Performance Analysis of MUSIC and ESPRIT DOA Estimation Algorithms for Adaptive Array Smart Antenna in Mobile Communication

Lavate, T.B.; Kokate, Vishwanath K; Sapkal, A. M.

doi:10.1109/iccnt.2010.45

Cited by 141 publications

(73 citation statements)

References 3 publications

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“…The requirements of modern high technology war weapon system include a fast response and good stealth performance that can timely detect the target, track the target, capture and lock the target, and realize the effective attack [1][2][3][4][5][6]. The infrared detection and imaging distance tracking technology can well satisfy this condition as it possesses conventional imaging modalities such as visible light imaging and radar imaging compared.…”

Section: Introductionmentioning

confidence: 99%

Detection and Tracking of Infrared Dim Small Image Sequence Moving Target

Yang¹

2015

TOAUTOCJ

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Infrared dim target detection and tracking is widely used in precision guidance, automatic control and other fields. As the small target imaging system has far distance, small area, low SNR infrared characteristics and undergoes image preprocessing, dim target detection technology is more important and more difficult. As a full play of the advantages of infrared small target detection technique has been provided, this has significance in infrared imaging. Based on introducing the tracking technology development status at home and abroad by the study of small target detection, the problems of detecting and tracking small moving target in infrared image sequence of background noise and interference conditions, aiming at dynamic planning in energy diffusion problem are presented. The improved algorithm of dynamic programming is also presented including weighted dynamic programming algorithm and the realization steps of the algorithm. Detection structure and the experimental results are analyzed. The results show that the algorithm has a good effect. In the end, this paper studies the rejected false targets in IR image track association method to obtain real object trajectories for moving small target detection, so as to improve the detection rate and reduce the false alarm rate.

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Section: Introductionmentioning

confidence: 99%

Detection and Tracking of Infrared Dim Small Image Sequence Moving Target

Yang¹

2015

TOAUTOCJ

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“…Many algorithms were developed around this approach including the Multiple Signal Classification (MUSIC) algorithm. MUSIC can be used efficiently for DoA estimation of the non-coherent signals [4][5][6]. However, it fails if the signals impinging the array are coherent [11].…”

Section: Direction Of Arrival Estimation Algorithmsmentioning

confidence: 99%

“…For indoor localization scenarios, multipath signals are dominant, which are considered as coherent signal as seen by the DoA estimator algorithm [3]. A famous and extensively studied technique for DoA estimation is the Multiple Signal Classification (MUSIC) [4][5][6]. It is a super resolution DoA estimation but for non-coherent signals [7].…”

Section: Introductionmentioning

confidence: 99%

Performance assessment for MUSIC-based DoA estimators

Ghonim

Napoleon

Attia

2016

IJET

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Direction of arrival estimation is a popular technique used for localization and tracking. Many techniques were developed to detect it even in a multipath environment. In this paper we introduce an assessment for four techniques that are based on the MUSIC algorithm. They are the Toeplitz, modified virtual SVD (MV-SVD), Virtual array extension (VSS) and the Modified VSS (MVSS). Two assessment metrics are employed for evaluating these techniques under several conditions such as various SNR vales and numbers of samples. These metrics are RMSE and the success rate.

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“…Among the high resolution techniques that rely on eigenvalues properties is the ESPRIT (Estimation of Signal Parameters via Rotational Invariance) algebraic method [8,9], another method based on angular spectrum and peak detection procedure is the EG [10] (Ermolaev and Gershman) where the constructed operator is based on chosen threshold value of the eigenvalues of spectral matrix. In fact, the signal vector at base station can be decomposed into two complementary parts, signal and noise subspaces [5,6,7] that are orthogonal to each other, each part corresponds to a set of eigenvalues of spectral matrix, the EG operator [10] is computed based on threshold value between signal and noise eigenvalues. Theoretically, the signal eigenvalues have higher magnitudes and noise eigenvalue is degenerate q times where q in the number of sensors of the array minus the number of present sources, the threshold is strictly superior to the noise eigenvalue and strictly inferior to the smallest signal eigenvalue.…”

Section: Introductionmentioning

confidence: 99%