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Weak signal detection based on chaotic oscillator

Ding Liu, Hai‐Peng Ren, Li Song, Huming Li

Open publisher page 18 citations

Abstract

The weak signal detection based on chaotic oscillator is analyzed, and the weaknesses of the existing Melnikov method and the phase trajectory observation method are pointed out in this paper. A new weak signal detection method based on Lyapunov exponents (LE) is proposed. Firstly, the model based LE calculation method is used to determine the accurate initial threshold value. Secondly, an improved LE calculation algorithm is proposed based on phase space reconstruction of the observed data. The characteristics of this improved method are the high precision of the threshold value setting and the automatic recognition. Simulation results verify analysis and the effectiveness of this method.

About this research paper

What this paper is about

The weak signal detection based on chaotic oscillator is analyzed, and the weaknesses of the existing Melnikov method and the phase trajectory observation method are pointed out in this paper. A new weak signal detection method based on Lyapunov exponents (LE) is proposed. Firstly, the model based LE calculation method is used to determine the accurate initial threshold value. Secondly, an improved LE calculation algorithm is proposed based on phase space reconstruction of the observed data. The characteristics of this improved method are the high precision of the threshold value setting and the automatic recognition. Simulation results verify analysis and the effectiveness of this method.

Why it matters

OpenAlex reports 18 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Method / approach

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Available abstract

The weak signal detection based on chaotic oscillator is analyzed, and the weaknesses of the existing Melnikov method and the phase trajectory observation method are pointed out in this paper. A new weak signal detection method based on Lyapunov exponents (LE) is proposed. Firstly, the model based LE calculation method is used to determine the accurate initial threshold value. Secondly, an improved LE calculation algorithm is proposed based on phase space reconstruction of the observed data. The characteristics of this improved method are the high precision of the threshold value setting and the automatic recognition. Simulation results verify analysis and the effectiveness of this method.

Key concepts: Lyapunov exponent, Chaotic, SIGNAL (programming language), Trajectory, Phase space, Threshold limit value, Computer science, Algorithm

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