An Indoor Localization Algorithm Based on Dynamic Measurement Compressive Sensing for Wireless Sensor Networks

Indoor positioning is an important application of wireless sensor network. In order to improve the localization accuracy and fulfill the real-time requirement of localization in the large area, we propose an indoor localization algorithm based on dynamic measurement compressive sensing for wireless sensor network. The algorithm first builds a potential area that possesses the independent features with Bounding-Box method, which can decrease the number of meshing and reduce the dimension of measurement matrix. Given that only the anchor node that has a communication relationship with the unknown nodes can be used as the measuring node, we construct the dynamic measurement matrix so that the maximum number of measurement is associated with the number of grid. By this means, we can reduce the redundancy of the measurement, and improve the real-time feature of the algorithm. The simulation results indicate that the proposed algorithm can reduce the time complexity, while also ensuring the localization accuracy and efficiency.