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Journals / Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik / 2018 / Cilt: 19 - Sayı: 2

COPRIME ARRAYS WITH ENHANCED DEGREES OF FREEDOM

Ahmet Musab ELBiR
Pages
235–241
Publication Date
2022-10-11
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The theoretical analysis of the proposedmethod is provided and its superior performance is evaluated through numerical simulations.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Coprime array geometries provide robust performance for direction-of-arrival estimation problem with more sources thansensor elements.
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Key Findings & Data:In previous works it is shown that K source directions can be resolved using only 2M + N -1 sensorelements where K is less than or equal to MN for M and N are integer numbers.
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Conclusion & Impact:The theoretical analysis of the proposedmethod is provided and its superior performance is evaluated through numerical simulations.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Coprime array geometries provide robust performance for direction-of-arrival estimation problem with more sources thansensor elements. In previous works it is shown that K source directions can be resolved using only 2M + N -1 sensorelements where K is less than or equal to MN for M and N are integer numbers. In this paper we introduce a new approach toenhance the degrees of freedom (DOF) from MN to 2MN by using the same number of sensor elements. The proposedmethod is based on computing the covariance matrix of the observation data multiple times. Hence more DOF can beobtained. The resulting cross terms corresponding to the coherent sources are modeled as interference in a sparse recoveryalgorithm which is solved effectively by an alternating minimization procedure. The theoretical analysis of the proposedmethod is provided and its superior performance is evaluated through numerical simulations.

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Cite This Article

ELBiR, A. M. (2022). COPRIME ARRAYS WITH ENHANCED DEGREES OF FREEDOM. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik, Cilt: 19 - Sayı: 2, 235–241. https://ajindex.com/articles/438235

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References (12)

  1. Tuncer TE, Friedlander B. Classical and Modern Direction-of-Arrival Estimation. Academic Press, 2009
  2. Krim H, Viberg M. Two decades of array signal processing research: the parametric approach. IEEE Signal Processing Magazine, vol. 13, no. 4, pp. 67-94, Jul 1996
  3. Schmidt RO. Multiple emitter location and signal parameter estimation. Antennas and Propagation, IEEE Transactions on, 34(3):276–280, Mar 1986
  4. Pillai SU, Bar-Ness Y, Haber F. A new approach to array geometry for improved spatial spectrum estimation. Proceedings of the IEEE, 73(10):1522–1524, Oct 1985
  5. Abramovich YI. Gray DA, Gorokhov AY, Spencer NK, Positive-definite toeplitz completion in doa estimation for nonuniform linear antenna arrays. i. fully augmentable arrays. IEEE Transactions on Signal Processing, 46(9):2458–2471, Sep 1998
  6. Abramovich YI. Spencer NK, Gorokhov AY, Positive-definite toeplitz completion in doa estimation for nonuniform linear antenna arrays. ii. partially augmentable arrays. IEEE Transactions on Signal Processing, 47(6):1502–1521, Jun 1999
  7. Pal P, Vaidyanathan PP. Nested arrays: A novel approach to array processing with enhanced degrees of freedom. IEEE Transactions on Signal Processing, 58(8):4167–4181, Aug 2010
  8. Pal P, Vaidyanathan PP. Coprime sampling and the music algorithm. In 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE), pages 289–294, Jan 2011
  9. Friedlander B, Weiss AJ. Direction finding using spatial smoothing with interpolated arrays IEEE Transactions on Aerospace and Electronic Systems, vol. 28, no. 2, pp. 574-587, Apr 1992
  10. Khatri CG, Radhakrishna Rao C. Solutions to some functional equations and their applications to characterization of probability distributions. Sankh: The Indian Journal of Statistics, Series A (1961-2002), 30(2):167–180, 1968
  11. Yang Z, Zhang C, Xie L. Robustly stable signal recovery in compressed sensing with structured matrix perturbation. IEEE Transactions on Signal Processing, 60(9):4658–4671, Sept 2012
  12. Eldar YC, Sampling Theory: Beyond Bandlimited Systems. Cambridge University Press, New York, NY, USA, 2014.