Abstract
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.
Highlights
Satellite navigation and positioning systems are widely used
For the antenna with the half-power beamwidth (HPBW) of 10◦, the antenna squint angle of 10◦, and the signal arrival direction of 10◦, the root mean square error (RMSE) of the proposed NN-amplitude comparison-based monopulse (ACM) algorithm is reduced by 70.86% and 69.89% in comparison with the traditional ACM and the third-order Taylor expansion ACM, respectively
For the amplitude comparison-based direction-finding systems in the positioning and navigation field, an NEURAL NETWORK-OPTIMIZED AMPLITUDE COMPARISON-BASED MONOPULSE (NN-ACM) angle estimation algorithm is proposed in this investigation
Summary
Satellite navigation and positioning systems are widely used. But in locations such as mines, tunnels, and high-rise ‘‘urban canyons’’ in valleys, satellite signals are blocked; they cannot receive and provide accurate positioning services. For the problem of nonlinear errors in the estimation process, we established a neural network model to compensate for the initial estimation results This improves the accuracy of the angle estimation results, it enhances the limitation of the original algorithm on the measurement range, and it improves the robustness of the algorithm. The contributions of this investigation are as follows: 1) This study is focused on the shortcomings of the traditional ACM DOA algorithm’s active angle measurement interval This investigation addresses the limitation of the angle measurement range; a method based on a neural network model is proposed for compensation. The neural network model is used to compensate for the error, and the final DOA estimation result is obtained, which increases the angle measurement performance.
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