Abstract
Aiming at the problem that BOC (Binary Offset Carrier) signal is difficult to be detected by traditional means under low SNR, this paper proposes a phased BOC signal detection algorithm based on cyclic stationary feature. Firstly, this paper introduces BOC signal model, proposes an improved cyclic stationary feature detection method, analyzes the difference of gray images under binary hypothesis, and utilizes this difference to extract features by putting the two types of images into the convolutional neural network, and then uses the trained network for detection. Furthermore, in order to detect signals more efficiently, energy detection is carried out at first. If no signal is detected, the improved cyclostationary detection is adopted in the second stage. Simulation results show that the detection performance of the proposed method is significantly better than that of the traditional cycliostationary detection and energy detection when SNR is less than −7db.
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Yang, H., Wang, X., Song, X. (2022). A Phased BOC Signal Detection Algotithm Based on Cyclic Stationary Characteristics. In: Yan, L., Duan, H., Yu, X. (eds) Advances in Guidance, Navigation and Control . Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-15-8155-7_10
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DOI: https://doi.org/10.1007/978-981-15-8155-7_10
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