Research on the Trend of UAV Communication Signal Indication System

To interact with the environment efficiently, the nervous system must generate expectations about redundant sensory signals and detect unexpected ones. Neural circuits can, for example, compare a prediction of the sensory signal that was generated by the nervous system with the incoming sensory input, to generate a response selective to novel stimuli. In the first-order electrosensory neurons of a gymnotiform electric fish, a negative image of low-frequency redundant communication signals is subtracted from the neural response via feedback, allowing unpredictable signals to be extracted. Here we show that the cancelling feedback not only suppresses the predictable signal but also actively enhances the response to the unpredictable communication signal. A transient mismatch between the predictive feedback and incoming sensory input causes both to be positive: the soma is suddenly depolarized by the unpredictable input, whereas the neuron's apical dendrites remain depolarized by the lagging cancelling feedback. The apical dendrites allow the backpropagation of somatic spikes. We show that backpropagation is enhanced when the dendrites are depolarized, causing the unpredictable excitatory input to evoke spike bursts. As a consequence, the feedback driven by a predictable low-frequency signal not only suppresses the response to a redundant stimulus but also induces a bursting response triggered by unpredictable communication signals.

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Communication Signal Pre-Processing in Impulsive Noise: A Bandpass Myriad Filtering-Based Method

IEEE Communications Letters ◽

10.1109/lcomm.2018.2832642 ◽

2018 ◽

Vol 22 (7) ◽

pp. 1402-1405 ◽

Cited By ~ 1

Author(s):

Guosheng Yang ◽

Jun Wang ◽

Guoyong Zhang ◽

Shaoqian Li

Keyword(s):

Impulsive Noise ◽

Communication Signal

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Research on Modulation Recognition Algorithm of Digital Communication Signal Based on Wavelet Denoising

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.608-609.459 ◽

2014 ◽

Vol 608-609 ◽

pp. 459-467 ◽

Cited By ~ 1

Author(s):

Xiao Yu Gu

Keyword(s):

Pattern Recognition ◽

Feature Extraction ◽

Decision Theory ◽

Recognition Rate ◽

Recognition Algorithm ◽

Modulation Recognition ◽

The Core ◽

Feature Parameters ◽

Communication Signal ◽

Modulation Signal

The paper researches a recognition algorithm of modulation signal and modulation modes. The modulation modes to be recognized include 2ASK, 2FSK, 2PSK, 4ASK, 4FSK and 4PSK modulation. There are two methods recognizing modulation modes of digital signal, method based on decision theory and pattern-recognition method based on feature extraction. The method based on decision theory is not suitable for recognition with multiple modulation modes. The core of pattern recognition based on feature extraction is selection of feature parameters. So the paper uses the feature parameters with simple calculation, easy to be implemented and high recognition rate as the core. The extraction of feature parameters is based on instant feature of modulation signal after Hilbert transformation.

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