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A Non-Coherent Method of UWB Signal Acquisition Based on Genetic Algorithm

Qinyu Zhang

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Abstract

Focusing on ultra wideband(UWB) signal acquisition conditional on the uncertain signals to noise ratio(SNR),a non-coherent scheme is proposed in this article based on genetic algorithm,which combines online SNR estimation with genetic algorithm to search the parameters pair of integral length and timing alignment.Contrast with general schemes,the scheme proposed is suitable for the uncertain SNR conditions where integral length is unavailable and dynamic detection threshold is hard to be set.The results show that,compared with Look and Jump by K steps algorithm,the proposed scheme improved significantly the acquisition performances on uncertain SNRs.

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What this paper is about

Focusing on ultra wideband(UWB) signal acquisition conditional on the uncertain signals to noise ratio(SNR),a non-coherent scheme is proposed in this article based on genetic algorithm,which combines online SNR estimation with genetic algorithm to search the parameters pair of integral length and timing alignment.Contrast with general schemes,the scheme proposed is suitable for the uncertain SNR conditions where integral length is unavailable and dynamic detection threshold is hard to be set.The results show that,compared with Look and Jump by K steps algorithm,the proposed scheme improved significantly the acquisition performances on uncertain SNRs.

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Available abstract

Focusing on ultra wideband(UWB) signal acquisition conditional on the uncertain signals to noise ratio(SNR),a non-coherent scheme is proposed in this article based on genetic algorithm,which combines online SNR estimation with genetic algorithm to search the parameters pair of integral length and timing alignment.Contrast with general schemes,the scheme proposed is suitable for the uncertain SNR conditions where integral length is unavailable and dynamic detection threshold is hard to be set.The results show that,compared with Look and Jump by K steps algorithm,the proposed scheme improved significantly the acquisition performances on uncertain SNRs.

Key concepts: Algorithm, Scheme (mathematics), Genetic algorithm, Jump, Set (abstract data type), SIGNAL (programming language), Computer science, Signal-to-noise ratio (imaging)

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