基于临近锚节点修正的DBSCAN聚类加权定位算法

为了提高无线传感器网络节点的定位精确度,给出一种基于临近锚节点修正(CAAN)的具有噪声的基于密度的聚类(DBSCAN)加权定位算法.首先,在未知节点通信范围内的锚节点中选择三个构成三角形,证明当未知节点处在此三角形外接圆圆心位置时定位误差最小,然后据此选择合适的锚节点,结合滤波后的接收信号强度指示(RSSI)值进行定...

DBSCAN clustering weighted localization algorithm based on Correction of Adjacent Anchor Node

In order to improve the localization accuracy of Wireless Sensor Network(WSN) nodes, a Density-Based Spatial Clustering of Applications with Noise(DBSCAN) clustering weighted localization algorithm based on Correction of Adjacent Anchor Node(CAAN) is proposed. Firstly, the algorithm selects three nodes from the anchor nodes within the communication range of the unknown nodes to form a triangle, and proves that the positioning error is the smallest when the unknown node is at the center of the circumscribed circle of the triangle. And the algorithm selects some appropriate anchor nodes according to this theory and combines with the filtered Received Signal Strength Indicator(RSSI) values for positioning. Then the DBSCAN clustering algorithm is adopted to remove the values with large errors. Secondly, the number of core points of the clusters is regarded as the weight, and the initial coordinates of the unknown nodes are calculated by the weighted location algorithm. Finally, the distances between anchor nodes coordinates and the initial coordinates are calculated, and the adjacent anchor nodes are selected to correct the initial coordinates, so that the final positioning results are more accurate. The simulation results show that the positioning accuracy of the proposed algorithm is improved by 69.55% and 38.64% compared with the weighted centroid location algorithm and the weighted centroid location algorithm based on RSSI ranging filtering optimization.