Minimum Variance Distortionless Response Beamformer with Enhanced Nulling Level Control via Dynamic Mutated Artificial Immune System

Kiong, Tiong Sieh; Salem, Sanaz; Paw, Johnny Koh Siaw; Sankar, K. Prajindra; Darzi, Soodabeh

doi:10.1155/2014/164053

Cited by 29 publications

(15 citation statements)

References 20 publications

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“… Repeat until termination criteria are met. DM-AIS [ 18 ] is a type of AIS algorithm with a new dynamic mutation function. The new population of antibodies is derived from the fitness value based on dynamic mutation rate.…”

Section: Methodsmentioning

confidence: 99%

“…The maximum iterations for DM-AIS are 500 for fair comparison with GSA and PSO. DM-AIS parameters implemented in this study were the same as in [ 18 ].…”

Section: Proposed Lcmv Beamforming Assisted By Gravitational Searcmentioning

confidence: 99%

“…Previous studies show that the employment of metaheuristics algorithm has been growing instead of exhaustive and exact procedures. In this regard, approaches such as genetic algorithms (GA) [ 6 , 7 ], artificial bee colony (ABC) [ 8 , 9 ], differential evolution (DE) [ 10 ], particle swarm optimization (PSO) [ 11 , 12 ], ant colony optimization (ACO) [ 13 – 15 ], tabu search (TS) [ 8 , 16 , 17 ], artificial immune system (AIS) [ 18 ], and clonal selection (CS) [ 19 , 20 ] have been used to solve a variety of problems in order to improve various issues in antenna system. According to the above-mentioned study, the main goal of this paper is to optimize the LCMV beamforming weights by PSO and gravitational search algorithm (GSA) technique to improve the performance of system in the expect of SINR.…”

Section: Introductionmentioning

confidence: 99%

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Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

Darzi

Kiong

Islam

et al. 2014

The Scientific World Journal

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Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program.

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Section: Methodsmentioning

confidence: 99%

“…The maximum iterations for DM-AIS are 500 for fair comparison with GSA and PSO. DM-AIS parameters implemented in this study were the same as in [ 18 ].…”

Section: Proposed Lcmv Beamforming Assisted By Gravitational Searcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

Darzi

Kiong

Islam

et al. 2014

The Scientific World Journal

Self Cite

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“…Dynamic mutated artificial immune system (DM-AIS) properties were used with MVDR by Kiong [16] to track and correct steering vector errors. The DM-AIS algorithm controlled the null level in the direction of the interference.…”

Section: Related Workmentioning

confidence: 99%

Modifying MVDR Beamformer for Reducing Direction-of-Arrival Estimation Mismatch

Abdulrahman

Rahman

Hassnawi³

et al. 2015

Arab J Sci Eng

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The minimum variance distortionless response (MVDR) beamforming algorithm is used in smart antenna design for wireless communication. The operation of MVDR is based on finding the optimum weight to direct the main lobe beam to the desired user location with a unity gain. MVDR is very sensitive to signature vector mismatch. This mismatch occurs due to waveform deformation, local scattering, imperfect array element calibration and element shape distortion, which leads to errors in finding the direction of arrival (DOA) of the signal. In this paper, a new technique to modify the MVDR is presented, modelled and evaluated. The proposed algorithm is named modified MVDR (MMVDR) and is dependent on reconstructing the signature vector (steering vector) and the covariance matrix to introduce accurate beamformer weight by re-localization the reference element to be in the middle of ULA, rather than at one end side. The new reference position partitions the array's elements into two groups around this reference, which leads to treat received signals with identical phase along the array's elements, as well as increasing the degree of freedom to deals with different types of uniform arrays. The evaluation results show that MMVDR outperforms MVDR with respect to beamformer accuracy, system cost, processing time and signal classification to overcome the errors in DOA estimation which occur due to fabrication and calibration errors.

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“…Thus, to localize the near field targets, the range information must also be incorporated into the signal model along with DOA [3,4]. Near field targets localization, especially 3D (range, elevation angle, and azimuth angle), plays significant role in radar, cognitive radio networks, and array signal processing, since it is a preliminary step for adaptive beamformer to guide the main beam in preferred direction and simultaneously manage the nulls in the direction of jammers [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Multiple Target Localization with Bistatic Radar Using Heuristic Computational Intelligence Techniques

Zaman

Qureshi

Rehman

et al. 2015

International Journal of Antennas and Propagation

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on its receiver. Let the transmitter of this Bistatic radar send coherent signals using a subarray that gives a fairly wide beam with a large solid angle so as to cover up any potential relevant target in the near field. We developed Heuristic Computational Intelligence (HCI) based techniques to jointly estimate the range, amplitude, and elevation and azimuth angles of these multiple targets impinging on the CSCA. In this connection, first the global search optimizers, that is,are developed separately Particle Swarm Optimization (PSO) and Differential Evolution (DE) are developed separately, and, to enhance the performances further, both of them are hybridized with a local search optimizer called Active Set Algorithm (ASA). Initially, the performance of PSO, DE, PSO hybridized with ASA, and DE hybridized with ASA are compared with each other and then with some traditional techniques available in literature using root mean square error (RMSE) as figure of merit.

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Minimum Variance Distortionless Response Beamformer with Enhanced Nulling Level Control via Dynamic Mutated Artificial Immune System

Cited by 29 publications

References 20 publications

Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

Modifying MVDR Beamformer for Reducing Direction-of-Arrival Estimation Mismatch

Multiple Target Localization with Bistatic Radar Using Heuristic Computational Intelligence Techniques

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