and phase errors. On this basis, a DOA estimation method without a spectral peak search is proposed; this method is robust for arrays that still have residual gain–phase errors after calibration. Our method overcomes the difficulties of DOA estimation for nested arrays under conditions of gain–phase errors and impulse noise. Compared with traditional methods, the proposed method does not require prior knowledge, does not require a spectral peak search and has high superiority and robustness; a series of experiments have verified its superiority. In addition, a Cramér‒Rao bound (CRB) for angle estimation under conditions of gain–phase errors and impulse noise is proposed in this paper.

Integrating Intelligent Reflecting Surface Into Base Station: Architecture, Channel Model, and Passive Reflection Design

Intelligent reflecting surface (IRS) has emerged as a cost-efficient technique to improve the wireless network’s capacity and performance. Existing works on IRS have mainly considered IRS being deployed in the environment to dynamically control the wireless channels between the base station (BS) and its served users in favor of their communications. In contrast, we propose in this paper a new integrated IRS-BS architecture by deploying IRSs inside the BS’s antenna radome to directly reconfigure the signal radiation to/from the BS’s antennas. In other words, the IRSs can be considered as auxiliary passive arrays with real-time reconfigurability equipped at the BS to enhance its communication performance cost-effectively. Since the distance between the integrated IRSs and BS’s antenna array is practically small (in the order of several to tens of wavelengths), the path loss among them is significantly reduced as compared to conventional IRS deployed much farther away from the BS, while the real-time control of the IRS’s reflection by the BS becomes easier to implement. However, the resultant near-field channel model also becomes drastically different from its far-field counterpart for conventional far-away IRSs in the literature. Thus, we propose an element-wise channel model for IRS to characterize the channel vector between each single-antenna user and the antenna array of the BS, which includes the direct (without any IRS’s reflection) as well as the single and double IRS-reflection channel components. Based on this channel model, we formulate a problem to optimize the reflection coefficients of all IRS reflecting elements for maximizing the uplink sum-rate of the users. By considering two typical cases with/without perfect channel state information (CSI) at the BS, the formulated problem is solved efficiently by adopting the successive refinement method and iterative random phase algorithm (IRPA), respectively. Numerical results validate the substantial capacity gain of the integrated IRS-BS architecture over the conventional multi-antenna BS without integrated IRS. Moreover, the proposed algorithms significantly outperform other benchmark schemes in terms of sum-rate, and the IRPA without CSI can approach the performance upper bound with perfect CSI as the training overhead increases.

Magnetic signal denoising based on auxiliary sensor array and deep noise reconstruction

Magnetic field measurement technologies are extensively applied in nondestructive testing, quality inspection, and fault diagnosis of machines and equipment. Magnetic signals are highly susceptible to noise interference, and how to effectively conduct signal denoising under strong noise disturbance remains a challenge. This study designs a novel auxiliary sensor array comprising center and distributed satellite sensors for magnetic noise reconstruction and signal denoising. The signals of the center and satellite sensors are highly correlative owing to the propagation of the magnetic noise through space. An attention-based long short term memory model is designed and trained to establish the mapping relationship among the center and satellite signals. The noise of the center sensor can then be reconstructed from those of the satellite sensors in the procedure of magnetic field measurement. Finally, the denoised signal of the center sensor is obtained by subtracting the noisy signal with the reconstructed noise. The effectiveness and superiority of the proposed method are validated by experiments and compared with the conventional methods. The designed auxiliary sensor array and algorithm are easily implemented with remarkable robustness, flexibility, and scalability, thereby showing greatly potential use for magnetic signal denoising in a variety of mechatronic systems.

A Digitally assisted nonlinearity suppression architecture using over‐the‐air beam cancelation

AbstractUsing a large number of power amplifiers brings serious nonlinear distortion to 5G communication systems, which becomes a challenging problem for conventional digital predistortion (DPD) to linearize signals. To address this challenge, we propose a digitally assisted nonlinearity suppression architecture for applications in multiple antenna arrays. In this architecture, we use an auxiliary array to generate a nonlinear cancelation signal to cancel with the main beam at the receiving end. We explore a two‐step method to model the nonlinear signal and combine neural network to complete the modeling. In addition, we introduce a novel method for estimating and tracking the channel to update the model coefficients. To verify the proposed methods, we design a scene and compare the performance of our architecture with that of conventional DPD. Our experiments demonstrate that our proposed methods can significantly enhance the adjacent channel power leakage ratio performance by 12 dB while also optimizing in‐band distortion. Moreover, our architecture outperforms conventional DPD approaches even with enhanced distortion.

Signal-Independent Background Calibration with Fast Convergence Speed in Pipeline-SAR ADC

This brief proposes a signal-independent background calibration in pipeline-SAR analog-to-digital converters (ADCs) with a convergence-accelerated technique. To achieve signal independence, an auxiliary capacitor array CA is introduced to pre-inject a pseudo-random noise (PN) in the sampling phase to cancel out the opposite PN injection of the calibrated capacitor in the conversion phase, and CA is also used to realize the D/A function of the calibrated capacitor in the conversion phase. In this way, no matter what the signal is, the residue headroom remains unchanged even with PN injection. Moreover, the first sub-ADC is designed with extended conversion bits to quantize its own residue after delivering the conversion bits required by the first stage. Afterwards, this result is provided to the calibration algorithm to reduce the signal component and accelerate the convergence. Based on the simulation, the signal-to-noise and distortion ratio (SNDR) and spur-free dynamic range (SFDR) improve from 45.3 dB and 56.4 dB to 68.2 dB and 88.4 dB, respectively, after calibration. In addition, with the acceleration technique, convergence cycles decrease from 1.7 × 108 to 5.8 × 106. Moreover, no matter whether the input signal is DC, sine wave or band-limited white noise, the calibration all works normally.

Elements Selection for Auxiliary Array in the Adaptive Sidelobe Canceller Radar System

In a recent paper, the conventional sidelobe canceller radar system was developed by replacing the separate auxiliary antennas by few elements at the center of the main antenna array. The modified system with reused elements was associated with some attenuation in the desired signal due to the emerging correlation between the signals that exists in the main and the reused array elements. This problem was solved by imposing some constraints on the array pattern of the reused elements. In this paper, few of the side elements of the main array are employed as the auxiliary antennas. This new proposed configuration is called sided-elements. Unlike the previous centered-elements configuration, the proposed sided-elements configuration offers more desired features since the pattern of the side elements has sidelobes of similar widths of those of the main array. Moreover, a better diversity is obtained due to the wider separation between the two groups of elements at both sides of the main array. Simulation results fully confirm the effectiveness of the new proposed sided-elements configuration for suppressing the undesired interfering signals and retaining the desired signal undistorted.

Semi-blind calibration for orbital angular momentum generating system using maximal likelihood estimation.

Generating a high quality vortex beam carrying orbital angular momentum (OAM) is of importance in the application of the OAM to target detections or communications. This paper presents a semi-blind method to calibrate the uniform circle array (UCA) OAM generating system by using a virtual receiving sensor array without knowing the precise position of this auxiliary array. A low computational complexity algorithm is proposed to solve the maximum likelihood estimate (MLE) for the amplitude and phase errors of the transducers of the UCA based on the separation of parameters to be estimated. Moreover, this MLE is proved to be related to the beamforming of the virtual array. Simulations indicate that this method can estimate the mismatch parameters of the UCA effectively, and experiments in an anechoic tank show that the OAM generating system can generate a desirable vortex beam after we compensate each transducer of the UCA.

Photonic integrated interferometric imaging based on main and auxiliary nested microlens arrays.

The traditional microlens array imager lacks content information and has low imaging quality when restoring the image of the target, which limits the development of photon integrated detection and imaging technology. Therefore, this paper proposes a new structure of the microlens array optical frequency detection imager, wherein the main microlens array is embedded into the auxiliary microlens array to achieve high-quality restoration of the target. The simulation results show that the proposed structure increased the peak signal-to-noise ratio (PSNR) of the restored image by 21.05% and reduced the mean square error (MSE) by 29.39%. The microlens array with the main and auxiliary nested structure can achieve high-quality imaging of the target and has great application potential in the field of target detection.

Low-Complexity 2D DOA Estimation and Self-Calibration for Uniform Rectangle Array with Gain-Phase Error

Most subspace-based algorithms need exact array manifold for direction of arrival (DOA) estimation, while, in practical applications, the gain-phases of different array elements are usually inconsistent, degrading their estimation performance. In this paper, a novel low-complexity 2D DOA and gain-phase error estimation algorithm is proposed by adding auxiliary array elements in a uniform rectangular array (URA). Firstly, the URA is modeled as the Kronecker product of two uniform linear arrays (ULAs) to decouple the 2D DOA estimation. Then, several well-calibrated auxiliary array elements are added in the two ULAs, based on which the rotation invariant factor of the URA destroyed by the gain-phase error is reconstructed by solving constrained optimization problems. Lastly, ESPRIT is used to estimate the 2-D DOA and the gain-phase error coefficients. The closed-form expressions of the estimation CRBs are also derived, providing insight into the impact of gain-phase error on DOA estimation. Simulation results are used to validate the effectiveness of the proposed algorithm and the correctness of the theoretical analysis.

An Anti-Main-Lobe Jamming Algorithm for Airborne Early Warning Radar based on APC-SVRGD Joint Optimization

Main lobe jamming seriously affects the detection performance of airborne early warning radar. The joint processing of polarization-space has become an effective way to suppress the main lobe jamming. To avoid the main beam distortion and wave crest migration caused by the main lobe jamming in adaptive beamforming, a joint optimization algorithm based on adaptive polarization canceller (APC) and stochastic variance reduction gradient descent (SVRGD) is proposed. First, the polarization plane array structure and receiving signal model based on primary and auxiliary array cancellation are established, and an APC iterative algorithm model is constructed to calculate the optimal weight vector of the auxiliary channel. Second, based on the stochastic gradient descent principle, the variance reduction method is introduced to modify the gradient through internal and external iteration to reduce the variance of the stochastic gradient estimation, the airspace optimal weight vector is calculated and the equivalent weight vector is introduced to measure the beamforming effect. Third, by setting up a planar polarization array simulation scene, the performance of the algorithm against the interference of the main lobe and the side lobe is analyzed, and the effectiveness of the algorithm is verified under the condition of short snapshot number and certain signal to interference plus noise ratio.

Impact of Asymmetric Weight Update on Neural Network Training With Tiki-Taka Algorithm.

Recent progress in novel non-volatile memory-based synaptic device technologies and their feasibility for matrix-vector multiplication (MVM) has ignited active research on implementing analog neural network training accelerators with resistive crosspoint arrays. While significant performance boost as well as area- and power-efficiency is theoretically predicted, the realization of such analog accelerators is largely limited by non-ideal switching characteristics of crosspoint elements. One of the most performance-limiting non-idealities is the conductance update asymmetry which is known to distort the actual weight change values away from the calculation by error back-propagation and, therefore, significantly deteriorates the neural network training performance. To address this issue by an algorithmic remedy, Tiki-Taka algorithm was proposed and shown to be effective for neural network training with asymmetric devices. However, a systematic analysis to reveal the required asymmetry specification to guarantee the neural network performance has been unexplored. Here, we quantitatively analyze the impact of update asymmetry on the neural network training performance when trained with Tiki-Taka algorithm by exploring the space of asymmetry and hyper-parameters and measuring the classification accuracy. We discover that the update asymmetry level of the auxiliary array affects the way the optimizer takes the importance of previous gradients, whereas that of main array affects the frequency of accepting those gradients. We propose a novel calibration method to find the optimal operating point in terms of device and network parameters. By searching over the hyper-parameter space of Tiki-Taka algorithm using interpolation and Gaussian filtering, we find the optimal hyper-parameters efficiently and reveal the optimal range of asymmetry, namely the asymmetry specification. Finally, we show that the analysis and calibration method be applicable to spiking neural networks.

Evidence for enhanced neutron-proton correlations from the level structure of the N=Z+1 nucleus Tc444387

The low-lying excited states in the neutron-deficient N=Z+1 nucleus Tc444387 have been studied via the fusion-evaporation reaction Fe54(Ar36, 2n1p)Tc87 at the Grand Accélérateur National d'Ions Lourds (GANIL), France. The AGATA spectrometer was used in conjunction with the auxiliary NEDA, Neutron Wall, and DIAMANT detector arrays to measure coincident prompt γ rays, neutrons, and charged particles emitted in the reaction. A level scheme of Tc87 from the (9/2g.s.+) state to the (33/21+) state was established based on six mutually coincident γ-ray transitions. The constructed level structure exhibits a rotational behavior with a sharp backbending at ℏω≈0.50 MeV. A decrease in alignment frequency and increase in alignment sharpness in the odd-mass isotonic chains around N=44 is proposed as an effect of the enhanced isoscalar neutron-proton interactions in odd-mass nuclei when approaching the N=Z line.Received 16 April 2021Revised 5 July 2021Accepted 9 August 2021DOI:https://doi.org/10.1103/PhysRevC.104.L021302Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Funded by Bibsam.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEnergy levels & level densitiesNuclear structure & decaysNuclear Physics

A Unified Framework of adaptive sidelobe canceller design by antenna/subarray selection

This paper proposes a unified framework of adaptive sidelobe canceller (SLC) design, specifically the optimization of auxiliary array configuration in terms of two commonly-used metrics under both structures of antenna reuse and array splitting via antenna/subarray selection. Concerning the cancellation of desired signal caused by conventional SLCs, we first propose two new methods to design the beamforming weights for SLCs. For the first metric of minimum output power, a regularization penalty on signal cancellation is added. For the second metric of beampattern matching, additional zero-forcing constraint of auxiliary beampattern is imposed. Both modified methods aim to maximize the output signal to interference-plus-noise ratio (MaxSINR) under certain constraints. To further enhance the performance of these two methods, we optimize the auxiliary array configuration in terms of MaxSINR. We conduct the optimal SLC design under two structures of antenna reuse and array splitting, where both antenna and subarray selection are leveraged for auxiliary array construction. Furthermore, the optimum SLC design problem is formulated into convex optimization, which in turn can be readily solved using off-the-shelf commercial software or toolbox. Comparative simulation results demonstrate the optimality and superiority of the proposed adaptive SLC.