DOA Estimation Using Compressed Sparse Array

Guo, Muran; Zhang, Yimin D.; Chen, Tao

doi:10.1109/tsp.2018.2847645

Cited by 123 publications

(64 citation statements)

References 28 publications

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“…As demonstrated in [17], the essence of a compressed array is to reduce the dimensionality of the received signal. This can be achieved by multiplying the received signal with a complex compression matrix Ψ ∈ C M ×L , and elements of the complex compression matrix are generated by independent and identically distributed random entries.…”

Section: Principle Of the Cula-based Systemmentioning

confidence: 99%

“…The compression matrix method was proposed to reduce the numbers of branches behind the antennas in [17]. This method uses a complex compression matrix Ψ ∈ C M ×L at the antenna output, where L is the number of antennas, and M is the number of branches behind the antennas with M < L. The dimension of received signal is reduced from L to M by multiplying the received signal with the compression matrix.…”

Section: Introductionmentioning

confidence: 99%

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A Sub-Nyquist Sampling Digital Receiver System Based on Array Compression

Chen¹,

Han²,

Yu³

2020

PIER Letters

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In order to obtain the carrier frequency (CF) and direction-of-arrival (DOA) estimation, a uniform linear array (ULA)-based modulated wideband converter (MWC) discrete compressed sampling (CS) digital receiver system is proposed. It can achieve sub-Nyquist sampling, save the storage space, and obtain the CF and DOA estimation by processing the CS data directly. However, the existing method for this system needs more branches to get better performance. In this paper, a compressed ULA (CULA)-based MWC discrete CS digital receiver system is proposed. First, a compression matrix is used to reduce the number of branches behind the antennas. Then, the MWC discrete CS structure is used to reduce the data volume. Finally, the multiple signal classification (MUSIC) algorithm is used to jointly estimate the CF and DOA by processing the CS data directly. The simulation results validate the effectiveness of the proposed system and the proposed method for the joint CF and DOA estimation.

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Section: Principle Of the Cula-based Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Sub-Nyquist Sampling Digital Receiver System Based on Array Compression

Chen¹,

Han²,

Yu³

2020

PIER Letters

Self Cite

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show abstract

“…The orientation of a PU with respect to SU's location is determined in [12], [13] based on the RSS where an SU is equipped with a reconfigurable antenna. The signal received at an antenna array is sparse in the spatial domain and compressive sensing can be employed for DoA estimation using less number of radio frequency (RF) chains [14]. In [15] the problem of DoA estimation using electronically steerable parasitic array radiator (ESPAR) antenna based on compressive sensing is exploited for a sensor equipped with a single RF chain.…”

Section: Introductionmentioning

confidence: 99%

Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas

Joneidi

Yazdani

Vosoughi

et al. 2019

2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)

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Utilization of directional antennas is a promising solution for efficient spectrum sensing and accurate source localization and tracking. Spectrum sensors equipped with directional antennas should constantly scan the space in order to track emitting sources and discover new activities in the area of interest. In this paper, we propose a new formulation that unifies received-signal-strength (RSS) and direction of arrival (DoA) in a compressive sensing (CS) framework. The underlying CS measurement matrix is a function of beamforming vectors of sensors and is referred to as the propagation matrix. Comparing to the omni-directional antenna case, our employed propagation matrix provides more incoherent projections, an essential factor in the compressive sensing theory. Based on the new formulation, we optimize the antenna beams, enhance spectrum sensing efficiency, track active primary users accurately and monitor spectrum activities in an area of interest. In many practical scenarios there is no fusion center to integrate received data from spectrum sensors. We propose the distributed version of our algorithm for such cases. Experimental results show a significant improvement in source localization accuracy, compared with the scenario when sensors are equipped with omni-directional antennas. Applicability of the proposed framework for dynamic radio cartography is shown. Moreover, comparing the estimated dynamic RF map over time with the ground truth demonstrates the effectiveness of our proposed method for accurate signal estimation and recovery.Index Terms-Spectrum sensing, source localization and tracking, directional antennas, radio cartography. 1 The propagation model can be extended for fading channels by assuming an uncertainty on the channel gains.

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“…In multiple-input and multiple-output (MIMO) radar systems, a CS-based DOA estimation method was proposed in [18,21]. Additionally, in [22], a compressed sparse array scheme was proposed.…”

Section: Introductionmentioning

confidence: 99%

Atomic Norm-Based DOA Estimation with Dual-Polarized Radar

Han

Dou

2019

Electronics

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In the dual-polarized radar system, the horizontally and vertically polarized signals can be exploited to improve the direction of arrival (DOA) estimation performance. In this paper, the DOA estimation problem is considered in the dual-polarized radar. By exploiting the target sparsity in the spatial domain, the sparse-based method is proposed after formulating the DOA estimation problem as a sparse reconstruction problem. In the traditionally sparse methods using the compressed sensing (CS) theory, the spatial domain is discretized into grids to establish a dictionary matrix and solve the sparse reconstruction problem, but the off-grid error is introduced in the discretized grids. Therefore, we formulate a novel definition of atomic norm for the dual-polarized signals and give an atomic norm-based method to denoise the received signals. Then, an efficient semidefinite program (SDP) is derived, and the DOA is estimated by searching the peak values of the denoised signals. Simulation results show that the proposed method can significantly improve the DOA estimation performance in the dual-polarized radar. Additionally, compared with the state-of-art methods, the proposed method has better estimation performance with relatively low computational complexity.

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DOA Estimation Using Compressed Sparse Array

Cited by 123 publications

References 28 publications

A Sub-Nyquist Sampling Digital Receiver System Based on Array Compression

A Sub-Nyquist Sampling Digital Receiver System Based on Array Compression

Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas

Atomic Norm-Based DOA Estimation with Dual-Polarized Radar

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