Abstract
The design of the modern computing paradigm of heuristics is an innovative development for parameter estimation of direction of arrival (DOA) using sparse antenna arrays. In this study, the optimization strength of the flower pollination algorithm (FPA) is exploited for the DOA estimation in a low signal to noise ratio (SNR) regime by applying coprime sensor arrays (CSA). The enhanced degree of freedom (DOF) is achieved with FPA by investigating the global minima of highly nonlinear cost function with multiple local minimas. The sparse structure of CSA demonstrates that the DOF up to O(MN) is achieved by employing M+N CSA elements, where M and N are the numbers of antenna elements used to construct the CSA. Performance analysis is conducted for estimation accuracy, robustness against noise, robustness against snapshots, frequency distribution of root mean square error (RMSE), variability analysis of RMSE, cumulative distribution function (CDF) of RMSE over Monte Carlo runs and the comparative studies of particle swarm optimization (PSO). These reveal the worth of the proposed methodology for estimating DOA.
Highlights
Accurate estimation of target locations in array signal processing has been received a great acclaim in military and commercial applications like radar [1,2] sonar [3], seismology [4] and mobile communications [5,6]
direction of arrival (DOA) estimation can be categorized into two important aspects with respect to estimation:the first is distribution array structures and the second one is estimation algorithms
The array structure is divided into two major groups depending upon the distance between the antenna elements
Summary
Accurate estimation of target locations in array signal processing has been received a great acclaim in military and commercial applications like radar [1,2] sonar [3], seismology [4] and mobile communications [5,6]. DOA estimation can be categorized into two important aspects with respect to estimation:the first is distribution array structures and the second one is estimation algorithms. The array structure is divided into two major groups depending upon the distance between the antenna elements. To detect more targets using ULA, more antenna elements will be needed, which will increase the cost of hardware and computing complexity. This limitation in ULAs leads us to the world of sparse arrays
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
