摘要
在超宽带冲激无线电信号检测中,针对FHN神经元模型随机共振中检测信噪比受限的问题,研究分析了小波包理论及传统单阈值小波包的缺点,结合新的分段阈值小波包去噪算法,提出一种小波包与FHN神经元模型随机共振联合检测的新方法,并对所提算法的性能进行仿真验证。仿真实验表明,新方法克服了FHN模型的信噪比门限,降低了FHN模型的检测信噪比,改善了FHN模型的检测性能,可有效恢复强噪声背景下的超宽带冲激无线电信号波形。
In UWB-IR signal detection,FHN model detection method performance is limited by the minimum detectable signal to noise ratio（SNR）. From this point,wavelet packet is introduced into FHN model. In addition,the shortcoming of traditional single threshold wavelet packet is analyzed,combined with the new piecewise threshold wavelet packet,a novel UWB-IR signal detection method based on wavelet packet and FHN model is proposed to detect UWB-IR signal. Furthermore,the performance of the proposed algorithm is simulated and analyzed. Simulation results shows that the proposed algorithm reduce the detectable SNR and improve the detection performance of FHN model detection method. Therefore,the UWB-IR signal can be detected effectively under strong noise.
出处
《信号处理》
CSCD
北大核心
2017年第10期1324-1331,共8页
Journal of Signal Processing
基金
国家自然科学基金(61471386)
