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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 劉俊麟 | zh_TW |
| dc.contributor.advisor | Chun-Lin Liu | en |
| dc.contributor.author | 陳羿竹 | zh_TW |
| dc.contributor.author | Yi-Chu Chen | en |
| dc.date.accessioned | 2023-08-15T17:38:06Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-15 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-07 | - |
| dc.identifier.citation | [1] S. Haykin, Array Signal Processing. Englewood Cliffs, NJ, USA: Prentice-Hall, 1984.
[2] H. L. V. Trees, Optimum Array Processing: Part IV of Detection, Estimation, and Modulation Theory. John Wiley and Sons, Ltd, 2002. [3] M. I. Skolnik, Introduction to Radar Systems, 3rd ed. New York, NY, USA: McGraw-Hill, 2001. [4] P. Pal and P. P. Vaidyanathan, “Nested arrays: A novel approach to array processing with enhanced degrees of freedom,” IEEE Transactions on Signal Processing, vol. 58, no. 8, pp. 4167 – 4181, Aug. 2010. [5] ——, “Coprime sampling and the MUSIC algorithm,” 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE), 2011. [6] C. L. Liu and P. P. Vaidyanathan, “Remarks on the spatial smoothing step in coarray MUSIC,” IEEE Signal Processing Letters, vol. 22, no. 9, pp. 1438 – 1442, Sep. 2015. [7] R. Schmidt, “Multiple emitter location and signal parameter estimation,” IEEE Transactions on Antennas and Propagation, vol. 34, no. 3, pp. 276 – 280, Mar. 1986. [8] R. Roy, A. Paulraj, and T. Kailath, “Estimation of signal parameters via rotational invariance techniques - ESPRIT,” MILCOM 1986 - IEEE Military Communications Conference: Communications-Computers: Teamed for the 90’s, Oct. 1986. [9] ——, “ESPRIT–A subspace rotation approach to estimation of parameters of cisoids in noise,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 34, no. 5, pp. 1340 – 1342, Oct. 1986. [10] S. Sekizawa, “Estimation of arrival directions using MUSIC algorithm with a planar array,” ICUPC ’98. IEEE 1998 International Conference on Universal Personal Communications. Conference Proceedings (Cat. No.98TH8384), Oct. 1998. [11] A. Swindlehurst and T. Kailath, “Azimuth/elevation direction finding using regular array geometries,” IEEE Transactions on Aerospace and Electronic Systems, vol. 29, no. 1, pp. 145 – 156, Jan. 1993. [12] M. Haardt, M. D. Zoltowski, C. P. Mathews, and J. Nossek, “2D unitary ESPRIT for efficient 2D parameter estimation,” 1995 International Conference on Acoustics, Speech, and Signal Processing, May 1995. [13] P. P. Vaidyanathan and P. Pal, “Sparse sensing with co-prime samplers and arrays,”IEEE Transactions on Signal Processing, vol. 59, no. 2, pp. 573 – 586, 2010. [14] A. Moffet, “Minimum-redundancy linear arrays,” IEEE Transactions on Antennas and Propagation, vol. 16, no. 2, pp. 172 – 175, 1968. [15] H. Taylor and S. W. Golomb, “Rulers part I,” Univ. Southern Calif., Los Angeles, Tech. Rep, 1985. [16] C. L. Liu and P. P. Vaidyanathan, “Hourglass arrays and other novel 2-D sparse arrays with reduced mutual coupling,” IEEE Transactions on Signal Processing, vol. 65, no. 13, pp. 3369 – 3383, July 2017. [17] P. O’Connor and A. Kleyner, Practical Reliability Engineering. John Wiley and Sons, 2012. [18] A. Myers, Complex System Reliability. Springer, 2010. [19] S. Vigneshwaran, N. Sundararajan, and P. Saratchandran, “Direction of arrival (DoA) estimation under array sensor failures using a minimal resource allocation neural network,” IEEE Transactions on Antennas and Propagation, vol. 55, no. 2, pp. 334 – 343, 2007. [20] C. Zhu, W. Q. Wang, H. Chen, and H. C. So, “Impaired sensor diagnosis, beamforming, and DOA estimation with difference coarray processing,” IEEE Sensors Journal, vol. 15, no. 7, July 2015. [21] C. L. Liu and P. P. Vaidyanathan, “Optimizing minimum redundancy arrays for robustness,” 2018 52nd Asilomar Conference on Signals, Systems, and Computers, 2018. [22] ——, “Robustness of coarrays of sparse array to sensor failures,” 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018. [23] ——, “Robustness of difference coarrays of sparse arrays to sensor failures ─ Part II: Array geometries,” IEEE Transactions on Signal Processing, vol. 67, no. 12, pp. 3227 – 3242, 2019. [24] C. L. Liu, “A general framework for the robustness of structured difference coarrays to element failure,” 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2020. [25] M. Wang, Z. Zhang, and A. Nehorai, “Direction finding using sparse linear arrays with missing data,” 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017. [26] C. L. Liu, “Sparse array signal processing: New array geometries, parameter estimation, and theoretical analysis.” Ph.D. dissertation, California Institute of Technology, 2018. [27] T. S. Wang, “Sparse array design and performance analysis with mutual coupling,” Master’s thesis, National Taiwan University, 2021. [28] D. H. Johnson and D. E. Dudgeon, Array Signal Processing: Concepts and Techniques. Simon and Schuster, Inc., 1992. [29] M. Haardt and J. A. Nossek, “Unitary ESPRIT: How to obtain increased estimation accuracy with a reduced computational burden,” IEEE Transactions on Signal Processing, vol. 43, no. 5, pp. 1232 – 1242, May 1995. [30] M. D. Zoltowski, M. Haardt, and C. P. Mathews, “Closed-form 2-D angle estimation with rectangular arrays in element space or beamspace via unitary ESPRIT,” IEEE Transactions on Signal Processing, vol. 44, no. 2, pp. 316 – 328, Feb 1996. [31] G. Xu, R. H. Roy, and T. Kailath, “Detection of number of sources via exploitation of centro-symmetry property,” IEEE Transactions on Signal Processing, vol. 42, no. 1, pp. 102 – 112, Jan 1994. [32] V. K. Madisetti and D. B. Williams, Eds., Digital Signal Processing Handbook. CRC Press LLC, 1999. [33] S. Ren, X. Ma, S. Yan, and C. Hao, “2-D unitary ESPRIT-like direction-of-arrival (DOA) estimation for coherent signals with a uniform rectangular array,” Sensors, vol. 13, no. 4, pp. 4272 – 4288, Mar. 2013. [34] S. Haykin, Ed., Adaptive Filter Theory, 5th ed. Pearson, 2014. [35] C. Berenstein, R. Gay, A. Vidras, and A. Yger, Eds., Residue Currents and Bezout Identities. Birkhäuser Basel, 1993. [36] C. Berenstein and A. Yger, “Analytic Bezout identities,” Advances in Applied Mathematics, vol. 10, no. 1, pp. 51 – 74, March 1989. [37] M. Abramowitz and I. Stegun, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. United States Department of Commerce, National Bureau of Standards, 1964. [38] C. H. Chen, “Robustness analysis of sparse arrays with sensor failures and array symmetry,” Master’s thesis, National Taiwan University, 2022. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88750 | - |
| dc.description.abstract | 近年來，陣列訊號處理在各種應用中被廣泛使用，利用多個感測器接收來自發射源的訊號。稀疏陣列因為差異協列有中心均勻線性陣列段，因此比傳統的均勻線性陣列有更好的效能。當中心均勻線性陣列段大小與差異協列的大小一樣時，則被稱為無洞差異協列。中心均勻線性陣列段越大，越能進一步提高效能。到達方向估計是稀疏陣列的一個應用，可以估計一維陣列和二維陣列中源的角度。然而，每個感測器在使用一段時間後都會有隨機錯誤的問題，導致接收訊號不正確。雖然一些方法解決了一維陣列中的這個問題，例如通過定義脆弱性來量化陣列的穩健性，但二維陣列的方法仍需要進一步研究。因此，我們開發了一種新型的二維稀疏陣列，稱為帶蓋式盒狀陣列。
帶蓋式盒狀陣列的陣列幾何形狀類似於開放式盒狀陣列順時針旋轉90度後帶有蓋子，其大小取決於參數W、H和S。帶蓋式盒狀陣列具有無洞差異協列。我們將根據移除感測器的影響來定義二維陣列的脆弱性，並證明帶蓋式盒狀陣列具有與均勻矩形陣列一樣最小的脆弱性。我們提出了一種方法來減少選擇帶蓋式盒狀陣列參數的搜尋範圍，主要是解決兩種情況。第一個情況是在固定N個感測器數量下最大化差異協列的大小。第二個情況是在在給定面積A的情況下最小化感測器的數量。 我們模擬了二維到達方向估計，比較了使用不同快照數量、信噪比和感測器錯誤機率p的帶蓋式盒狀陣列、均勻矩形陣列和開放式盒狀陣列的效能。雖然均勻矩形陣列和開放式盒狀陣列都有無洞差異協列，但開放式盒狀陣列的脆弱性相對於均勻矩形陣列和帶蓋式盒狀陣列更大。在相同數量的感測器下，開放式盒狀陣列由於具有最大的差異協列而表現最佳，其次是帶蓋式盒狀陣列，然後是均勻矩形陣列。但是，感測器錯誤會顯著影響開放式盒狀陣列的效能。當p增加時，在特定條件下，帶蓋式盒狀陣列的效能可以與開放式盒狀陣列相當甚至更好。 | zh_TW |
| dc.description.abstract | Array signal processing has found various applications in recent years, utilizing multiple sensors to receive signals from emitting sources. Sparse arrays offer better performance than traditional uniform linear arrays (ULAs) due to the difference coarrays with the central ULA segment. A hole-free difference coarray is achieved when the central ULA segment size matches the size of the difference coarray. A larger central ULA segment further improves performance. Direction-Of-Arrival (DOA) estimation is an application for sparse arrays which can estimate the angles of sources in one-dimensional (1-D) arrays and two-dimensional (2-D) arrays. However, each sensor has the problem of random failure after being used for some time, leading to incorrect signal reception. While some methods address this problem in 1-D arrays, such as by defining the fragility to quantify the robustness of the array, methods for 2-D arrays remain to be further investigated. Therefore, we develop a novel 2-D sparse array called the Lidded Box Array (LBA).
The LBA has an array geometry similar to an Open Box Array (OBA) rotated 90 degrees clockwise with a lid. Its size depends on the parameters: W, H, and S. The LBA has a hole-free difference coarray. We define the fragility of 2-D arrays based on the influence of removing a sensor and prove that the LBA has the minimum fragility as Uniform Rectangular Arrays (URAs). We propose a method to reduce the search range for selecting the parameters of the LBA by addressing two conditions. The first condition aims to maximize the size of the difference coarray with a fixed number of N sensors. The second condition aims to minimize the number of sensors for a given area A. We simulate the 2-D DOA estimation and compare the LBA with the URA and OBA under various numbers of snapshots, Signal-to-Noise Ratios (SNR), and probability p of sensor failures. Both the URA and OBA have the hole-free difference coarrays, but the fragility of the OBA is larger compared to the URA and LBA. With the same number of sensors, the OBA performs the best due to the largest difference coarray, followed by the LBA, and then the URA. However, sensor failures significantly affect the performance of the OBA. As p increases, the performance of the LBA becomes comparable to or even better than that of the OBA under certain conditions. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T17:38:06Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-15T17:38:06Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i
Acknowledgements iii 摘要v Abstract vii Contents ix List of Figures xiii List of Tables xxi Chapter 1 Introduction 1 1.1 Overview and Motivation . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Outline of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Chapter 2 Preliminaries 7 2.1 Array Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1 Review of the System Model of 1-D Arrays . . . . . . . . . . . . . 7 2.1.2 Review of the System Model of 2-D Arrays . . . . . . . . . . . . . 11 2.2 Difference Coarray . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3 Sensor Failures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.3.1 Sensor Failures in 1-D Arrays . . . . . . . . . . . . . . . . . . . . . 18 2.3.2 Definition of the Fragility in 2-D Arrays . . . . . . . . . . . . . . . 22 2.4 Review of URAs and OBAs . . . . . . . . . . . . . . . . . . . . . . 24 2.4.1 Uniform Rectangular Arrays (URAs) . . . . . . . . . . . . . . . . . 24 2.4.2 Open Box Arrays (OBAs) . . . . . . . . . . . . . . . . . . . . . . . 25 2.5 2-D DOA Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.5.1 2-D Unitary ESPRIT . . . . . . . . . . . . . . . . . . . . . . . . . 32 Chapter 3 Lidded Box Array (LBA) 37 3.1 Definition and Properties of the LBA . . . . . . . . . . . . . . . . . 37 3.2 The Difference Coarray of the LBA . . . . . . . . . . . . . . . . . . 42 3.3 The Fragility of the LBA . . . . . . . . . . . . . . . . . . . . . . . . 50 3.3.1 The Essential Sensors of the LBA . . . . . . . . . . . . . . . . . . 50 3.3.2 The Inessential Sensors of the LBA . . . . . . . . . . . . . . . . . . 53 3.4 Parameters of the LBA . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.4.1 Maximization of the Difference Coarray for N Sensors . . . . . . . 59 3.4.2 Minimization of the Number of Sensors for Area A . . . . . . . . . 64 Chapter 4 Simulation for 2-D DOA Estimation 69 4.1 Array Configurations . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.2 Simulation with Two Fixed Angles for All Arrays . . . . . . . . . . . 71 4.3 Simulation with Random Angles for All Arrays . . . . . . . . . . . . 74 4.3.1 All Arrays Have the Same Area Size . . . . . . . . . . . . . . . . . 75 4.3.2 All Arrays Have the Same Number of Sensors . . . . . . . . . . . . 77 4.4 Simulation for One Sensor Failure Each Time . . . . . . . . . . . . . 80 4.5 Simulation under the Sensor Failures . . . . . . . . . . . . . . . . . 82 4.5.1 All Arrays Have the Same Area Size . . . . . . . . . . . . . . . . . 83 4.5.2 All Arrays Have the Same Number of Sensors . . . . . . . . . . . . 85 4.5.3 Simulation with Two Specified Angles . . . . . . . . . . . . . . . . 88 Chapter 5 Conclusion and Future Work 91 References 93 Appendix A — Proof of the Difference Coarray of the LBA 99 A.0.1 The Difference Coarray between the Vertical Parts of the LBA . . . 102 A.0.2 The Difference Coarray between the Horizontal Parts of the LBA . . 103 A.0.3 The Difference Coarray between the Vertical Parts and Parallel Parts of the LBA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 A.0.4 The Difference Coarray between the Points and the Other Parts of the LBA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Appendix B — Proof of the Fragility of the LBA 113 B.0.5 X is in P1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 B.0.6 X is in P2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 B.0.7 X is in P3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 B.0.8 X is in Q1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 B.0.9 X is in Q2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 B.0.10 X is T1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 B.0.11 X is T2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 | - |
| dc.language.iso | en | - |
| dc.subject | 二維稀疏陣列 | zh_TW |
| dc.subject | 差異協列 | zh_TW |
| dc.subject | 到達方向估計 | zh_TW |
| dc.subject | 感測器錯誤 | zh_TW |
| dc.subject | 穩健性 | zh_TW |
| dc.subject | DOA estimation | en |
| dc.subject | difference coarrays | en |
| dc.subject | sensor failures | en |
| dc.subject | Two-dimensional sparse arrays | en |
| dc.subject | robustness | en |
| dc.title | 帶蓋式盒狀陣列：一種基於感測器錯誤具有增強穩健性的新型二維稀疏陣列設計 | zh_TW |
| dc.title | Lidded Box Arrays: A Novel 2-D Sparse Array Design for Robustness to Sensor Failures | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林源倍;馮世邁 | zh_TW |
| dc.contributor.oralexamcommittee | Yuan-Pei Lin;See-May Phoong | en |
| dc.subject.keyword | 二維稀疏陣列,到達方向估計,差異協列,感測器錯誤,穩健性, | zh_TW |
| dc.subject.keyword | Two-dimensional sparse arrays,DOA estimation,difference coarrays,sensor failures,robustness, | en |
| dc.relation.page | 187 | - |
| dc.identifier.doi | 10.6342/NTU202303064 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-08-09 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電信工程學研究所 | - |
| dc.date.embargo-lift | 2024-09-01 | - |
| Appears in Collections: | 電信工程學研究所 | |
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