Research On Maximum Likelihood Direction Finding Method Based On Quantum Intelligent Computing Algorithm

Direction finding is a crucial technique in the field of array signal processing.It can accurately estimate and locate incoming signal sources,making it widely used in radar,wireless communication,and aerospace industries.Due to its importance and versatility,direction finding algorithms have been extensively studied to improve their accuracy,reduce computational complexity,and maximize their practical application.Among the many highresolution direction finding methods developed so far,maximum likelihood(ML)methods can simultaneously estimate multiple signals with high accuracy,outperforming traditional methods.However,ML methods require complex multidimensional maximum searching,which limitstheir practical use.In this paper,we use quantum intelligent computing to efficiently solve the ML equation and design several efficient quantum intelligent computing algorithms for specific problems.We achieved ML direction finding based on quantum intelligent computing algorithms,which can be summarized as follows:1.To reduce the computational complexity of the ML direction finding method and obtain accurate results with limited data,we introduce a single snapshot ML direction finding method and design a quantum grey wolf algorithm for fast solving of the estimation equation.2.To deal with common impulse noise in practical engineering problems,we introduce a low-order ML direction finding method based on fractional lower-order statistics and design a quantum ant-lion algorithm for solving the ML equation of low-order moments,making it more practical and extensible to higher-dimensional cases.3.To obtain dynamic direction finding results,we introduce dynamic ML direction finding methods and zero-memory nonlinear processing methods,and design a quantum cat swarm algorithm to obtain more robust dynamic direction finding results in adverse noise environments.