Displaced Sensor Array for Improved Signal Detection Under Grazing Incidence Conditions

Shubair, Raed M.; Nuaimi, Rashid S.

doi:10.2528/pier07103005

Cited by 11 publications

(15 citation statements)

References 21 publications

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“…So, in many research works, it is attempted to develop linear arrays or combine them together to obtain better performance while having proportional simplicity. Parallel linear arrays, displaced sensor arrays (DSAs), uniform rectangular arrays (URAs), and L-shaped, V-shaped, and Y-shaped arrays are some of the investigated configurations [10][11][12][13][14][15][16]. These geometries have the capability of 2D-DOA estimation.…”

Section: International Journal Of Antennas and Propagationmentioning

confidence: 99%

Efficient Narrowband Direction of Arrival Estimation Based on a Combination of Uniform Linear/Shirvani-Akbari Arrays

Moghaddam

Akbari²

2012

International Journal of Antennas and Propagation

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Uniform linear array (ULA) geometry does not perform well for direction of arrival (DOA) estimation at directions close to the array endfires. Shirvani and Akbari solved this problem by displacing two elements from both ends of the ULA to the top and/or bottom of the array axis. Shirvani-Akbari array (SAA) presents a considerable improvement in the DOA estimation of narrowband sources arriving at endfire directions in terms of DOA estimation accuracy and angular resolution. In this paper, all new proposed SAA configurations are modelled and also examined, numerically. In this paper, two well-known DOA estimation algorithms, multiple signal classification (MUSIC) and minimum variance distortionless response (MVDR), are used to evaluate the effectiveness of proposed arrays using total root mean square error (RMSE) criterion. In addition, two new scenarios are proposed which divide angular search to two parts, directions close to array endfires as well as middle angles. For middle angles, which belong to (−70 • ≤ θ ≤ 70 • ), ULA is considered, and for endfire angles, the angles which belong to (−90 • ≤ θ ≤ −70 • ) and (70 • ≤ θ ≤ 90 • ), SAA is considered. Simulation results of new proposed scenarios for DOA estimation of narrowband signals show the better performance with lower computational load.

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Section: International Journal Of Antennas and Propagationmentioning

confidence: 99%

Efficient Narrowband Direction of Arrival Estimation Based on a Combination of Uniform Linear/Shirvani-Akbari Arrays

Moghaddam

Akbari²

2012

International Journal of Antennas and Propagation

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“…ULA is the most common geometry for smart antennas because of its simplicity, excellent directivity and production of the narrowest main lobe in a given direction in comparison to the other array geometries [22]. In a ULA, as it is seen in Figure 1, the elements are aligned along a straight line and with a uniform inter-element spacing usually d = λ/2, where λ denotes the wavelength of the received signal.…”

Section: Smart Antennasmentioning

confidence: 99%

“…σ n 2 and I are variance of noise and identity matrix, respectively. Since the antennas cannot receive DC signals, the mean values of arriving signals and noise are zero and so, the correlation matrix obtained in (5) is referred as covariance matrix [22]. This matrix is used for many beamforming and AOA estimation algorithms such as MUSIC and MVDR.…”

Section: Signal Model For the Ula And Pa Configurationsmentioning

confidence: 99%

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A novel ULA-based geometry for improving AOA estimation

Moghaddam

Akbari

2011

EURASIP J. Adv. Signal Process.

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Due to relatively simple implementation, Uniform Linear Array (ULA) is a popular geometry for array signal processing. Despite this advantage, it does not have a uniform performance in all directions and Angle of Arrival (AOA) estimation performance degrades considerably in the angles close to endfire. In this article, a new configuration is proposed which can solve this problem. Proposed Array (PA) configuration adds two elements to the ULA in top and bottom of the array axis. By extending signal model of the ULA to the new proposed ULAbased array, AOA estimation performance has been compared in terms of angular accuracy and resolution threshold through two well-known AOA estimation algorithms, MUSIC and MVDR. In both algorithms, Root Mean Square Error (RMSE) of the detected angles descends as the input Signal to Noise Ratio (SNR) increases. Simulation results show that the proposed array geometry introduces uniform accurate performance and higher resolution in middle angles as well as border ones. The PA also presents less RMSE than the ULA in endfire directions. Therefore, the proposed array offers better performance for the border angles with almost the same array size and simplicity in both MUSIC and MVDR algorithms with respect to the conventional ULA. In addition, AOA estimation performance of the PA geometry is compared with two well-known 2D-array geometries: L-shape and V-shape, and acceptable results are obtained with equivalent or lower complexity.

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“…Co mbination of linear arrays is considered in literature and has shown improve-ments in array performance specially the accuracy of DOA estimation algorith ms. So me of these structures are parallel linear arrays and Displaced Sensor Arrays (DSAs), Lshaped and two L-shaped arrays, V-shaped, Y-shaped, Uniform Rectangular Arrays (URA), hexagonal arrays and so on [6][7][8][9][10][11]. Each configuration has particular properties and capabilit ies.…”

Section: Introductionmentioning

confidence: 99%

Improving LMS/NLMS-Based Beamforming Using Shirvani-Akbari Array

Moghaddam¹,

Akbari²

2012

AJSP

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ULA is the most common geometry exp lo ited in array signal processing. In the beamforming operation, employing the ULA leads to obtaining narrower beamwidth with respect to other geometries in similar element numbers. Recently, Shirvani and Akbari proposed a new array by adding two elements to the ULA in top and bottom of the array axis, named as SAA. Th is new array offers a considerable imp rovement in DOA estimation performance in detection and resolution of signal sources placed at angles close to the array endfires. In this article, the performance of the proposed SAA is investigated especially in beamforming and co mpared with ULA. LMS and NLMS algorith ms that are popular adaptive beamforming methods are used for evaluation and co mparing the performance of SAA and ULA. Considering array factor, mean square erro r and bit error rate metrics, simu lation results show improved convergence speed and higher data transmission accuracy in different signal source locations, boresight angles as well as endfire ones, for SAA with respect to ULA.

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Displaced Sensor Array for Improved Signal Detection Under Grazing Incidence Conditions

Cited by 11 publications

References 21 publications

Efficient Narrowband Direction of Arrival Estimation Based on a Combination of Uniform Linear/Shirvani-Akbari Arrays

Efficient Narrowband Direction of Arrival Estimation Based on a Combination of Uniform Linear/Shirvani-Akbari Arrays

A novel ULA-based geometry for improving AOA estimation

Improving LMS/NLMS-Based Beamforming Using Shirvani-Akbari Array

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