The Bayesian inference framework for design introduced in Chan and Goggans ["Using Bayesian inference for linear antenna array design," IEEE Trans. Antennas Propag., vol. 59, no. 9, pp. 3211-3217, Sep. 2011] is applied to design linear antenna arrays capable of realizing multiple radiation patterns while satisfying various design requirements. Many design issues are involved when designing a linear antenna array. This paper focuses on four practical design issues: the need for minimum spacing between two adjacent array elements, limitations in the dynamic range and accuracy of the current amplitudes and phases, the ability to produce multiple desired radiation patterns using a single array, and the ability to maintain a desired radiation pattern over a certain frequency band. We present an implementation of these practical design requirements based on the Bayesian inference framework, together with representative examples. Our results demonstrate the capability and robustness of the Bayesian method in incorporating real-world design requirements into the design of linear antenna arrays.Index Terms-Automated multiobjective design, Bayesian data fusion, inference-based design, linear antenna array.
Markov chain Monte Carlo (MCMC) methods are widely used in the solution of parameter estimation problems arising in acoustics and other applications. The use of MCMC to estimate the parameters of a single model is well established. However, in many applications, there is not a single model for the data but rather a number of competing models. A common method of dealing with multiple models is to use MCMC to compute the posterior probability and estimate the parameter values of each model in turn. However, for problems with many models, it is more efficient to combine the parameter spaces of all models into a single space and use MCMC to perform across-model sampling of the joint space. Although the development of an MCMC algorithm of this sort is sufficiently difficult so as to be unprofitable for the non-specialist, the acoustician wishing to solve their multi model parameter estimation problem using MCMC can still do so using an existing algorithm. This presentation gives an overview and brief tutorial of MCMC for parameter estimation and then discusses and gives an example of using the open source computer program BayeSys [Skilling, 2004] to determine the model order of a simple atomic model.
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