A method of indoor positioning based on rssi

Abstract

The invention discloses an RSSI (Received Signal Strength Indicator)-based indoor positioning method. The method comprises the following steps: firstly performing kalman-based filter optimization on an RSSI measured value and then performing indoor positioning by using the RSSI value after the filter optimization; firstly comparing a maximum value of a current residual singular value with a preset threshold value each time before a gain matrix is solved in the kalman-based filter optimization, resetting the gain matrix and entering a next self-circulation if the maximum value of the residual singular value is greater than or equal to the threshold value, and otherwise, entering a state update stage without adopting extra operation. Compared with the prior art, the method has the advantages that noise influence produced to wireless signal propagation by people can be effectively eliminated, the reduction in positioning accuracy caused by accumulated error is improved, higher positioning accuracy and positioning stability are realized in an indoor environment with larger noise mean value change amplitude, and the time expenditure is low. The method is particularly suitable for indoor positioning of crowded environments.

Description

technical field [0001] The present invention relates to an RSSI-based indoor positioning method, in particular to an RSSI-based indoor positioning method that uses a Kalman-based filtering optimization method to filter and denoise RSSI measurement values ​​and improve the cumulative error of the positioning system in a noisy environment. Intersecting technical application areas of computing and signal processing. Background technique [0002] The indoor positioning algorithm strives to quickly and efficiently locate the object to be positioned in the indoor environment by using different wireless signal technologies and using the corresponding distance calculation method, so as to obtain accurate object coordinates. [0003] Current indoor positioning algorithms are based on Wireless Fidelity (Wi-Fi), Wireless Sensor Network (WSN), Radio Frequency Identification (RFID), Bluetooth (Bluetooth) and Ultra Wideband (UWB). Band, UWB) and other wireless technologies. One of these...

AI Technical Summary

Problems solved by technology

Some indoor positioning algorithms can predict high positioning accuracy in theory, but the positioning accuracy in the actual environment is very low, because these positioning algorithms do not consider the people and their activities in the indoor environment

For Wi-Fi signals, its communication frequency is 2.4GHz, which is the same as the resonance frequency of water, and the proportion of water in the human body is about 70%. People in the indoor environment will affect the propagation of the signal. If there are many people and activities Frequently, people in these indoor environments will generate a lot of noise during the indoor positioning process, reducing the positioning accuracy

[0005] It can be seen that it is not enough to simply consider the positioning of various wireless technologies in indoor unmanned environments, especially in large indoor positioning scenarios such as museums and commercial centers, where there are many people and the flow of people is large. For higher positioning accuracy, it is necessary to solve the impact of noise caused by people themselves

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