Distributed Compressed Sensing off the Grid

Lu, Zhenqi; Ying, Rendong; Jiang, Sumxin; Liu, Peilin; Wang, Yu

doi:10.1109/lsp.2014.2349904

Cited by 18 publications

(11 citation statements)

References 43 publications

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“…For r j = z c + z j ∈ ℂ N , j ∈ Λ,

which is proved in [ 35 ]. Therefore, Equation (36) can be expressed as the following computationally tractable SDP

…”

Section: Sdp Based Joint Doa Estimation Algorithmmentioning

confidence: 97%

“…In the prior work, we have considered a joint frequency sparsity (JFS) model and extended the atomic norm to JFS setting [ 35 ]. To develop a norm description of the JSS, we also extend the atomic norm to JSS setting and give the definition of CA-norm of JSS.…”

Section: Sdp Based Joint Doa Estimation Algorithmmentioning

confidence: 99%

“…But the observed signals of common sources in different arrays have redundancy which brings about repeated calculations in the DOA estimations. In our prior work, a joint frequency sparsity (JFS) model was built up and the distributed compressed sensing (DCS) off the grid was studied [ 35 , 36 ]. We introduce this idea of off-grid DCS into solving the problem of joint DOA estimation with distributed SLAs under the coexistence of common/innovation sources.…”

Section: Introductionmentioning

confidence: 99%

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Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays

Liang

Ying

et al. 2014

Sensors

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In the design phase of sensor arrays during array signal processing, the estimation performance and system cost are largely determined by array aperture size. In this article, we address the problem of joint direction-of-arrival (DOA) estimation with distributed sparse linear arrays (SLAs) and propose an off-grid synchronous approach based on distributed compressed sensing to obtain larger array aperture. We focus on the complex source distribution in the practical applications and classify the sources into common and innovation parts according to whether a signal of source can impinge on all the SLAs or a specific one. For each SLA, we construct a corresponding virtual uniform linear array (ULA) to create the relationship of random linear map between the signals respectively observed by these two arrays. The signal ensembles including the common/innovation sources for different SLAs are abstracted as a joint spatial sparsity model. And we use the minimization of concatenated atomic norm via semidefinite programming to solve the problem of joint DOA estimation. Joint calculation of the signals observed by all the SLAs exploits their redundancy caused by the common sources and decreases the requirement of array size. The numerical results illustrate the advantages of the proposed approach.

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“…For r j = z c + z j ∈ ℂ N , j ∈ Λ,

which is proved in [ 35 ]. Therefore, Equation (36) can be expressed as the following computationally tractable SDP

…”

Section: Sdp Based Joint Doa Estimation Algorithmmentioning

confidence: 97%

Section: Sdp Based Joint Doa Estimation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays

Liang

Ying

et al. 2014

Sensors

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“…Therefore, grid mismatch error is unavoidable [36]. The gridfree algorithms were developed to reduce the grid mismatch error in CS-based spectrum sensing [14,37,38] and CS-based DoA estimation [14,39,40]. Although grid-free algorithms obtain more accurate estimates, they impose high computational load, which results in serious challenges in space borne implementation.…”

Section: Accurate Location Estimation Of Co-channel Radarsmentioning

confidence: 99%

Space-Borne Compressed Sensing Based Receiver for Accurate Localization of Ground-Based Radars

Ramezani¹,

Sabahi²,

Saberali³

2020

PIER C

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Space borne accurate emitter localization has become an important and indispensable part of electronic warfare (EW) systems. In this paper, a system-level approach to design a space borne receiver for accurate localization of long range co-channel radars (e.g., a network of similar surveillance radars) is presented. Due to the wide frequency range of modern radar signals, the receiver should have a wide instantaneous bandwidth and requires high sampling rate analog-to-digital converters (ADCs). To address this issue, we propose a receiver structure with an appropriate sub-Nyquist sampling scheme and fast sparse recovery algorithm. The proposed sub-Nyquist sampler employs a three dimensional uniform linear array (ULA), followed by a modulated wideband converter (MWC). To accurately estimate the location of the co-channel radars from sub-Nyquist samples, a novel quad-tree variational Bayesian expectation maximization (QVBEM) algorithm is proposed. The QVBEM algorithm minimizes the computational load and grid mismatch error by iteratively narrowing the search area. This is done by a smart grid refinement around radars' locations. To evaluate the performance of the proposed receiver, location finding of pulsed radars is studied through numerical simulations in various scenarios. The results show that the proposed QVBEM method has a significantly lower estimation error than conventional deterministic and Bayesian approaches, with a reasonably computational complexity.

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“…where the row-sparse matrix X is defined similarly as for the FCA case in (13). Furthermore, the M × P K overcomplete subarray dictionary matrix B(ν) and the P K × K overcomplete subarray shift matrix Φ(ν, η) are defined in correspondence to (8) and (9), respectively.…”

Section: Partly Calibrated Arraymentioning

confidence: 99%

Block- and Rank-Sparse Recovery for Direction Finding in Partly Calibrated Arrays

Sandor

Pesavento

2018

IEEE Trans. Signal Process.

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A sparse recovery approach for direction finding in partly calibrated arrays composed of subarrays with unknown displacements is introduced. The proposed method is based on mixed nuclear norm and ℓ 1 norm minimization and exploits block-sparsity and low-rank structure in the signal model. For efficient implementation a compact equivalent problem reformulation is presented. The new technique is applicable to subarrays of arbitrary topologies and grid-based sampling of the subarray manifolds. In the special case of subarrays with a common baseline our new technique admits extension to a gridless implementation. As shown by simulations, our new block-and rank-sparse direction finding technique for partly calibrated arrays outperforms the state of the art method RARE in difficult scenarios of low sample numbers, low signal-to-noise ratio or correlated signals.

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Distributed Compressed Sensing off the Grid

Cited by 18 publications

References 43 publications

Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays

Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays

Space-Borne Compressed Sensing Based Receiver for Accurate Localization of Ground-Based Radars

Block- and Rank-Sparse Recovery for Direction Finding in Partly Calibrated Arrays

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