A brief study of the ESPRIT Direction-Of-Arrival estimation algorithm in an uncorrelated environment for application in the Smart Antenna System

<p>This paper presents the performance analysis of the direction of arrival estimation algorithms such as Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), Multiple Signal Classification (MUSIC), Weighted Subspace Fitting (WSF), The Minimum Variance Distortionless Response (MVDR or capon) and beamspace. These algorithms are necessary to overcome the problem of detecting the arrival angles of the received signals in wireless communication. Therefore, these algorithms are evaluated and compared according to several constraints required in smart antenna system parameters, as the number of array elements, number of samples (snapshots), and number of the received signals. The main purpose of this study is to obtain the best estimation of the direction of arrival, which can be perfectly implemented in a smart antenna system. In this context, the ROOT-Weighted Subspace Fitting algorithm provides the most accurate detection of arrival angles in each of the proposed scenarios.</p>

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Angle of Arrival Estimation in Smart Antenna for Wireless Communication

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.7.20529 ◽

2018 ◽

Vol 7 (4.7) ◽

pp. 136

Author(s):

T. S. Jyothi Lakshmi ◽

S. Sandeep ◽

Dr. V. Rajya Lakshmi

Keyword(s):

Smart Antenna ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Minimum Variance ◽

Antenna System ◽

Maximum Efficiency ◽

Communication Link ◽

Performance Study ◽

Angle Of Arrival ◽

Comparative Performance

Smart antenna technology has emerged as one of the most efficient techniques in supporting maximum communication link throughput. A smart antenna system with innovative signal processing can enhance the resolution of a signal’s direction of arrival estimation. Successful design of Smart antenna is dependent on the efficient performance of DOA estimation algorithm as well as beamforming algorithm. This paper presents a comparative performance study between Classical Beam forming method, MUSIC (Multiple Signal Classification), MVDR (Minimum Variance Distortion less Response) which are different direction of arrival algorithms used in linear antenna array. The objective is to analyse and compare these algorithms and determine which provides maximum efficiency.

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Analysis of optimized signal processing algorithms for smart antenna system

Neural Computing and Applications ◽

10.1007/s00521-012-1035-x ◽

2012 ◽

Vol 23 (3-4) ◽

pp. 1083-1087 ◽

Cited By ~ 20

Author(s):

Muhammad Asif Gondal ◽

Amir Anees

Keyword(s):

Signal Processing ◽

Smart Antenna ◽

Antenna System ◽

Signal Processing Algorithms ◽

Processing Algorithms ◽

Smart Antenna System

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An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix

Sensors ◽

10.3390/s21134403 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4403

Author(s):

Ji Woong Paik ◽

Joon-Ho Lee ◽

Wooyoung Hong

Keyword(s):

Covariance Matrix ◽

Phased Array ◽

Null Space ◽

Estimation Method ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Direction Of Arrival Estimation ◽

Improve Performance ◽

Space Projection

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.

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A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

Sensors ◽

10.3390/s21062191 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2191

Author(s):

Huichao Yan ◽

Ting Chen ◽

Peng Wang ◽

Linmei Zhang ◽

Rong Cheng ◽

...

Keyword(s):

Compressed Sensing ◽

Spatial Clustering ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Cluster Center ◽

Direction Of Arrival Estimation ◽

Vector Hydrophone ◽

Regularization Parameters ◽

Selection Of

Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).

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