Adaptive array beamforming using a combined LMS-LMS algorithm

Abstract-In this paper, a novel beamformer for adaptive combination of two adaptive filters is proposed for interference mitigation of sensor array. The proposed approach adaptively combines two individual filters by coefficient weights vector instead of one scale parameter and takes the constraint of affine combination into consideration rather than previous studies. Due to the more degrees of freedom offered by the mixing vector, the proposed beamformer significantly improves the convergence and tracking performances of the combined filter under both stationary and non-stationary environments, respectively. Based on the generalized sidelobe canceller (GSC) structure, the optimal mixing vector is derived by Lagrange method, and then several new effective iterative algorithms are developed for its updating in practical implementation. Furthermore, theoretical discussions of the convergent performances and complexities of the proposed iterative algorithms are also investigated to verify the feasibility of the proposed beamformer. Moreover, the proposed methods in application of beamforming for interference mitigation of antenna array are simulated based space-time processing technique. When compared to existing methods, the proposed approach exhibits faster convergence rate and higher output signal to interference plus noise ratio (SINR). Its good behavior is illustrated through simulation results.

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“…We also investigate the effect of SINR loss at the output of beamformer, when a jammer arrives from different DOAs as [42] in …”

Section: Simulation Resultsmentioning

confidence: 99%

A Mixing Vector Based an Affine Combination of Two Adaptive Filters for Sensor Array Beamforming

Sun

Wang

2012

PIER

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“…In addition to above algorithms, some combined and hybrid algorithms are also used for beamforming [5][6][7]. A better performance for reduction of side lobe level has achieved by using VSLMS algorithm [8].…”

Section: Asian Journal Of Applied Sciences (Issn: 2321 -0893)mentioning

confidence: 99%

Adaptive Beam Formation in Smart Antenna Using VSLMS and VSNLMS Algorithms for Side Lobe Level Reduction

Bairagya

2017

AJAS

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In mobile communication using smart antenna, the capacity of a cellular network can be enhanced significantly. Adaptive beamforming is one of the critical issues in smart antenna. A comparative study of beamforming algorithms by using their variable step size parameter like, variable step size least mean square algorithm (VSLMS) and variable step size normalized least mean square algorithm (VSNLMS) are presented in this paper. Smart antennas of 8 elements and 10 elements linear array are considered here. It is found that using VSNLMS algorithm a lower side lobe level is achieved compared to VSLMS algorithm for various angles of desired user and undesired interferer.

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“…Using the Leonov-Shiryayev formula, one can develop an unbiased estimator for the cumulant as e Cum 4 Let μ r {k} be the estimator of the rth-order moment at the kth iteration, and we can develop the following adaptive estimators for the second-and the fourth-order statistics: …”

Section: Unbiased and Adaptive Hos Estimatorsmentioning

confidence: 99%

“…In numerous proposed algorithms, statistical properties or probability density function (PDF) of transmission channels are assumed to be perfectly known or already estimated [2][3][4][5][6]. To analyze the performance of various wireless transmission schemes, Simon and Alouini in [7] have used channel statistical models and PDF properties.…”

Section: Introductionmentioning

confidence: 99%

Blind estimation of statistical properties of non-stationary random variables

Mansour

Mesleh

Aggoune

2014

EURASIP J. Adv. Signal Process.

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To identify or equalize wireless transmission channels, or alternatively to evaluate the performance of many wireless communication algorithms, coefficients or statistical properties of the used transmission channels are often assumed to be known or can be estimated at the receiver end. For most of the proposed algorithms, the knowledge of transmission channel statistical properties is essential to detect signals and retrieve data. To the best of our knowledge, most proposed approaches assume that transmission channels are static and can be modeled by stationary random variables (uniform, Gaussian, exponential, Weilbul, Rayleigh, etc.). In the majority of sensor networks or cellular systems applications, transmitters and/or receivers are in motion. Therefore, the validity of static transmission channels and the underlying assumptions may not be valid. In this case, coefficients and statistical properties change and therefore the stationary model falls short of making an accurate representation. In order to estimate the statistical properties (represented by the high-order statistics and probability density function, PDF) of dynamic channels, we firstly assume that the dynamic channels can be modeled by short-term stationary but long-term non-stationary random variable (RV), i.e., the RVs are stationary within unknown successive periods but they may suddenly change their statistical properties between two successive periods. Therefore, this manuscript proposes an algorithm to detect the transition phases of non-stationary random variables and introduces an indicator based on high-order statistics for non-stationary transmission which can be used to alter channel properties and initiate the estimation process. Additionally, PDF estimators based on kernel functions are also developed. The first part of the manuscript provides a brief introduction for unbiased estimators of the second and fourth-order cumulants. Then, the non-stationary indicators are formulated. Finally, simulation results are presented and conclusions are derived.

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Adaptive array beamforming using a combined LMS-LMS algorithm

Cited by 39 publications

References 18 publications

A Mixing Vector Based an Affine Combination of Two Adaptive Filters for Sensor Array Beamforming

A Mixing Vector Based an Affine Combination of Two Adaptive Filters for Sensor Array Beamforming

Adaptive Beam Formation in Smart Antenna Using VSLMS and VSNLMS Algorithms for Side Lobe Level Reduction

Blind estimation of statistical properties of non-stationary random variables

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