An Introduction to Convex Optimization Theory in Communication Signals Recognition
Jin-Feng Pang, Yun Lin, Xiao-Chun Xu, Zheng Dou, Zi-Cheng Wang
(College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China)
Abstract:
In this paper, convex optimization theory is introduced into the recognition of communication signals. The detailed content contains three parts. The first part gives a survey of basic concepts, main technology and recognition model of convex optimization theory. Special emphasis is placed on how to set up the new recognition model of communication signals with multisensor reports. The second part gives the solution method of the recognition model, which is called Logarithmic Penalty Barrier Function. The last part gives several numeric simulations, in contrast to D-S evidence inference method, this new method can also generate reasonable recognition results. Moreover, this new method can deal with the form of sensor reports which is more general than that allowed by the D-S evidence inference method, and it has much lower computation complexity than that of D-S evidence inference method. In addition, this new method has better recognition result, stronger anti-interference and robustness. Therefore, the convex optimization methods can be widely used in the recognition of communication signals.
Key words: convex optimization theory signal recognition D-S evidence theory logarithmic penalty barrier function
DOI：10.11916/j.issn.1005-9113.2013.05.003
Clc Number:TN971
Fund:
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An Introduction to Convex Optimization Theory in Communication Signals Recognition
本站小编 哈尔滨工业大学/2019-10-24
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