The design of the waveform covariance matrix for beampattern matching in colocated multiple-input multiple-output (MIMO) radars represents a challenging problem because of its large number of variables and the presenc...
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The design of the waveform covariance matrix for beampattern matching in colocated multiple-input multiple-output (MIMO) radars represents a challenging problem because of its large number of variables and the presence of multiple constraints. The solutions available in the technical literature are computationally intensive and usually rely on iterative procedures that minimize a constrained mean square error (MSE). In this manuscript, a new computationally efficient method for beampattern matching design is proposed. This method, called sequential weight-shift unconstrained programming (SWSUP), allows to compute the covariance matrix of the probing signals achieving a desired beampattern at the transmit side of a colocated MIMO radar. Its derivation is based on the idea of reformulating the beampattern matching problem in an unconstrained form that can be tackled by breaking it into two subproblems. The first subproblem admits a closed-form solution, whose accuracy, in terms of MSE, is comparable to provided by other known methods. The solution of the second subproblem, instead, is evaluated through an iterative procedure and allows to achieve further improvement. Our numerical results evidence that the SWSUP method achieves precise beampattern matching with a substantially lower computational effort and computing time with respect to various existing alternatives.
Achieving high code coverage is essential in testing, which gives us confidence in code quality. Testing floating-point code usually requires painstaking efforts in handling floating-point constraints, e.g., in symbol...
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ISBN:
(纸本)9781450349888
Achieving high code coverage is essential in testing, which gives us confidence in code quality. Testing floating-point code usually requires painstaking efforts in handling floating-point constraints, e.g., in symbolic execution. This paper turns the challenge of testing floating-point code into the opportunity of applying unconstrained programming-the mathematical solution for calculating function minimum points over the entire search space. Our core insight is to derive a representing function from the floating-point program, any of whose minimum points is a test input guaranteed to exercise a new branch of the tested program. This guarantee allows us to achieve high coverage of the floating-point program by repeatedly minimizing the representing function. We have realized this approach in a tool called CoverMe and conducted an extensive evaluation of it on Sun's C math library. Our evaluation results show that CoverMe achieves, on average, 90.8% branch coverage in 6.9 seconds, drastically outperforming our compared tools: (1) Random testing, (2) AFL, a highly optimized, robust fuzzer released by Google, and (3) Austin, a state-of-the-art coverage-based testing tool designed to support floating-point code.
In this paper, we examine the sensitivity of trust-region algorithms on the parameters related to the step acceptance and update of the trust region. We show, in the context of unconstrained programming, that the nume...
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The consideration of the mechanical model of mathematical programming will be helping for discussing unconstrained programming and their *** this paper,we start off with the particles system of conservative mechamics,...
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The consideration of the mechanical model of mathematical programming will be helping for discussing unconstrained programming and their *** this paper,we start off with the particles system of conservative mechamics,so that schemes of many known algorithms are unified by using the artificially release energy(ARE),and pointed out that imply some new algorithms of computing efficiency better.
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