In recent years,WiFi indoor positioning technology has become a hot research topic at home and abroad.However,at present,indoor positioning technology still has many problems in terms of practicability and stability,w...In recent years,WiFi indoor positioning technology has become a hot research topic at home and abroad.However,at present,indoor positioning technology still has many problems in terms of practicability and stability,which seriously affects the accuracy of indoor positioning and increases the complexity of the calculation process.Aiming at the instability of RSS and the more complicated data processing,this paper proposes a low-frequency filtering method based on fast data convergence.Low-frequency filtering uses MATLAB for data fitting to filter out low-frequency data;data convergence combines the mean and multi-data parallel analysis process to achieve a good balance between data volume and system performance.At the same time,this paper combines the position fingerprint and the relative position method in the algorithm,which reduces the error on the algorithm system.The test results show that the strategy can meet the requirements of indoor passive positioning and avoid a large amount of data collection and processing,and the average positioning error is below 0.5 meters.展开更多
文摘In recent years,WiFi indoor positioning technology has become a hot research topic at home and abroad.However,at present,indoor positioning technology still has many problems in terms of practicability and stability,which seriously affects the accuracy of indoor positioning and increases the complexity of the calculation process.Aiming at the instability of RSS and the more complicated data processing,this paper proposes a low-frequency filtering method based on fast data convergence.Low-frequency filtering uses MATLAB for data fitting to filter out low-frequency data;data convergence combines the mean and multi-data parallel analysis process to achieve a good balance between data volume and system performance.At the same time,this paper combines the position fingerprint and the relative position method in the algorithm,which reduces the error on the algorithm system.The test results show that the strategy can meet the requirements of indoor passive positioning and avoid a large amount of data collection and processing,and the average positioning error is below 0.5 meters.
