针对传统单节点调制识别存在阴影和多径问题,提出了一种利用无线传感器网络进行分布式协同调制识别的方法。首先由相互协作的传感器节点提取信号的五个典型特征构成特征矢量,将特征矢量发送到中心节点,在中心节点通过粒子群算法对人工神经网络进行参数优化,最后利用训练好的神经网络进行分类识别,得到识别结果。对6种信号进行了仿真,结果表明该方法在信噪比大于5 dB且测试样本数大于50时,最终的识别率超过了90%,验证了将粒子群算法应用于神经网络参数优化进行分类识别可以有效提升识别性能。%The traditional modulation identification with single node has problems of shadow effect and multipath fading, in view of which a kind of distributed collaborative modulation identification method based on wireless sensor network is presented. Firstly, characteristic vectors are formed by five typical signal characteristics extracted by the mutual cooperative sensor nodes and then sent to the central node. Secondly, parameters of artificial neural network are optimized by the particle swarm algorithm in the cen- tral node. Finally, classification is carried on by the trained neural network and result is got. 6 kinds of signals are simulated, and the results show that the final recognition rate of this method is more than 90% under the condition that the signal-to-noise ratio is higher than 5 dB and test samples are more than 50. It illustrates that identification performance can be improved effectively when particle swarm algorithm is used in parameters optimization of neural network.
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