Vandermonde decomposition-based coprime array direction of arrival estimation method
A direction of arrival estimation and array technology, applied in the field of signal processing, can solve problems that cannot be used to represent signal power, growth, and precision-limited grid density
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
example 1
[0095]Simulation example 1: The method for estimating DOA of coprime arrays based on Vandermonde decomposition proposed by the present invention will be compared with the existing method for estimating DOA of coprime arrays based on signal sparse reconstruction, and the method of coprime array DOA estimation based on multiple signal subspace classification The DOA estimation method and the coprime array DOA estimation method based on virtual array interpolation are compared in spatial spectrograms to illustrate the advantages of the method of the present invention in terms of power spectrum characteristics and power estimation. Since the number of incident signal sources is greater than the number of antenna elements in the simulation conditions, the degree of freedom of the uniform linear array method is limited and effective DOA estimation cannot be achieved, so no comparison is made here. Assume that the number of incident narrow-band signals is 15, and the incident directio...
example 2
[0096] Simulation example 2: The method for estimating direction of arrival of coprime arrays based on Vandermonde decomposition proposed by the present invention will be compared with the existing method for estimating direction of arrival of coprime arrays based on signal sparse reconstruction, and the method of coprime arrays based on multi-signal subspace classification The root mean square error is compared between the DOA estimation method and the coprime array DOA estimation method based on virtual array interpolation. The incident signal direction in each Monte Carlo experiment is generated by a random standard normal distribution, and the root mean square error (RMSE) is calculated as
[0097]
[0098] in, and θ q Indicates the estimated and true values of DOA in each Monte Carlo experiment. For each set of parameter values, the number of Monte Carlo experiments is Q=500. Figure 6 It is the relationship curve diagram between root mean square error and SNR, a...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com