摘要
提出了一种混沌背景下的编码信号检测新方法。信号检测过程包含两个步骤:混沌信号的预测和检测判决。该方法利用非线性前馈神经网络进行混沌信号模型的创建,并采用13位巴克码作为编码信号。仿真结果表明,通过该方法进行编码信号检测可以得到较高的检测概率和较低的虚警概率,整体检测性能较高,并且对于不同信噪比的信号具有较强的鲁棒性。
The process of signal detection consists of two stages, preliminary detection of chaotic signals and detection decision making. In this way, models of chaotic signals were created in the form of non-linear feedforward neural networks, and 13-element Barker code was used as the coded signals. The experiment results show that the detection of coded signals by using this method has higher detection probability, lower false alarm probability and good performance of the whole detection. This method turns out to be very robust to signals with different SNR.
出处
《海军工程大学学报》
CAS
北大核心
2008年第2期96-100,104,共6页
Journal of Naval University of Engineering
关键词
信号分类
信号检测
神经网络
混沌建模
signal classification
signal detection
neural network
chaotic modeling
