Efficient utilization of limited radio frequency spectrum is only possible to use smart/adaptive antenna system. Smart antenna radiates not only narrow beam towards desired users exploiting signal processing capability but also places null towards interferers, thus optimizing the signal quality and enhancing capacity. Least mean square (LMS) and normalized least mean square (NLMS) are two adaptive beamforming algorithms which are presented in this paper. Smart antenna incorporates these algorithms in coded form which calculates complex weights according to the signal environment. The efficiency of LMS and NLMS algorithms is compared on the basis of normalized array factor and mean square error (MSE) for mobile communication. Simulation results reveal that both algorithms have high resolution for beam formation. However LMS has good performance to minimize MSE as compared to NLMS. Therefore, LMS is found more efficient algorithm to implement in the mobile communication environment to minimize MSE and enhancing capacity.
ABSTRACT:Rapid advancement in remote sensing open new avenues to explore the hyperspectral Hyperion imagery pre-processing techniques, analysis and application for land use mapping. The hyperspectral data consists of 242 bands out of which 196 calibrated/useful bands are available for hyperspectral applications. Atmospheric correction applied to the hyperspectral calibrated bands make the data more useful for its further processing/ application. Principal component (PC) analysis applied to the hyperspectral calibrated bands reduced the dimensionality of the data and it is found that 99% of the data is held in first 10 PCs. Feature extraction is one of the important application by using vegetation delineation and normalized difference vegetation index. The machine learning classifiers uses the technique to identify the pixels having significant difference in the spectral signature which is very useful for classification of an image. Supervised machine learning classifier technique has been used for classification of hyperspectral image which resulted in overall efficiency of 86.6703 and Kappa co-efficient of 0.7998.
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