【摘要】：在混沌預(yù)測模型基礎(chǔ)上,提出了消散型同步的混沌背景下微弱信號檢測算法。采用徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)(RBFNN)擬合混沌模型,結(jié)合消散型同步實(shí)現(xiàn)混沌時(shí)間序列與混沌系統(tǒng)的同步,利用同步誤差實(shí)現(xiàn)微弱信號的檢測。以Rossler混沌系統(tǒng)為研究對象,驗(yàn)證了算法的可行性,研究了噪聲對微弱信號檢測的影響。仿真研究表明,該算法能檢測各種頻率的微弱信號,在一定條件下可檢測到信雜比大于-110dB的微弱周期信號;若信噪比SNR≥0dB,噪聲對微弱信號檢測的影響很小;但若SNR-10dB,將檢測不出微弱信號。在理論研究基礎(chǔ)上,由MKS-CEC-Ⅲ新型混沌演化控制實(shí)驗(yàn)儀獲取Coullet混沌時(shí)間序列,添加不同頻率的微弱信號,利用該算法實(shí)現(xiàn)了不同頻率微弱信號的檢測,說明該算法適用于其他混沌系統(tǒng)。
[Abstract]:Based on the chaotic prediction model, a weak signal detection algorithm based on dissipative synchronization is proposed. The chaotic model is fitted by radial basis function neural network (RBFNN). The synchronization of chaotic time series and chaotic system is realized with dissipative synchronization. The weak signal is detected by synchronization error. Taking Rossler chaotic system as the research object, the feasibility of the algorithm is verified, and the influence of noise on weak signal detection is studied. Simulation results show that the algorithm can detect weak signals at various frequencies, and detect weak periodic signals with signal-to-clutter ratio greater than-110dB under certain conditions, and if SNR 鈮，

本文編號：2370668