Indoor WLAN Augmented Manifold Alignment and Location Method Based on Transition Probability Hotspot Mapping

Abstract

The invention discloses an indoor WLAN (Wireless Local Area Network) augmentation manifold alignment localization method based on transition probability hotspot mapping. The method comprises the following steps: partitioning a localization target region to obtain a physical environment diagram, and meanwhile observing user motion behaviors to obtain transition distribution among partitioned physical sub-regions; acquiring RSS (Received Signal Strength) sequences in the localization target region, getting a signal diagram through a correlation sequencing method, and meanwhile performing statistics to obtain transition distribution of all the RSS sequences in the signal diagram; getting a probability transition matrix with the obtained transition distribution in order to establish a hotspot mapping relation between the signal diagram and the physical environment diagram; specific to newly-acquired RSS, judging the physical sub-regions to which the newly-acquired RSS belongs according to the hotspot mapping relation; and performing localization by an augmentation manifold alignment method according to mark point fingerprints of the physical sub-regions to which the newly-acquired RSS belongs and a location coordinate set in the localization target region. The method does not need to acquire the RSS from different APs (Access Points) on each reference point at an offline stage, and is independent of a motion sensor. Meanwhile, the method has high localization accuracy.

Description

technical field [0001] The invention belongs to radio communication technology, and in particular relates to an indoor WLAN augmented manifold alignment and positioning method based on transition probability hotspot mapping. Background technique [0002] With the large-scale deployment of WLAN in the indoor environment, WLAN-based indoor positioning technology has received more and more attention. In the indoor WLAN positioning system, the location fingerprint positioning method has high accuracy and does not need to add additional equipment. Thus it has been widely used. The positioning method based on location fingerprint is mainly divided into two stages: offline stage and online stage. In the offline phase, a location fingerprint database is established by selecting appropriate reference points in the target area and measuring the signal strength values ​​from different access points (APs) at each reference point. In the online phase, the corresponding positioning meth...

AI Technical Summary

Problems solved by technology

[0003] Since the offline stage of the location fingerprint positioning method needs to collect the signal strength values ​​from different APs at each reference point in the positioning target area, it is necessary to invest a lot of manpower and time in the early stage of the positioning system construction to complete the construction of the location fingerprint database. , especially when the number of reference points is large, the workload of fingerprint collection in the offline phase will greatly increase

At the same time, the positioning method based on SLAM (Simultaneous Localization AndMapping) technology requires the assistance of additional motion sensors, and the terminal cost is high

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