04057ntm a2200337 i 4500001001400000003000700014005001700021008004100038020002200079040002300101100003700124245009800161246006400259264003400323264001200357300007100369336002100440336002100461337002500482337002300507338002300530338003000553347002400583500005400607502007000661504004000731520282400771610006903595650004403664650001103708vtls000103164KUKTEM20251117113358.0190514s2017    my a   f am   000 0 eng d  aTHE0005189(Local)  aUMPbengcUMPerda0 aKelvin Lazarus Lazarus,eauthor.10aAdaptive beamforming algorithm based on Simulated Kalman Filter /cKelvin Lazarus A/L Lazarus04aSimulated Kalman Filter algorithms for adaptive beamforming 1aKuantan, Pahang :bUMP,c2017 4c© 2017  axiii, 91 pages :billustrations (some color) ;c30 cm. +e1 CD-ROM  atext2rdacontent  atext2rdacontent  aunmediated2rdamedia  acomputer2rdamedia  avolume2rdacarrier  acomputer disc2rdacarrier  atext filebPDF2rda  aFaculty of Electrical and Electronics Engineering  aThesis (Master of Science) -- Universiti Malaysia Pahang – 2017  aIncludes bibliographical references3 aAdaptive beamforming is a technique used to steer the radiation pattern towards the desired signal and cancel out any interference signal by finding the appropriate weights for every element in an array antenna, to achieve maximum signal to interference plus noise ratio (SINR). There are many methods to perform adaptive beamforming and one of the method is to use metaheuristic algorithm, to estimate the weights for individual elements in an array. Over the years, various metaheuristic algorithms have been applied to adaptive beamforming. Some of the metaheuristic algorithms have been modified from the original algorithms to improve the algorithms performance in adaptive beamforming application. A new metaheuristic algorithm named Simulated Kalman Filter (SKF), is inspired by the estimation capabilities of Kalman filter, has not been applied to adaptive beamforming application. Therefore, this research presents the first-time application of SKF algorithm to adaptive beamforming. The SKF algorithm, however, often converge prematurely at local optimum due to lack of exploration, preventing it from finding better solution. A modified version of the SKF algorithm, named Opposition-Based SKF (OBSKF), introduced by K. Zakwan, applies Opposition-Based Learning method to improve the exploration capabilities of SKF algorithm. Moreover, a new modified version of the SKF algorithm named SKF with Modified Measurement (SKFMM) is introduced to further improve the exploration capabilities of SKF algorithm by modifying the measurement-update equation. The SKF, OBSKF and SKFMM is applied to an array antenna with 10 elements arranged linearly with 0.5 𝜆 distance between elements. The desired signal angle is set 30° and the interference signal angle is set to −70°,−40°,−30°,−10°,0°,10°,50°,70°. The experiment is repeated for 100 times for various signal to noise ratio (SNR) values to obtain statistical results for best, worst, mean and standard deviation of the signal to interference plus noise ratio (SINR). The results obtained using SKF, OBSKF and SKFMM is compared to previously published work, Adaptive Mutated Boolean Particle Swarm Optimization (AMBPSO). The results show that all three SKF algorithms can produce higher mean SINR and lower standard deviation values compared to AMBPSO. The high mean SINR and low standard deviation value proves that the SKF algorithms are accurate and consistent in finding better solution. All the SKF algorithms produces consistency above 70% compared to existing AMBPSO for adaptive beamforming. Among the three SKF algorithms, the SKFMM produces the highest mean SINR values and is also the most consistent. The SKFMM is more consistent than the SKF algorithm by 25.20% and the SKFMM is more consistent than the OBSKF algorithm by 17.50%.20aFaculty of Electrical and Electronics EngineeringxDissertations 0aUniversities and collegesxDisertations 0aTheses