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

Iqbal, Muhammad; Khalid, Zubair; Zahid, Muhammad; Abdullah, Asim

doi:10.1049/iet-rsn.2019.0465

Cited by 6 publications

(1 citation statement)

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“…For the traditional ACM algorithm, it provides an angle estimation result with an accuracy higher than the Rayleigh criterion in a smaller angle range near the bisector of the two boresights, but when the signal direction deviates from the bisector, the angle estimation deviation is larger. M. F. Iqbal et al [17] proposed a method to reduce the error of the amplitude comparison-based direction-finding system. For signals with a direction of arrival that deviates more from the bisector, they adaptively used adjacent or spare antennas to make the signal as close as possible to the bisector of the two boresights for the DOA measurement.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%