Improved Kalman filtering algorithm-based indoor positioning system and method

Abstract

The invention discloses an improved Kalman filtering algorithm-based indoor positioning system and method. The indoor positioning system comprises a plurality of anchor nodes, at least one unknown node, at least one collector node and a host computer. The unknown node sends data to anchor nodes therearound periodically, the anchor nodes acquire RSSI (Received Signal Strength Indication) values of the unknown node, the RSSI values received by all of the anchor nodes are screened through Gaussian filtering processing to obtain a value meeting a condition, the anchor nodes as well as location coordinates and ID information thereof are transmitted to the host computer through the collector node, distances corresponding to all of the RSSI values are calculated through the host computer, the distance values corresponding to the anchor nodes are subjected to the improved Kalman filtering processing, and coordinates of the unknown node are calculated through a trilateral positioning and centroid algorithm. Through adoption of the system and the method, the Kalman filtering algorithm is applied to indoor positioning, so that an excellent filtering effect is realized.

Description

technical field [0001] The present invention relates to an indoor positioning system and method, in particular to an indoor positioning system and method based on an improved Kalman filtering algorithm. Background technique [0002] With the rapid development of the information age, people's demand for location information services is increasingly strong, and various positioning and navigation services have widely penetrated into people's daily life. The emergence and development of the Global Positioning System (GPS) has enabled people to have an unprecedented high-reliability precise positioning technology method for obtaining the geographical location of things in the outdoor environment. However, since GPS signals cannot penetrate buildings, they cannot be fully utilized in indoor environments. [0003] Indoor location information service technology has a broad application space. This technology can enable rapid positioning of people and objects in complex indoor enviro...

AI Technical Summary

Problems solved by technology

[0004] At present, there are also many indoor positioning methods that use RSSI (Received Signal Strength Indication, received signal strength indication) to introduce the Kalman filter algorithm, which uses the Kalman filter algorithm to filter the position coordinates obtained by RSSI ranging, thereby achieving improvement The effect of error accuracy, but the system noise covariance and measurement noise covariance in the Kalman filtering model are respectively a set of Gaussian white noise sequence matrices with zero mean deviation, which is not suitable for continuously changing targets

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