Semi-occlusion substation fusion positioning method based on multi-sensor signal statistical model

Abstract

There are problems such as building occlusion and electromagnetic interference in substations, which lead to a rapid decline in the accuracy of traditional personnel control methods based on electromagnetic wave positioning. In order to avoid the reduction of power safety management and control efficiency due to the degradation of single sensor positioning accuracy, it is very important to study the position estimation technology of patrol personnel based on multi-source information fusion. However, most of the existing fusion localization algorithms are difficult to robustly select sensor fusion strategies under the condition of unknown map information. A fusion positioning method based on satellite and near-ultrasonic signal feature analysis is proposed in this paper, which only relies on signal statistical features to realize environmental information discrimination and adaptively select fusion strategies. Firstly, the fingerprint database is constructed by using the multi-sensor signal feature statistical model, and the fingerprint library data is processed based on the t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction algorithm and the density peaks clustering (DPC) algorithm fingerprint library data. Secondly, a back propagation (BP) neural network is built based on the clustering results, and the signal environment features are mapped to the parameters of the Kalman filter. Finally, the neural network output is used to optimize the Kalman filter-based multi-source positioning switching model to form an adaptive fusion positioning algorithm. Experiments are carried out using data collected in a real substation semi-occluded environment. The results show that, compared with the fusion positioning method of unknown environmental information and known environmental information, the proposed method saves map marking information when the map information is unknown and realizes highly robust location estimation.

Keywords

• semi-occlusion substation environment
• signal character
• near-ultrasound
• t-distributed stochastic neighbor embedding (t-sne)
• density peaks clustering (dpc)
• fusion positioning