Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks
نویسندگان
چکیده
منابع مشابه
Detection of Direct Sequence Spread Spectrum Signals
Since early experimentation in the late 1800’s, wireless communication has become increasingly important and has been widely adopted by civilian and military markets worldwide. The proliferation of wireless communication systems presents new challenges, threats and opportunities for society and government institutions. Although the possibility of infringing privacy laws exist, electronic survei...
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Direct sequence spread spectrum transmissions (DSSS) are now widely used for secure communications, as well as for multiple access. Since the transmission uses a large bandwidth, the power spectral density of a DS-SS signal can be below the noise level. Hence, such a signal is difficult to detect. In this paper, we propose a method which is able to detect a spread spectrum signal hidden in the ...
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Applying the particle filter (PF) technique, this paper proposes a PF-based algorithm to blindly demodulate the chaotic direct sequence spread spectrum (CDS-SS) signals under the colored or non-Gaussian noises condition. To implement this algorithm, the PFs are modified by (i) the colored or non-Gaussian noises are formulated by autoregressive moving average (ARMA) models, and then the paramete...
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Secure transmission of information over hostile wireless environments is desired by both military and civilian parties. Direct-sequence spread spectrum (DS-SS) is such a covert technique resistant to interference, interception and multipath fading. Identifying spread-spectrum signals or cracking DS-SS systems by an unintended receiver (or eavesdropper) without a priori knowledge is a challengin...
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ژورنال
عنوان ژورنال: Sensors
سال: 2021
ISSN: 1424-8220
DOI: 10.3390/s21072538