Distributed Array Research Articles

Spatial Multi-Interference Suppression in Monopulse Radar With Reconfigurable Distributed Array Nodes

Spatial Multi-Interference Suppression in Monopulse Radar With Reconfigurable Distributed Array Nodes

Super-resolution imaging quality enhancement method for distributed array infrared camera

Abstract To address issues related to low resolution and high noise in infrared cameras, a distributed array infrared camera imaging system utilizing four cameras is proposed. The four cameras are arranged in an unconstrained array, and the combination algorithm of Projections onto Convex Sets (POCS) and Real-Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN) is applied to achieve high-quality super-resolution infrared imaging. The wavelet fusion algorithm is used to preprocess four low-resolution infrared images to reduce noise. Then, the POCS algorithm is used to reconstruct the preprocessed image. Finally, the Real-ESRGAN is employed for image reconstruction, resulting in an ultra-high-resolution infrared image. The results show that compared to single infrared camera imaging, the resolution of images reconstructed using the distributed infrared camera array is increased by 0.58 times, with the Modulation Transfer Function (MTF) increased by 1.2 times. Additionally, the entropy is increased by 18.87%, the standard deviation is increased by 13.51%, and the Naturalness Image Quality Evaluator (NIQE) is reduced by 18.87%. This demonstrates a significant enhancement in the super-resolution imaging quality of the distributed infrared camera array.

High-Resolution Collaborative Forward-Looking Imaging Using Distributed MIMO Arrays

Airborne radar forward-looking imaging holds significant promise for applications such as autonomous navigation, battlefield reconnaissance, and terrain mapping. However, traditional methods are hindered by complex system design, azimuth ambiguity, and low resolution. This paper introduces a distributed array collaborative, forward-looking imaging approach, where multiple aircraft with linear arrays fly in parallel to achieve coherent imaging. We analyze signal model characteristics and highlight the limitations of conventional algorithms. To address these issues, we propose a high-resolution imaging algorithm that combines an enhanced missing-data iterative adaptive approach with aperture interpolation technique (MIAA-AIT) for effective signal recovery in distributed arrays. Additionally, a novel reference range cell migration correction (reference RCMC) is employed for precise range–azimuth decoupling. The forward-looking algorithm effectively transforms distributed arrays into a virtual long-aperture array, enabling high-resolution, high signal-to-noise ratio imaging with a single snapshot. Simulations and real data tests demonstrate that our method not only improves resolution but also offers flexible array configurations and robust performance in practical applications.

Staged suppression of main-lobe compound jamming using airborne distributed arrays

For the compound jamming scenario where noise suppression jamming and interrupted sampling repeater jamming simultaneously exist, we propose a method of staged suppression of them using distributed arrays. The basic principle is using the generalized inner product for the selection of non-uniform samples. By using the generalized inner product quadratically for the staged selection of the samples, we can obtain the statistical feature estimates of the noise suppression jamming and the interrupted sampling repeater jamming, respectively, and then complete the robust suppression of the compounded jamming using the distributed array spatial domain adaptive processing. With typical configuration of parameters, we validate and analyze the effectiveness and performance of the proposed method through numerical simulation experiments.

Spatial distribution of poultry farms using point pattern modelling: A method to address livestock environmental impacts and disease transmission risks.

The distribution of farm locations and sizes is paramount to characterize patterns of disease spread. With some regions undergoing rapid intensification of livestock production, resulting in increased clustering of farms in peri-urban areas, measuring changes in the spatial distribution of farms is crucial to design effective interventions. However, those data are not available in many countries, their generation being resource-intensive. Here, we develop a farm distribution model (FDM), which allows the prediction of locations and sizes of poultry farms in countries with scarce data. The model combines (i) a Log-Gaussian Cox process model to simulate the farm distribution as a spatial Poisson point process, and (ii) a random forest model to simulate farm sizes (i.e. the number of animals per farm). Spatial predictors were used to calibrate the FDM on intensive broiler and layer farm distributions in Bangladesh, Gujarat (Indian state) and Thailand. The FDM yielded realistic farm distributions in terms of spatial clustering, farm locations and sizes, while providing insights on the factors influencing these distributions. Finally, we illustrate the relevance of modelling realistic farm distributions in the context of epidemic spread by simulating pathogen transmission on an array of spatial distributions of farms. We found that farm distributions generated from the FDM yielded spreading patterns consistent with simulations using observed data, while random point patterns underestimated the probability of large outbreaks. Indeed, spatial clustering increases vulnerability to epidemics, highlighting the need to account for it in epidemiological modelling studies. As the FDM maintains a realistic distribution of farm location and sizes, its use to inform mathematical models of disease transmission is particularly relevant for regions where these data are not available.

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.

An X/Ku‐band shared aperture antenna with open, short, and load‐ended series‐feed center‐fed chebyshev amplitude distribution array for AIR‐BORNE‐synthetic aperture radar

AbstractThis article presents a dual‐band shared aperture antenna array designed for ASAR applications. The antenna array comprises eight elements for Chebyshev array configurations in both X/Ku‐bands. The proposed array achieves a scanning angle of ±25° by employing an interelement spacing of 0.7 λ. The layout mitigates mutual coupling effects and provides good isolation performance between the X‐ and Ku‐band antenna arrays across all synthesis techniques. Adopting the open, short, and load antenna array achieves different side lobe level (SLL) performances, >−25 dB. The isolation between bands and between the three conditions is quantified as follows: S16 = S61 = −68 dB, S15 = S51 = −55 dB, S12 = S21 = −46 dB, S43 = S34 = −49 dB, and S56 = S65 = −43 dB. The SLL values for each synthesis technique in the X/Ku‐band are as follows: Chebyshev simulated and measured results for open‐ended (X/Ku‐band: −26/−24 dB/−27/−26.8 dB). Furthermore, the antenna array achieves maximum realized gains in different positions (0°, 5°, 10°, 15°, 20°, 25°) in the X/Ku‐band. The gains for each synthesis technique are as follows: Chebyshev (The measured gain of this X/Ku‐band is high around 16.2/16.5 dBi (open‐ended), 13.1/14.2 dBi (short‐ended), and 15.3/15.2 dBi (load‐ended). The antenna array exhibits a comparatively wide impedance bandwidth Chebyshev (X/Ku‐band: 100/230 MHz).

Direction-of-arrival estimation in closely distributed array exploiting mixed-precision covariance matrices

In this paper, we explore a collaborative direction-of-arrival (DOA) estimation technique that utilizes multiple closely spaced subarrays to maximize the potential of distributed arrays while minimizing communication overhead between the subarrays and the processing center. Each subarray computes its self-covariance matrix using the full-precision data and transmits it, along with a one-bit version of the measured data, to the processing center. The processing center generates one-bit cross-covariance matrices between subarrays, which are combined with full-precision subarray self-covariance matrices to create the mixed-precision covariance matrix of the entire array for source DOA estimation. This approach utilizes the full array aperture and all available degrees of freedom of the entire distributed array. To address missing entries in the full covariance matrix, we employ matrix completion, taking into account its Toeplitz and Hermitian structure. For subarrays that are not positioned on the half-wavelength grid, we propose an iterative DOA estimation method to ensure robust DOA estimation performance. Our proposed approach outperforms scenarios where cross-covariance matrices are unavailable or the entire covariance matrix is not interpolated. With the same communication traffic limitation, it demonstrates superiority over schemes that utilize only full-precision data or only one-bit data.

Airborne distributed array position calibration using sparse spatial-frequency accompanying flight MISO array

Airborne coherent distributed array radar (DCAR) can greatly improve radar detection range and angle resolution. However, the time-variant property of airborne DCAR elements’ position strongly limits the application in real situations. In this paper, we developed a high-accuracy position estimation method for airborne DCAR systems with position errors using a single accompanying flight (AF) array. The basic principle is using a small array with a spatial sparse configuration to transmit a sparse-frequency multiple input single output (MISO) signal. Then airborne DCAR elements receive the signals and perform multi-channel demodulation and parameter estimation processing to obtain their distance and angle information relative to the AF array. The presented method estimates the position of all DACR elements with the single same array and thus various inaccordance between different transmit sources can be avoided. By using a spatial-frequency sparse array, the system complexity of the accompanying flight platform can be greatly reduced. Our method can obtain position estimates that are smaller than the operating wavelength, which ensures that sufficient performance can be obtained from coherent processing. Simulation results are given to demonstrate the effectiveness of the proposed method.

Reverberation-Robust Self-Calibration and Synchronization of Distributed Microphone Arrays by Mitigating Heteroscedasticity and Outlier Occurrence in TDoA Measurements.

The network of distributed microphone arrays is usually established in an ad hoc manner; hence, network parameters such as the mutual positioning and rotation of the arrays, positions of sources, and synchronization of their recording onset times are initially unknown. In this article, we consider the problem of passively jointly self-calibrating and synchronizing distributed arrays in reverberant rooms. We use a typical two-step approach where, initially, the relative geometry of the network is estimated using Direction of Arrival (DoA) measurements. Subsequently, the absolute scale and synchronization parameters are estimated using Time Difference of Arrival (TDoA) measurements. This article presents methods to improve the robustness and accuracy of estimation of the absolute geometric scaling and synchronization parameters in reverberant conditions, in which TDoA measurements do not follow a normal distribution; furthermore, outliers often occur. To remedy these issues, we propose a Weighted Least Squares (WLS) estimator and schema for weighting the TDoA measurements to increase the estimation accuracy from heteroscedastic TDoA measurements. In addition, we propose an iterative reweighing algorithm with a binary weight to detect and reject TDoA outliers, which exploits the residuals of the parametric model in the least absolute value minimization. A numerical evaluation shows significant improvements in the proposed method over the state of the art in terms of the relative scaling error and mean absolute value of the synchronization parameters.

Sodium carboxymethyl cellulose and MXene reinforced multifunctional conductive hydrogels for multimodal sensors and flexible supercapacitors

With the growing demand for eco-friendly materials in wearable smart electronic devices, renewable, biocompatible, and low-cost hydrogels based on natural polymers have attracted much attention. Cellulose, as one of the renewable and degradable natural polymers, shows great potential in wearable smart electronic devices. Multifunctional conductive cellulose-based hydrogels are designed for flexible electronic devices by adding sodium carboxymethyl cellulose and MXene into polyacrylic acid networks. The multifunctional hydrogels possess excellent mechanical property (stress: 310 kPa; strain: 1127 %), toughness (206.67 KJ m−3), conductivity (1.09 ± 0.12 S m−1) and adhesion (82.19 ± 3.65 kPa). The multifunctional conductive hydrogels serve as strain sensors (Gauge Factor (GF) = 5.79, 0–700 % strain; GF = 14.0, 700–900 % strain; GF = 40.36, 900–1000 % strain; response time: 300 ms; recovery time: 200 ms) and temperature sensors (Temperature coefficient of resistance (TCR) = 2.5755 °C−1 at 35 °C- 60 °C). The sensor detects human activities with clear and steady signals. A distributed array of flexible sensors is created to measure the magnitude and distribution of pressure and a hydrogel-based flexible touch keyboard is also fabricated to recognize writing trajectories, pressures and speeds. Furthermore, a flexible hydrogel-based supercapacitor powers the LED and exhibits good cyclic stability over 15,000 charge-discharge cycles.

Robust Tracking of Multiple Targets Subspace Based on Distributed Array Networks

Robust subspace tracking is a real-time solution for suppressing harsh interference and estimating the target subspace in complex electromagnetic environments. In this paper, a robust and decentralized subspace tracking method is proposed. The multi-sensor array is divided into a number of nodes, each attached with a processor and no central processing unit (CPU) is deployed. In order to reduce the impact of burst pulse, a robust real-time update algorithm of the subspace is designed through the weighted least squares method. The optimization model established in this paper is robust to synchronization error. Then a subspace real-time update algorithm is proposed based on the average consensus (AC) method that integrates the information of distributed arrays. The computational complexity and convergence properties of the proposed algorithm are also investigated. Simulation results show that the proposed approach converges to the subspace estimation error by an order of magnitude compared with the single-node algorithm.

Gaussian Mixture Model for Marine Reverberations

Ocean reverberations, a significant interference source in active sonar, arise as a response generated by random scattering at the receiving end, a consequence of randomly distributed clutter or irregular interfaces. Statistical analysis of reverberation data has revealed a predominant adherence to the Rayleigh distribution, signifying its departure from specific distribution forms like the Gaussian distribution. This study introduces the Gaussian mixture model, capable of simulating random variables conforming to a wide array of distributions through the integration of an adequate number of components. Leveraging the unique statistical attributes of reverberation, we initiate the Gaussian mixture model’s parameters via the frequency histogram of the reverberation data. Subsequently, model parameters are estimated using the expectation–maximization (EM) algorithm and the most suitable statistical model is selected based on robust model selection criteria. Through a comprehensive evaluation that encompasses both simulated and observed data, our results underscore the Gaussian mixture model’s effectiveness in accurately characterizing the distribution of reverberation data, yielding a mean squared error of less than 4‰.

Bioinspired Artificial Hair Sensors for Flight-by-Feel of Unmanned Aerial Vehicles: A Review

Flight-by-feel is an emerging approach to flight control that uses distributed arrays of pressure, strain, and flow sensors to guide aircraft. Among these, hair-type flow sensors have received the least attention yet hold some advantages over conventional sensors. This paper reviews hair-like flow microsensors developed since 2013, focusing on developments in design, construction, and application. Hair-like flow sensors can be found in artificial cochleae, submersible navigation, terrestrial robots, and, rarely but increasingly, on aircraft. In this survey, we categorize hair-like flow sensors into three types (long whisker-like hairs, ultrasensitive microscale hairs, and short trichoid-like hairs), and primarily cover sensors that may be suitable for use on aircraft. The recent progress in flow-based flight control using distributed sensing is also discussed, along with the optimization of sensor placement and the potential for flight-by-feel in sixth-generation military and civilian aircraft designs. This survey aims to provide a consolidated account of the history and state-of-the-art of artificial hair-cell flow sensors, motivate consideration of flight-by-feel as a viable flight control paradigm, and define avenues for future research. As engineering and biological science continue to converge, we hope that researchers in both fields find this survey an inspirational and useful resource.

A distributed subarray position optimization method based on DE-MIFT algorithm

Distributed array radar is a next generation radar that has been a hot research direction due to its high maneuverability, low cost, and high reliability. However, the synthesized beam pattern of the distributed array usually has some high sidelobes and grating lobes, which reduce the system performance. In this paper, a hybrid algorithm based on the differential evolution algorithm and modified iterative Fourier transform algorithm called DE-MIFT is proposed for optimizing subarray positions in a distributed array model. This distributed array model is based on a uniform linear subarray with equal amplitude excitation. Moreover, an integer coding method based on the minimum spacing between subarrays is adopted in the proposed algorithm to encode and initialize population, then the two algorithms are used to optimize in parallel. Then a variable selection factor is used to select dominant individuals from the parallel optimization results, and the subarray position design of distributed array is obtained through iterations. It is shown by simulation results that the proposed algorithm can effectively suppress system grating lobes, reduce sidelobe levels, and has powerful global search capability and fast convergence speed.

On coherently processing multiple arrays in underwater environments with spatial coherence losses from random medium effects and sensor position uncertainties

Under ideal circumstances coherently beamforming multiple distributed arrays should provide improvements over the standard approach of processing the arrays individually and combining them incoherently. These include improved directivity and higher gain. However, two major challenges when coherently processing spatially separated arrays in a real ocean are dealing with coherence losses or wave front distortions caused by random medium effects like internal waves and uncertainties in sensor locations. Some of the consequences are degraded beampatterns and poorer array directivity. In this paper, we perform a simulation-based study utilizing numerical propagation models along with theory examining the effects of wave front distortions induced by random medium effects like internal waves and sensor position uncertainties on coherently beamforming distributed arrays in representative ocean waveguides. Realistic realizations of wave front distortions are generated from actual internal wave measurements and models. The sensitivities of coherent processing to these effects are characterized by resultant beampatterns and losses in array directivity along with a quantification of the tolerances.

Joint Generalized Inner Product Method for Main Lobe Jamming Suppression in Distributed Array Radar

Modern radar detection faces an increasingly complex electromagnetic environment, and strong clutter and strong jamming have severely restricted radar performance. This study proposes a robust jamming suppression method that is based on the joint generalized inner product (JGIP) method in a distributed array radar, which utilizes the characteristics of the extremely high angular resolution of the distributed array radar to effectively suppress main lobe jamming. Thus, the advantages of flexible aperture synthesis of distributed array radar are investigated to design a JGIP to detect targets in a cluttered environment. This method is not sensitive to the various nonideal factors that exist in distributed array radar engineering practice and is highly robust. Simulation and actual measured data verify the correctness and effectiveness of the proposed method.

Highly cascaded first-order sapphire optical fiber Bragg gratings fabricated by a femtosecond laser.

This Letter reports an innovative technique for fabricating large-scale, highly cascaded first-order sapphire optical fiber Bragg gratings (FBGs) using a femtosecond laser-assisted point-by-point inscription method. For the first time, to the best of our knowledge, this study successfully demonstrates a distributed array of 10 FBGs within highly multimode sapphire crystal fiber, made possible by employing a high-power laser technique to generate larger reflectors with a Gaussian intensity profile. These first-order FBGs offer advantages such as enhanced reflectivity, shorter fabrication time, and simplified spectral characteristics, making them easier to interpret compared with high-order FBGs. The FBGs' resilience and effectiveness are analyzed by subjecting them to temperature tests, proving their capacity for accurate temperature monitoring up to 1500°C-a testament to their suitability for harsh environments. This novel approach broadens the scope for sensing and communication applications in sapphire fibers, particularly under challenging conditions. The novelty of our work lies in successfully overcoming the limitations of previous designs by integrating a cascade of 10 FBGs in sapphire fibers, thereby enhancing multiplexing capabilities, minimizing overlapping of FBG peaks, and ensuring reliable temperature monitoring in industries and applications with thermal gradients.

DRFM Repeater Jamming Suppression Method Based on Joint Range-Angle Sparse Recovery and Beamforming for Distributed Array Radar

Distributed array radar achieves high angular resolution and measurement accuracy, which could provide a solution to suppress digital radio frequency memory (DRFM) repeater jamming. However, owing to the large aperture of a distributed radar, the far-field plane wave assumption is no longer satisfied. Consequently, traditional adaptive beamforming methods cannot work effectively due to mismatched steering vectors. To address this issue, a DRFM repeater jamming suppression method based on joint range-angle sparse recovery and beamforming for distributed array radar is proposed in this paper. First, the steering vectors of the distributed array are reconstructed according to the spherical wave model under near-field conditions. Then, a joint range-angle sparse dictionary is generated using reconstructed steering vectors, and the range-angle position of jamming is estimated using the weighted L1-norm singular value decomposition (W-L1-SVD) algorithm. Finally, beamforming with joint range-angle nulling is implemented based on the linear constrained minimum variance (LCMV) algorithm for jamming suppression. The performance and effectiveness of proposed method is validated by simulations and experiments on an actual ground-based distributed array radar system.

Phase Calibration of Distributed Antenna Arrays

Antenna arrays can be either reciprocity calibrated (R-calibrated), which facilitates reciprocity-based beamforming, or fully calibrated (F-calibrated), which additionally facilitates transmission and reception in specific physical directions. We first expose, to provide context, the fundamental principles of over-the-air R- and F-calibration of distributed arrays. We then describe a new method for calibration of two arrays that are individually F-calibrated, such that the combined array becomes jointly F-calibrated.