A Conceptual Framework for the Use of Minimum Redundancy Linear Arrays and Flexible Arrays in Future Smartphones

Patwari, Ashish; Reddy, G. Ramachandra

doi:10.1155/2018/9629837

Cited by 7 publications

(8 citation statements)

References 34 publications

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“…Similarly, the receiver would be a 6-element YINA capable of providing an aperture of 13. The respective transmitting and receiving arrays can then be easily obtained since YINA has closed-form 19,38,114,190,247] [0, 1,4,7,9,19,20,23,26,28,38,39,42,45,47,114,115,118,121,123,190,191,194,197,199,247,248,251,254,256] 17,34,51,119,187] [0, 1,2,5,8,17,18,19,22,25,34,35,36,39,42,51,52,53,…”

Section: Numerical Simulation Resultsmentioning

confidence: 99%

“…However, the use of linear sparse arrays as a replacement to ULAs could ease the system complexity if they can provide an acceptable level of estimation accuracy. Here too, MRAs remain a preferred choice among sparse arrays as they provide the largest filled difference co-array and the best resolution for a given number of sensors [7,35]. As mentioned before, MRAs with twelve or more sensors are named as LRLAs.…”

Section: Doa Estimation In Simo Radars Using Lrlamentioning

confidence: 99%

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Novel Mra-Based Sparse Mimo and Simo Antenna Arrays for Automotive Radar Applications

Gudheti

2020

PIER B

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Automotive radars make use of angle information obtained from antenna arrays to distinguish objects that lie in the same range-doppler cell (relative to the ego vehicle). This paper proposes novel ways of using presently known minimum redundancy arrays (MRAs) in single-input multiple-output (SIMO) and multiple-input multiple-output (MIMO) automotive radars. Firstly, an MRA-based sparse MIMO array is proposed as a novel modification to the nested MIMO array. The proposed sparse MIMO array uses MRAs as the transmitting and receiving modules, unlike the nested MIMO array, which uses two-level nested arrays (TLNAs) at the transmitting and receiving blocks. Upper bounds for the sum co-array aperture and the overall attainable degrees of freedom (DOF) offered by the MIMO radar have been derived in terms of the number of sensors. Secondly, the suitability of large Low-Redundancy Linear Arrays (LRLAs) in SIMO automotive radars is also studied. A long-range automotive radar driving scenario was assumed for DOA estimation and simulations were carried out in MATLAB using the co-array MUltiple SIgnal Classification (co-array MUSIC) algorithm. Simulation results confirm that the proposed MRA-based MIMO array provides better angular resolutions than the nested MIMO array for the same number of sensors and that LRLAs can serve as a handy replacement for ULAs in SIMO radars owing to their acceptable performance. As MIMO and SIMO radars designed from currently known MRAs were sufficient to satisfy the angular resolution requirements of modern automotive radars, a need to synthesize new MRAs did not arise.

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Section: Numerical Simulation Resultsmentioning

confidence: 99%

Section: Doa Estimation In Simo Radars Using Lrlamentioning

confidence: 99%

Novel Mra-Based Sparse Mimo and Simo Antenna Arrays for Automotive Radar Applications

Gudheti

2020

PIER B

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“…According to literatures [37,47], when the number of elements is less than 5, zero-redundancy arrays exist. In other words, there are only four zero-redundancy arrays, which have been elegantly proved in literature [52]. The four zero-redundancy arrays and their spatial distributions are shown in Figure 2.…”

Section: Minimum Redundancy Array In Sar Tomographymentioning

confidence: 89%

“…A MRA is formed from a full antenna array by carefully eliminating redundant antennas while the retained elements can generate all possible antenna separation between zero and the maximum antenna length. Its merits come from the fact that it can provide the highest possible resolution with minimized redundancy [51,52]. Numerically, redundancy R is defined as the number of pairs of antennas divided by maximum spacing of the linear array.…”

Section: Minimum Redundancy Array In Sar Tomographymentioning

confidence: 99%

Minimum Redundancy Array—A Baseline Optimization Strategy for Urban SAR Tomography

et al. 2020

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Synthetic aperture radar (SAR) tomography (TomoSAR) is able to separate multiple scatterers layovered inside the same resolution cell in high-resolution SAR images of urban scenarios, usually with a large number of orbits, making it an expensive and unfeasible task for many practical applications. Targeting at finding out the minimum number of images necessary for tomographic reconstruction, this paper innovatively applies minimum redundancy array (MRA) for tomographic baseline array optimization. Monte Carlo simulations are conducted by means of Two-step Iterative Shrinkage/Thresholding (TWIST) and Truncated Singular Value Decomposition (TSVD) to fully evaluate the tomographic performance of MRA orbits in terms of detection rates, Cramer Rao Lower Bounds, as well as resistance against sidelobes. Experiments on COSMO-SkyMed and TerraSAR-X/TanDEM-X data are also conducted in this paper. The results from simulations and experiments on real data have both demonstrated that introducing MRA for baseline optimization in SAR tomography can benefit from the dramatic reduction of necessary orbit numbers, if the recently proposed TWIST method is used for tomographic reconstruction. Although the simulation and experiments in this manuscript are carried out using spaceborne data, the outcome of this paper can also give examples for airborne TomoSAR when designing flight orbits using airborne sensors.

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“…Sparse arrays are known to offer higher degrees of freedom than regular arrays for the same number of sensors. Alternately, they need fewer sensors to offer the same aperture as regular/filled arrays [17]- [19]. Of late, sparse microphone arrays have made their way into audio source localization in recent times [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources

Patwari¹

2020

IJETER

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Microphone arrays are commonly used for Direction of Arrival (DOA) estimation of audio sources. This paper presents the design and implementation of a real-time embedded system (RTES) for audio source localization. A commercially available microphone array assembly (consisting of four microphones) has been interfaced to Raspberry Pi 3B for estimating the arrival angles of audio/sound/acoustic sources. The acoustic signals received a teach of the microphones are used for DOA estimation using the Generalized Cross Correlation Phase Transform (GCC-PHAT) algorithm. The estimated DOA is then displayed on the serial monitor. Servo motors are interfaced to the Raspberry Pi and are programmed to point towards the estimated direction of the audio source (on the basis of the estimated DOA value). The estimated DOA value is also updated to the cloud-based server Firebase using the inbuilt Wi-Fi on Raspberry Pi, and can be remotely accessed through a smartphone running on android platform. The proposed system has been designed and tested successfully and it is observed that it provides accurate DOA estimation and audio source tracking in real-time. This has various uses in applications like voice assistant robots, machine audition, hearing aids, source remixing, augmented reality headsets, security cameras, sound tracking (sonar sensor) devices, bird identification, mammal localization and so on.

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A Conceptual Framework for the Use of Minimum Redundancy Linear Arrays and Flexible Arrays in Future Smartphones

Cited by 7 publications

References 34 publications

Novel Mra-Based Sparse Mimo and Simo Antenna Arrays for Automotive Radar Applications

Novel Mra-Based Sparse Mimo and Simo Antenna Arrays for Automotive Radar Applications

Minimum Redundancy Array—A Baseline Optimization Strategy for Urban SAR Tomography

Microphone Array and Raspberry Pi Interfacing for Real-time DOA Estimation and Tracking of Audio Sources

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