Liu Jianlan
School of Electronic Information Engineering, Nantong Vocational University, Nantong, Jiangsu 226007, China.
Comput Intell Neurosci. 2022 Jul 14;2022:9026017. doi: 10.1155/2022/9026017. eCollection 2022.
In this paper, a better particle filter algorithm is put forth to address the issues of particle filter sample exhaustion and weight degradation. The algorithm frames the received signal and separates the signals in two steps based on the slow-varying properties of system parameters in practical applications, such as phase shift and transmission delay. In addition, the network model and energy consumption model are built while the sensor data is being collected and processed using the industrial IoT's communication mechanism and algorithm. The repeater is chosen as the node with the lowest transmission energy consumption, and the industrial field's sensor data is gathered via the fog server node. The simulation results demonstrate that the proposed algorithm's accuracy rate is 95.54 percent, higher than that of the comparison algorithm. The enhanced algorithm suggested in this paper can simultaneously achieve improved parameter estimation performance and achieve signal separation with low bit error rates. Additionally, the communication system and algorithm can efficiently gather the sensing information from the industrial field, and the indicators like energy consumption and the first dead node are better than other algorithms. It offers an innovative method for enhancing industrial field application.
本文提出了一种更好的粒子滤波算法，以解决粒子滤波样本耗尽和权重退化问题。该算法对接收到的信号进行帧处理，并根据实际应用中系统参数的慢变特性（如相移和传输延迟）分两步分离信号。此外，在使用工业物联网的通信机制和算法收集和处理传感器数据时，构建了网络模型和能耗模型。选择中继器作为传输能耗最低的节点，并通过雾服务器节点收集工业现场的传感器数据。仿真结果表明，所提算法的准确率为95.54%，高于比较算法。本文提出的改进算法能够同时提高参数估计性能，并以低误码率实现信号分离。此外，通信系统和算法能够有效地收集来自工业现场的传感信息，能耗和首个死亡节点等指标优于其他算法。它为增强工业现场应用提供了一种创新方法。
