KEYWORDS: Feature extraction, Signal to noise ratio, Spectral density, Detection and tracking algorithms, Signal processing, Pattern recognition, Deep learning, Time-frequency analysis, Modulation, Interference (communication)
Direct sequence spread spectrum (DSSS) signals, due to their complex signal structure, pose challenges for accurate modulation mode recognition and parameter estimation using traditional methods. This paper proposes a deep learning-based DSSS signal mode recognition and parameter estimation algorithm. It employs cyclic spectrum analysis to estimate the carrier frequency and code rate parameters, and combines the feature extraction capabilities of a convolutional neural network (CNN) with the modeling and recognition strengths of a long short-term memory (LSTM) network to achieve high-precision DSSS signal recognition. Experimental results demonstrate that the cyclic spectrum method can accurately estimate the carrier frequency and code rate parameters, and the CNN+LSTM mode recognition achieves an accuracy rate of over 85% even at a signal-to-noise ratio below 0 dB, validating the effectiveness of the proposed algorithm for DSSS signal mode recognition.
The mainstrean digital single lens reflex (DSLR) image has the characteristics of true color and high quality, this paper proposes apply DSLR to probe spacecraft in order to obtain better quality Color images. Firstly, the performance parameters of mainstream DSLR and industrial-grade optical detector are analysed and compared detailedly; Secondly, the performance and positioning ways etc. of optical detector and DSLR system integrated special telephoto lens are analysed and compared. Furthermore, some experiments have been done in different conditions. The experiments indicate that the performances of DSLR and optical detector are similar. In addition, DSLR has the advantage of small size, low cost and Easy positioning, which can be used to obtain the scene of spacecraft in the takeoff phase and part of reentry phase.
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