Abstract
While the modern communication system, embedded system, and sensor technology have been widely used at the moment, the wireless sensor network (WSN) composed of microdistributed sensors is favored due to its relatively excellent communication interaction, real-time computing, and sensing capabilities. Because GPS positioning technology cannot meet the needs of indoor positioning, positioning based on WSN has become the better option for indoor localization. In the field of WSN indoor positioning, how to cope with the impact of NLOS error on positioning is still a big problem to be solved. In order to mitigate the influence of NLOS errors, a Neural Network Modified Multiple Filter Localization (NNMML) algorithm is proposed in this paper. In this algorithm, LOS and NLOS cases are distinguished firstly. Then, KF and UKF are applied in the LOS case and the NLOS case, respectively, and appropriate grouping processing is carried out for NLOS data. Finally, the positioning results after multiple filtering are corrected by neural network. The simulation results illustrate that the location accuracy of NNMML algorithm is better than that of KF, EKF, UKF, and the version without neural network correction. It also shows that NNMML is suitable for the situation with large NLOS error.
Highlights
Within the framework of modern Internet of Things (IoT) technology, the wireless sensor network (WSN) plays a very key role
The coordinates of n beacon nodes set are an array of n rows and 2 columns randomly generated by the function “rand” in MATLAB, whose horizontal and vertical coordinates are known and fixed during measurement
The Neural Network Modified Multiple Filter Localization (NNMML) algorithm has excellent performance compared with conventional Kalman Filtering (KF), Extended Kalman Filtering (EKF), and Unscented Kalman Filtering (UKF) in LOS/NLOS mixed environment
Summary
Within the framework of modern Internet of Things (IoT) technology, the wireless sensor network (WSN) plays a very key role. The network collects the required information through various sensors, processes the data through embedded and information distribution technology, and transmits the data to the top device. In the practical application level, the positioning technology based on this network has relative advantages compared with other positioning means. Due to the practical application demand at the present stage, satellite positioning has significant advantages in the precision demand of outdoor positioning, but it has obvious disadvantages in indoor positioning. WSN positioning [1], which is light and small in size, inexpensive in price, low in energy consumption, and topologically strong, will be a cost-effective choice in the indoor positioning field with higher positioning accuracy requirements
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