Academic
Publications
Bayesian Compressive Sampling for Pattern Synthesis With Maximally Sparse Non-Uniform Linear Arrays

Bayesian Compressive Sampling for Pattern Synthesis With Maximally Sparse Non-Uniform Linear Arrays,10.1109/TAP.2010.2096400,IEEE Transactions on Ante

Bayesian Compressive Sampling for Pattern Synthesis With Maximally Sparse Non-Uniform Linear Arrays   (Citations: 6)
BibTex | RIS | RefWorks Download
A numerically-efficient technique based on the Bayesian compressive sampling for the design of maxi- mally-sparse linear arrays is introduced. The method is based on a probabilistic formulation of the array synthesis and it exploits a fast relevance vector machine for the problem solution. The proposed approach allows the design of linear arrangements fitting desired power patterns with a reduced number of non-uni- formly spaced active elements. The numerical validation assesses the effectiveness and computational efficiency of the proposed approach as a suitable complement to existing state-of-the-art techniques for the design of sparse arrays. Index Terms—Array synthesis, Bayesian compressive sampling (BCS), linear arrays, relevance vector machine, sparse arrays.
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
    • ...While the first problem has been widely studied [2][3][7]-[14], only few techniques have been introduced for the solution of the latter [16]...
    • ...In this framework, numerically inexpensive approaches, such as the steepest descent method, the iterative least-square technique, the simplex search, and the linear programming, were among the first methodologies applied to sparse array design [15][16]...
    • ...However, these techniques exhibit some drawbacks in terms of flexibility, required a-priori information, and final obtained performances [16]...
    • ...An innovative approach for the synthesis of sparse arrays with prescribed pattern features has been recently proposed [16]...
    • ...This methodology is based on the formulation of the sparse array synthesis problem as a “Compressive Sensing (CS) retrieval” one, in which the sparseness constraints are imposed on the final array layout [16]...
    • ...Thanks to this approach, BCS sparse array synthesis has proved to be effective in dealing with standard and reference sparse array synthesis problems [16]...
    • ...The problem of finding the sparsest (real and symmetric [16]) linear array with desired radiating properties can be cast in terms of a pattern matching one as follows [16]:...
    • ...The problem of finding the sparsest (real and symmetric [16]) linear array with desired radiating properties can be cast in terms of a pattern matching one as follows [16]:...
    • ...is the vector of the samples of the sparse array radiation pattern, λ is the wavelength, uk (k=1,..,K) are the matching angles, dn (n=0,…,N) are the allowed positions for the sparse array element, and χn is the Neumann’s number [3][16]...
    • ...By modeling the radiation pattern as a Gaussian random variable [16], the above synthesis problem can be recasted in the framework of BCS to obtain the following equivalent one [16]: [ ]...
    • ...By modeling the radiation pattern as a Gaussian random variable [16], the above synthesis problem can be recasted in the framework of BCS to obtain the following equivalent one [16]: [ ]...
    • ...[18] and the estimated fidelity variance [16]...
    • ...Following the RVM approach [17][18], this BCS problem is then solved by the following procedure [16]: 1. Input phase: define the reference pattern samples EREF, the set of admissible element locations dn (n=0,…,N), and the initial estimate of the fidelity variance; 2. Matrix Definition: calculate the problem matrix Φ, with Φ(k,n)=χn cos(2πdnuk/λ); 3. Hyperparameter Posterior Modes Estimation: find a and σ 2 according to the RVM procedure ...
    • ... the RVM approach [17][18], this BCS problem is then solved by the following procedure [16]: 1. Input phase: define the reference pattern samples EREF, the set of admissible element locations dn (n=0,…,N), and the initial estimate of the fidelity variance; 2. Matrix Definition: calculate the problem matrix Φ, with Φ(k,n)=χn cos(2πdnuk/λ); 3. Hyperparameter Posterior Modes Estimation: find a and σ 2 according to the RVM procedure [16];...

    Giacomo Oliveriet al. Synthesis of large sparse linear arrays by Bayesian Compressive Sensin...

    • ...<{[SECTION]}>(a BCS ,σ 2� BCS ) has been determined [45], [52]...

    Giacomo Oliveriet al. A Bayesian-Compressive-Sampling-Based Inversion for Imaging Sparse Sca...

    • ...In this work, two innovative design techniques are proposed, namely the CPM [4] and the BCS [5], aimed at synthesizing simple array architectures with high BCEs and low PSLs...
    • ...Towards this end, a matching either on the weights [4] or on the pattern [5] of known DPSS sequences [3] is performed...
    • ...The synthesis of sparse linear arrays characterized by the minimum number of radiating elements with real and symmetric excitations in order to obtain a pattern with the desired properties has been presented in [5]...
    • ...found according to the procedure described in [5]; (iv) Array weights estimation - The optimal sparse weights are computed as...

    Paolo Roccaet al. Innovative array designs for wireless power transmission

    • ...Such computational solutions, together with the design non-regular [16] as well as multibeam [17] WPT arrays are currently under investigation...

    Giacomo Oliveriet al. Array antenna architectures for solar power satellites and wireless po...

    • ...Example are sparse array synthesis [10], [12], and wireless sensor networks [6]...

    Marco Donald Miglioreet al. Compressed sensing in electromagnetics: Theory, applications and persp...

Sort by: