Chan-weighted centroid indoor positioning method based on Kalman filter

Abstract

The invention relates to a chan-weighted centroid indoor positioning method based on a Kalman filter. The method comprises the following steps: measuring the distance of a label to be positioned fromeach base station by a wireless sensor network; calculating a distance difference between a distance from the label to be positioned to other base station and a distance from the label to be positioned to a reference base station by using one of the base stations as the reference base station; inputting the distance difference data into the Kalman filter for filtering to obtain a distance difference in which a part of the noise interference is eliminated; inputting the filtered distance difference and base station coordinate information into a chan algorithm model to obtain an initial label coordinate; repeating step S1 to step S4 N times to obtain N initial label coordinates; and inputting the N initial label coordinates into a weighted centroid algorithm model to determine the final coordinate of the label to be positioned. According to the chan-weighted centroid indoor positioning method based on a Kalman filter, a Kalman filtering algorithm is used to preprocess the measured valueof the distance differences on the basis of the chan localization algorithm. The weighted centroid algorithm is used to optimize the initial positioning results. Therefore, the stability and accuracyof indoor positioning is improved.

Description

technical field [0001] The invention relates to an indoor positioning technology based on a chan algorithm, in particular to a chan-weighted centroid indoor positioning method based on a Kalman filter. Background technique [0002] With the rapid development of wireless sensor networks and the Internet of Things technology, people have more and more demands for indoor positioning. In shopping malls, warehouses, supermarkets, hospitals, prisons and other places, the precise positioning of people or objects is conducive to improving personnel In terms of material management efficiency, in the outdoor environment, GPS can provide high-precision positioning, but in the indoor environment, due to obstacles such as walls and tall buildings, it is difficult for GPS to give full play to its advantages. Therefore, it is difficult to find a high-precision indoor positioning method suitable for all indoor scenarios. Many scholars at home and abroad have also proposed different positio...

AI Technical Summary

Problems solved by technology

Among them, the Fang algorithm is only applicable to a limited number of base stations, and redundant measurement values ​​cannot be used. The Taylor algorithm requires a relatively accurate initial value, and the final solution is obtained through iteration. If the initial position coordinates have a large error from the real value, the result diverge

When the measurement error of the chan algorithm is small and obeys the Gaussian distribution, the root mean square error of the positioning result can reach CRLB (Cramereau lower bound), but under the influence of the indoor non-line-of-sight environment, the measurement error will increase, thus The positioning accuracy of the chan algorithm drops sharply

[0004] In order to improve the positioning effect of the chan algorithm, many methods to reduce the error have been proposed. Some of them use the identification of NLOS to judge whether the tag and the base station are transmitted by line-of-sight, and perform error compensation for the non-line-of-sight situation that greatly affects the positioning effect. , or after the chan algorithm obtains the initial location, the unscented Kalman filter and the particle filter are used for secondary location. Correct the positioning result of chan, but there is a hidden danger that the result will not converge

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