Applying correlation method to the problem of passive amplitude monopulse direction finding

Loginov, A. A.; Semenova, M. Yu.

doi:10.1109/iccsn.2011.6014850

Search citation statements

Order By: Relevance

Paper Sections

Select...

Related Work1

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several efforts have been made at the processing level to reduce DOA errors. In [12] correlation has been applied to passive amplitude direction finding. This algorithm was further improved by Guo and Li [13].…”

Section: Related Workmentioning

confidence: 99%

Accuracy improvement in amplitude comparison‐based passive direction finding systems by adaptive squint selection

Iqbal¹,

Khalid

Zahid

et al. 2020

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Accuracy improvement in amplitude comparison‐based passive direction finding systems by adaptive squint selection

Iqbal¹,

Khalid

Zahid

et al. 2020

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

“…Priyanka [14] proposed a classifier-based amplitude comparison direction-finding (ACDF) algorithm, which uses a fuzzy c-means algorithm and interpolation techniques to enhance the DOA's performance accuracy. In [15], a passive angle estimation using the monopulse correlation method is proposed. Guo and Li [16] further improved the algorithm and solved the direction-finding problem in two steps.…”

Section: Introductionmentioning

confidence: 99%

Improving Accuracy of an Amplitude Comparison-Based Direction-Finding System by Neural Network Optimization

et al. 2020

View full text Add to dashboard Cite

In the positioning and navigation field, it is essential to use the direction-finding system to obtain the signal direction of arrival (DOA) and target position. The amplitude comparison-based monopulse (ACM) DOA algorithm performs a few calculations, has a simple system structure, and is widely used. The traditional ACM DOA algorithm uses the first-order Taylor expansion to introduce the nonlinear errors, and the angle measurement range is limited. In response to this problem, this study establishes a neural network model for error compensation, and it optimizes the traditional algorithm to obtain a better angle estimation performance. In order to perform an experiment with the proposed algorithm, a novel experimental device was designed. Two measurements at different angles were obtained by rotating the antenna. The ACM angle estimation used only one directional antenna. The results verified the optimization algorithm. The experimental results demonstrated that in comparison with the traditional first-order and the improved third-order Taylor expansion ACM DOA algorithm, the mean absolute error of this method reduced by 81.62% and 72.62%, respectively. INDEX TERMS Positioning, direction-finding, amplitude comparison, neural network, signal direction of arrival estimation.

show abstract

Applying correlation method to the problem of passive amplitude monopulse direction finding

Cited by 2 publications

References 3 publications

Accuracy improvement in amplitude comparison‐based passive direction finding systems by adaptive squint selection

Accuracy improvement in amplitude comparison‐based passive direction finding systems by adaptive squint selection

Improving Accuracy of an Amplitude Comparison-Based Direction-Finding System by Neural Network Optimization

Contact Info

Product

Resources

About