CRPSOWM for Linear Antenna Arrays with Improved SLL and Directivity Gopi Ram 1 , Durbadal Mandal 1 , Rajib Kar 1 and Sakti Prasad Ghoshal 2 1 Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, India, 2 Department of Electrical Engineering, National Institute of Technology, Durgapur, India ABSTRACT In this paper, swarm-based algorithms craziness particle swarm optimization with wavelet mutation (CRPSOWM) have been applied for the optimal design of hyper beamforming of linear antenna array. The conventional gradient based optimization techniques are not efficient and get trapped on local optima for such multimodal highly non-linear con- strained hyper beamforming optimization problem. Thus, the above evolutionary technique has been adopted. CRPSOWM incorporates a new definition of swarm updating with the help of wavelet theory. Wavelet mutation enhan- ces the CRPSO to explore the solution space more effectively compared to the other optimization methods as reported in firefly algorithm (FFA). Thus CRPSOWM is apparently free from getting trapped at local optima and premature con- vergence. The simulation results show CRPSOWM outperforms FFA by achieving much greater reduction in side lobe level, improved directivity and much more improved first null beam width keeping the same value of hyper beam exponent. The optimized hyper beam is achieved by optimization of current excitation weights and uniform inter-ele- ment spacing. The approach is illustrated through 10-, 14-, and 20-element linear antenna arrays. Keywords: Hyper beam, linear antenna arrays, CRPSO, CRPSOWM, SLL, FNBW. 1. INTRODUCTION Beamforming is a signal processing technique used to control the directionality of the transmission and recep- tion of the radio signals [1]. This is achieved by distrib- uting the elements of the array in such a way that signals at particular angles experience constructive interference while others experience destructive inter- ference. Beamforming can be used at both transmitting and receiving ends in order to achieve spatial selectiv- ity. Hyper beamforming refers to spatial processing algorithm used to focus an array of spatially distributed elements (called sensors) to increase the signal to inter- ference plus noise ratio at the receiver. This beamform- ing processing improves significantly the gain of the wireless link over a conventional technology, thereby increasing range, rate, and penetration [24]. It has found numerous applications in radar, sonar, seismol- ogy, wireless communication, radio astronomy, acous- tics, and biomedicine [5]. It is generally classified as either conventional (switched and fixed) beamforming or adaptive beamforming. Switched beamforming sys- tem [6,7] is a system that can choose one pattern from many predefined patterns in order to enhance the received signals. Fixed beamforming uses fixed set of weights and time delays (or phasing) to combine the signals received from the sensors in the array, primarily using only information about the locations of the sen- sors in space and the wave direction of interest [8]. Adaptive beamforming or phased array is based on the desired signal maximization mode and interference sig- nal minimization mode [911]. It is able to place the desired signal at the maximum of main lobe. A new optimized hyper beamforming technique is presented in this paper, and CRPSOWM approach is applied to obtain optimal hyper beam patterns. Hyper beamform- ing/any other beamforming offers high detection performance like beam width, the target bearing esti- mation, and reduces false alarm, side lobe suppression. Different evolutionary optimization algorithms such as simulated annealing algorithms [12], genetic algorithm (GA) [1317], firefly algorithm (FFA) [18], and particle swarm optimization (PSO) [1921]. The limitations of these algorithms are that it may be influenced by premature convergence and stagnation problem. In order to overcome these problems, PSO algorithm has been modified as craziness PSO with wavelet muta- tion (CRPSOWM) [2226]. Based upon improved CRPSOWM with wavelet theory, this paper presents a good and comprehensive set of results, and states argu- ments for the superiority of the algorithm over FFA [18]. IETE JOURNAL OF RESEARCH | VOL 61 | NO 2 | MARAPR 2015 109