Experimental adaptive cylindrical array

Haupt, Randy L.; Southall, H.L.

doi:10.1109/aero.1999.789789

Cited by 6 publications

(7 citation statements)

References 9 publications

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“…There is no mechanism provided for beamforming on the various curvatures. In Reference 8, experimental demonstration of sidelobe cancelation on cylindrical array using genetic algorithm is done. With genetic algorithm, single null placement is achieved without main beam scanning and multiple nulls placement capability.…”

Section: Introductionmentioning

confidence: 99%

Cylindrical phased array with adaptive nulling using eigen‐correlation technique

Ullah

Munsif

Razzaq

et al. 2021

Int J RF Mic Comp-Aid Eng

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In this research work, eigen‐correlation matrix based technique is developed for cylindrical antenna array to generate main beam and nulls simultaneously. The array complex weights (amplitude and phase) computed using the developed technique are used for single‐beamforming, multi‐beamforming, and beamforming with nulls steering simultaneously. The limitations of null steering range are investigated for different surface deformations of the cylindrical antenna array. The effects of surface deformations for different cylindrical radii on peak gain of main beam, half‐power beamwidth, first‐null beamwidth, and sidelobe level is also investigated. It is concluded that the developed eigen‐correlation matrix based technique for cylindrical antenna array can be used for desired main beam generation and nulls steering simultaneously. The MATLAB, CST, and measurement validation of eight elements microstrip cylindrical phased array for single/multi‐beams steering and adaptive nulling capability is demonstrated. This in turn makes the cylindrical phased array with adaptive nulling, a potential candidate suitable for use on fuselage portion of aircraft, airport surveillance radars, and mobile communication applications.

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Section: Introductionmentioning

confidence: 99%

Cylindrical phased array with adaptive nulling using eigen‐correlation technique

Ullah

Munsif

Razzaq

et al. 2021

Int J RF Mic Comp-Aid Eng

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“…Least-Mean-Square (LMS) and Recursive-Least-Square (RLS) algorithms are well-known examples of mathematical solutions to array optimization (see [3] for a thorough overview). Despite their mathematical elegance, such methods present some drawbacks, hindering their practical implementation [4] [5] [6]. In more detail, LMS and RLS require analog amplitude and phase weights at each element.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the convergence of conventional approaches for array optimization strongly depends upon the eigenvalue spread [3] and on the external noise environment. Moreover, these techniques result very slow in severe jamming situations [8] and do not prevent the solution be trapped in local minima [4] [6]. In this framework, the use of Genetic Algorithms (GAs) can be regarded as a valuable solution for the array optimization problem [4] [5] [9].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Antenna Array Control in the Presence of Interfering Signals With Stochastic Arrivals: Assessment of a GA-Based Procedure

Sacchi

Natale

Donelli

et al. 2004

IEEE Trans. Wireless Commun.

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“…Some prototypes have been implemented making use of complex acquisition systems, where the signal is collected at the receiver and at the output of the array elements in order to compute the co-variance matrix [3]. On the other hand, simpler fully-adaptive systems based on the measurement of the received signal at the receiver have been also implemented [4]. In both cases, the effectiveness of the implementation has been assessed by comparing measured and simulated radiation patterns in correspondence with a single interferer incoming from a fixed direction.…”

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confidence: 99%

Experimental validation of a smart antenna system model

Benedetti

Oliveri

Donelli

et al. 2009

2009 IEEE Antennas and Propagation Society International Symposium

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IntroductionIn the last decade, the need of effective mobile communication devices has favored the development of wireless technologies. Nowadays, the demand of dealing with complex communication scenarios characterized by multiple users and standards fosters the study of systems able to provide a suitable quality of service (QoS) and an enhanced security [1]. In such a framework, smart antennas have been recognized as promising tools for an efficient management of the physical layer. As a matter of fact, these systems are aimed at maximizing the signal-to-interference-plus-noise ratio (SINR) at the receiver by steering the main lobe of the beam pattern to track the desired signals and placing attenuations to cancel the interferences. With respect to standard solutions, smart antennas allow to increase the channel capacity and the service coverage [2]. Although the effectiveness of a hardware implementation has been theoretically proved, the technological difficulties and costs for the implementation of fullyadaptive solutions prevented up till now the application of smart antennas in wireless communications. Some prototypes have been implemented making use of complex acquisition systems, where the signal is collected at the receiver and at the output of the array elements in order to compute the co-variance matrix [3]. On the other hand, simpler fully-adaptive systems based on the measurement of the received signal at the receiver have been also implemented [4]. In both cases, the effectiveness of the implementation has been assessed by comparing measured and simulated radiation patterns in correspondence with a single interferer incoming from a fixed direction. Recently, a smart antenna prototype characterized by a simple functional scheme has been proposed in [5]. In this implementation, the signals collected by the array elements are suitably weighted by the hardware control unit according to an iterative strategy based on the Particle Swarm Optimizer (PSO) in order to maximize a suitable fitness function. The antenna has been preliminarily validated by considering both the behavior of the SINR in a complex scenario and the capability of placing nulls in suitable positions of the radiation pattern. In addition, the measured quantities have been compared with the simulations provided by a simple system model [6]. In order to take into account the complexity of the hardware implementation, this paper proposes an innovative model of the adaptive system based on the integration of the strategy proposed in [6] with a suitable fitness function. After presenting the mathematical formulation, the proposed approach is validated by considering real data collected in an experiment modeling a complex interference scenario. Mathematical Formulation of the System Modelwhere ( , and a background noise contribution ( )

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Experimental adaptive cylindrical array

Cited by 6 publications

References 9 publications

Cylindrical phased array with adaptive nulling using eigen‐correlation technique

Cylindrical phased array with adaptive nulling using eigen‐correlation technique

Adaptive Antenna Array Control in the Presence of Interfering Signals With Stochastic Arrivals: Assessment of a GA-Based Procedure

Experimental validation of a smart antenna system model

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