含噪混合通信数据中信号源定位挖掘算法仿真

Mining Algorithm Simulation of Mixed Communication Data Source with Noises Localization

研究含噪混合通信信号中信号源的准确定位问题。当前的通信过程中,环境干扰和其它的有源干扰会造成接收到的通信数据信号包含大量的时间白噪声,使得数据的属性和关联性发生混乱。传统的关联规则的信号源定位方法在包含大量的噪声数据中,很难针对混乱的数据关联性建立稳定的数据关联模型,造成信号源定位的不确定。提出一种用于含噪混合通信数据原信号挖掘算法,算法通过贝叶斯决策理论对含噪信号进行有效区分,运用推理关联机制在区分后的数据中建立较为清晰的关联关系,克服传统方法的弊端。实验表明,改进算法能够有效的在大量含噪通信数据中定位信号源,准确度较高。

Research the accurate localization of mixed signal source in communication with noises. This paper put forward a hybrid communication with noise used in the original signal data mining algorithm, through the Bayesian de- cision theory the algorithm was used to effectively distinguish the noise signals, and the reasoning linking mechanism was used to build up a clear relationship between the distinguished data. The experimental results show that this method is effective for the data localization of signal source in a large number of communications with noises, and its accuracy is higher.