Sparse arrays are known to achieve an increased number of degrees-of-freedom (DOFs) for direction-of-arrival (DOA) estimation, where an augmented virtual uniform array calculated from the correlations of sub-Nyquist spatial samples is processed to retrieve the angles unambiguously. Nevertheless, the geometry of the derived virtual array is dominated by the specific physical array configurations, as well as the deviation caused by the practical unforeseen circumstances such as detection malfunction and missing data, resulting in a quite sensitive model for virtual array signal processing. In this paper, we propose a novel sparse array DOA estimation algorithm via structured correlation reconstruction, where the Nyquist spatial filling is implemented on the physical array with a compressed transformation related to its equivalent filled array to guarantee the general applicability. While the unknown correlations located in the whole rows and columns of the augmented covariance matrix lead to the fact that strong incoherence property is no longer satisfied for matrix completion, the structural information is introduced as a priori to formulate the structured correlation reconstruction problem for matrix reconstruction. As such, the reconstructed covariance matrix can be effectively processed with full utilization of the achievable DOFs from the virtual array, but with a more flexible constraint on the array configuration. The described estimation problem is theoretically analyzed by deriving the corresponding Cramér-Rao bound (CRB). Moreover, we compare the derived CRB with the performance of the virtual array interpolation-based algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of DOFs, resolution, and estimation accuracy.

Structured Nyquist Correlation Reconstruction for DOA Estimation With Sparse Arrays

Sparse array direction-of-arrival (DOA) estimation using tensor model has been developed to handle multi-dimensional sub-Nyquist sampled signals. Furthermore, augmented virtual arrays can be derived for Nyquist-matched coarray tensor processing. However, the partially augmentable sparse array corresponds to a discontinuous virtual array, whereas the existing methods can only utilize its continuous part. Conventional virtual linear array interpolation techniques complete coarray covariance matrices with dispersed missing elements, but fail to complete the coarray tensor with whole missing slices. In this paper, we propose a coarray tensor completion algorithm for two-dimensional DOA estimation, where the coarray tensor statistics can be entirely exploited. In particular, in order to impose an effective low-rank regularization on the slice-missing coarray tensor, we propose shift dimensional augmenting and coarray tensor reshaping approaches to reformulate a structured coarray tensor with sufficiently dispersed missing elements. Furthermore, the shape of the reformulated coarray tensor is optimized by maximizing the dispersion-to-percentage ratio of missing elements. As such, a coarray tensor nuclear norm minimization problem can be designed to optimize the completed coarray tensor corresponding to a filled virtual array, based on which the closed-form DOA estimation is achieved. Meanwhile, the global convergence of the coarray tensor completion is theoretically proved. Simulation results demonstrate the effectiveness of the proposed algorithm compared to other matrix-based and tensor-based methods. 

Coarray Tensor Completion for DOA Estimation

Recently, coprime arrays have attracted lots of interest due to their ability of providing enhanced degrees-of-freedom and reduced mutual coupling effect compared to conventional uniform linear arrays. Benefitting from these excellent properties, coprime multiple-input multiple-output (MIMO) radar has been recently suggested for improving parameter identifiability and target detection. In this paper, we address the problem of joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation of coherent targets in coprime MIMO radar. First of all, we establish an extended virtual uniform rectangular array (URA) by performing array interpolation on the transmit and receive arrays of coprime MIMO radar. Subsequently, we derive a low-rank block Hankel matrix that is constructed using the correlation information of the virtual URA expected outputs, and utilize the matched filter outputs to recover the block Hankel matrix by solving a low-rank structured matrix completion problem. Finally, we estimate the DODs and DOAs of coherent targets by applying the modified matrix pencil method on the recovered low-rank matrix. We also derive the Cramér-Rao bounds with closed-form expressions for DOD and DOA estimation of coherent targets using coprime MIMO radar. The proposed algorithm can identify multiple coherent targets and achieve parameter automatic pairing. Numerical results demonstrate the superiority of the proposed algorithm over several existing approaches when handling coherent targets.

Joint DOD and DOA Estimation of Coherent Targets for Coprime MIMO Radar

Target localization based on frequency diverse array (FDA) radar has lately garnered significant research interest. A linear frequency offset (FO) across FDA antennas yields a range–angle-dependent beampattern that allows for the joint estimation of range and direction of arrival. Prior works on the FDA largely focus on the one-dimensional linear array to estimate only azimuth angle and range while ignoring the elevation and Doppler velocity. However, in many applications, the latter two parameters are also essential for target localization. Furthermore, there is also an interest in radar systems that employ fewer measurements in temporal, Doppler, or spatial signal domains. We address these multiple challenges by proposing a coprime L-shaped FDA, wherein coprime FOs are applied across the elements of L-shaped coprime array, and each element transmits at a nonuniform coprime pulse repetition interval (C-Cube). This co-pulsing FDA yields a significantly large number of degrees of freedom (DoFs) for target localization in the range–azimuth–elevation–Doppler domain while also reducing the time on target and the transmit spectral usage. By exploiting these DoFs, we develop a C-Cube autopairing (CCing) algorithm, in which all the parameters are ipso facto paired during a joint estimation. We show that the C-Cube FDA requires at least $2\sqrt{Q+1}-1$ antenna elements and $2\sqrt{Q+1}-1$ pulses to guarantee the perfect recovery of $Q$ targets as against $Q+1$ elements and $Q+1$ pulses required by both the L-shaped uniform linear array and the L-shaped linear FO FDA with uniform pulsing. We derive Cramér–Rao bounds (CRBs) for joint angle–range–Doppler estimation errors for the C-Cube FDA and provide the conditions under which the CRBs exist. Numerical experiments with our CCing algorithm show great performance improvements in parameter recovery, wherein the C-Cube radar achieves at least 15% higher target hit rate with shorter dwell time than its uniform counterparts.

Co-Pulsing FDA Radar

New reweighted atomic norm minimization approach for line spectral estimation

In this paper, we address the problem of joint direction-of-arrival (DoA) and range estimation using frequency diverse coprime array (FDCA). By incorporating the coprime array structure and coprime frequency offsets, a two-dimensional space-frequency virtual difference coarray corresponding to uniform array and uniform frequency offset is considered to increase the number of degrees-of-freedom (DoFs). However, the reconstruction of the doubly-Toeplitz covariance matrix is computationally prohibitive. To solve this problem, we propose an interpolation algorithm based on decoupled atomic norm minimization (DANM), which converts the coarray signal to a simple matrix form. On this basis, a relaxation-based optimization problem is formulated to achieve joint DoA-range estimation with enhanced DoFs. The reconstructed coarray signal enables application of existing subspace-based spectral estimation methods. The proposed DANM problem is further reformulated as an equivalent rank-minimization problem which is solved by cyclic rank minimization. This approach avoids the approximation errors introduced in nuclear norm-based approach, thereby achieving superior root-mean-square error which is closer to the Cramér-Rao bound. The effectiveness of the proposed method is confirmed by theoretical analyses and numerical simulations.

Joint DoA-Range Estimation Using Space-Frequency Virtual Difference Coarray

Frequency diverse array (FDA) produces a beampattern with controllable direction and range by slightly shifting the carrier frequencies across the elements, which is attractive in many applications. By further incorporating coprime array structure and coprime frequency offsets, improved degrees-of-freedom and spatial/range resolutions have been achieved. For such a relatively new array configuration, theoretical performance analyses are essential to explore the potentials and to facilitate practical implementation. In this work, we consider coprime-FDA-based joint/separate angle-range estimation of far-field targets that exhibit two different types of Swerling fluctuation behavior, which are respectively modelled as deterministic and stochastic sources. Analytical expressions of the Cramér–Rao bounds (CRB) and numerical simulations for both cases are provided. The results reveal that the relationship between CRB and coprime FDA parameters is not simply monotonic. As shown in the numerical simulations, the CRB of coprime FDA outperforms that of uniform FDA-MIMO for more than 60% under commonly-adopted coprime patterns. The presented results can be used as a guideline for optimal design of coprime FDA.

/pdf/cramer-rao-bound-of-joint-doa-range-estimation-for-coprime-3irt2cn5.pdf

Cramér-Rao Bound of Joint DOA-Range Estimation for Coprime Frequency Diverse Arrays

In this paper, we address the problem of direction finding using coprime array, which is one of the most preferred sparse array configurations in the radar community. Motivated by the fact that conventional algorithms based on the convex relaxation is computationally expensive, we propose a non-convex accelerated structured alternating projection-based direction-of-arrival (DoA) estimation approach without the need to solve a semi-definite programming. The low rank Hankel matrices constructed by the sub-arrays of the virtual uniform linear array (ULA) and the sparse hole signal respectively correspond to the sets of low-rank matrices and sparse manifolds. After initialization, we update the estimations by iterative projections between the two sets, which employs an accelerated low-rank approximation method. Once the desired Hankel matrix with DoA information is recovered, simple subspace-based spectral estimation algorithms can be applied to obtain the target directions. The estimation performance in terms of root-mean-square error is examined and the superiority of the proposed method over the competitive approaches in the computational cost sense is also demonstrated.

DoA estimation based on accelerated structured alternating projection using coprime array

High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to achieve positioning functionality, which are susceptible to performance degradation in practical scenarios, primarily due to hardware impairments. Integrating artificial intelligence into the positioning framework presents a promising solution to revolutionize the accuracy and robustness of location-based services. In this study, we address this challenge by reformulating the problem of angle-of-arrival (AoA) estimation into image reconstruction of spatial spectrum. To this end, we design a model-driven deep neural network (MoD-DNN), which can automatically calibrate the angular-dependent phase error. The proposed MoD-DNN approach employs an iterative optimization scheme between a convolutional neural network and a sparse conjugate gradient algorithm. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed method in enhancing spectrum calibration and AoA estimation. 

pdf/model-driven-deep-neural-network-for-enhanced-aoa-estimation-3giurs0qap.pdf

Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB

The beam steering of a frequency diverse array (FDA) is range-angle dependent, which facilitates two-dimensional lo-calization in space but comes at the expense of increased data matrix size. As sufficiently low sampling rate and hard-ware complexity are important for practical implementation, we consider information theoretic kernel design for parameter estimation using compressed measurements. The compressed sensing kernel is optimized via gradient decent by exploiting the spatial distribution priors of the targets of interest. We showcase the advantage of the proposed scheme in terms of dramatically smaller measurement size and approximate recovery accuracy using extensive numerical simulations.

Information-Theoretic Target Localization With Compressed Measurement Using FDA Radar

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Z. Mao

Author Tools

Chat about Author