In wireless sensor networks, the signals received by sensors are usually complex nonlinear single-channel mixed signals. In practical applications, it is necessary to separate the useful signals from the complex nonlinear mixed signals. However, the traditional array signal blind source separation algorithms are difficult to separate the nonlinear signals effectively. Building upon the traditional recurrent neural network, we improved the network structure, and further proposed the three layers deep recurrent neural networks to realize single channel blind source separation of nonlinear mixed signals. The experiments and simulation were conducted to verify the performance of this method; the results showed that the mixed signals can be separated excellently and the correlation coefficient can be reached up to 99%. Thus, a new method was given for blind signal processing with artificial intelligence.

在无线传感器网络中，传感器接收的信号通常是复杂的非线性单通道混合信号。在实际应用中，有必要将有用信号与复杂的非线性混合信号分开。然而，传统的阵列信号盲源分离算法难以有效地分离非线性信号。在传统递归神经网络的基础上，改进了网络结构，进一步提出了三层深度递归神经网络，以实现非线性混合信号的单通道盲源分离。通过实验和仿真验证了该方法的有效性。结果表明，混合信号可以很好地分离，相关系数可以达到99％。从而，