Introduction Genetic Algorithms (CA) have proven to be a useful method of optimization for difficult and discontinuous multidimensional engineering problem. A new method of optimization, Particle Swarm Optimization (PSO), is able to accomplish the same goal as GA optimization in a new and faster way [l]. The purpose of this paper is to investigate: the foundations and performance of the two algorithms when qplied to the design of a profiled corrugated horn antcnna. Also investigated is the possibility of hybridizing the two algorithms.GA Background GA is an evolutionary optimizer (EO) that takes a sample of possible solutions (individuals) and employs mutation, crossover, and selection as the primary operator!: for optimization [2]. For the case of a profiled corrugated horn antenna, there are five parameters being optimized for the design of a profiled corrugated horn antenna. The optimization parameters are the '3'' paramcter relating to the length, the number of corrugations per wavelength, the ratio of tooth width to total corrugation width, profile parameter, and matching section parameter.The fitness function takes the values of each parameter and returns a single number representing how good the solution is. For this example the design parameters are fed into a hom simulation program that creates a horn cross section and simulates the far field pattern. A representative fitness function is definedas:The BWtaTg, is the desired beamwidth of the horn. In this case 34' is chosen because that was the desired outcome for a particular project. Weight is the wight of the horn, and Xpol is the peak crosspolarization within a reflector subtended angle of +36 '. Additional constraints are made to discourage unrealistic dcsigns. Any design with a tooth thickness of lcss than I mm is given a fitness value of -100 bccause 1 mm is the thinnest tooth that can be effectively manufactured. If the value of SI I was lcss than -30 dB, the fitness is evaluated using -30 as the value of S I I.PSO Background PSO is in principle a much simpler algorithm. It operates on the principle that each solution can be rcprcsentcd as a particle (agcnt) in a swarm. Each agcnt has a position and velocity vector. Each position coordinate represents a paramctcr value. Thus for an n-dimensional optimization, each agcnt will have a position in n-dimensional space that reprcscnts a solution [3]. For this case the position corresponds to thc horn design parainetcrs. Figurc la shows a flow chart of the PSO algorithm.Like GA, PSO must also have a fitncss evaluation function that takes thc agent's positiori and assigns to it a fitness value. For consistency the fitncss function IS the same as for CA. Thc position with the highest fitness value in the entire run is called the global bcst (ghcrt). Each :agent also keeps track of its highest fitness value. The location of this value is called its personal bcst (pbcit). Each agent is initialized with a random position and random velocity. The velocity in each of n dimensions is accelerated toward the globa...

Recent investigations have shown that it is quite possible to accurately characterize the surface profile of large reflectors using microwave holographic techniques. In these techniques the complex (amplitude and phase) far‐field pattern of the antenna is measured first. The surface profile is then constructed using the Fourier transform relationship which exists between the far field and a function related to the induced surface current. In this paper the concept of the Fourier transform relationship is first investigated to demonstrate that it is, in general, a summation of many Fourier transforms. However, for large reflectors with small beam widths, only the first term of the series has the major contribution. Furthermore, an iterative scheme is described to analytically/numerically continue the far‐field pattern outside the measurement window. This, then, results in an improved resolution of the surface map data and, in particular, reduces the amplitude artifacts outside the boundary of the reflector. A novel and efficient simulation model has been developed to properly evaluate the accuracy of the technique in recovering the simulated surface errors. Finally, results of a recent measurement are summarized.

No abstract

No abstract

The performance of large reflector antennas can be improved by identifying the location and amount of their surface distortions and then by correcting them. Microwave holography techniqnes are finding considerable applications as viable tools for performing this task.In these techniques, the complex (amplitude and phase) far-field pattern of the antenna is measured, using a reference antenna. Then, the Fourier transform relationship, which exists between the far field and a function related to the induced current, is invoked to result in the identification of the surface distortions.To critically examine the accuracy of the constructed surface profiles, simulation studies are required to incorporate both the effects of systematic and random distortions, particularly the effects of the displaced surface panels.In this paper, different simulation models are investigated with emphasis given to a model based on the vector diffraction analysis of a curved refleetor with displaced panels. The simulated far-field patterns are then used to reconstruct the location and amount of displacement of the surface panels by employing a fast Fourier transform (FFT)/iterative procedure. The sensitivity of the microwave holography technique based on the number of far-field sampled points, level of distortions, polarizations, illumination tapers, etc., is also examined. In addition, the relationships between Az-El and uu spaces are addressed in the Appendix. Most of the data are tailored to the dimensions of the NASAIJPL Deep Space Network (DSN) 64-m reflector antennas for which the result of a recent measurement is also presented.