Indoor positioning method for reducing fingerprint data acquisition workload

Abstract

The invention discloses an indoor positioning method for reducing fingerprint data acquisition workload. For a large number of point pairs with larger AP (Access Point) RSSI (Received Signal Strength Indicator) difference values, the physical distances are larger. The method comprises the following steps: calculating the RSSI 'distance value' between points by taking the RSSI values of multiple APs obtained at a certain position as high-dimension characteristic data of the position according to the characteristic for serving as the intimate relationship between two points; acquiring the relative position relationship of one low-dimension space between each object by using a multi-dimensional scaling analysis algorithm; evaluating the absolute positions of other points by taking a small number of sample points as anchor nodes. By adopting the method, the aim of reducing the acquisition number of sample points is fulfilled. A hierarchical region reducing method is put forward on the basis in order to further fulfill the aim of reducing the positioning error. Compared with a KNN (K Nearest Neighbor) algorithm, the method has the advantages that the sampling points can be reduced, and meanwhile the positioning error is reduced.

Description

technical field [0001] The invention relates to an indoor positioning method for reducing the workload of fingerprint data collection. Background technique [0002] Currently, common indoor positioning technologies include acoustic signals, FM signals, RFID, Bluetooth signals, cellular networks, ZigBee, UWB and WiFi. Generally speaking, the mode of wireless indoor positioning is to first obtain the physical measurement value of the wireless signal, and then either adopt the method of propagation model or the method based on fingerprint. [0003] Divided according to the physical measurement value utilized, it can be divided into time, angle and power. The time-based physical measurement values ​​include Time of Arrival (TOA) and Time Difference of Arrival (TDOA), and the power-based positioning methods include RSSI and CSI. In complex multi-path indoor environments, both TOA and AOA measurements require special hardware measurement equipment, and there is a large deviation...

AI Technical Summary

Problems solved by technology

In the online positioning stage, the user is roughly positioned to the positioning sub-area through clustering and segmentation, and then the precise positioning is carried out. A total of 110 reference sample points are collected, and the distance between the sample points is about 1.52 meters. The positioning accuracy is 0.8 meters, but there are also articles pointing out that such a high positioning accuracy cannot be achieved when copying this method in other work

[0007] The above methods all need to collect a large number of online RSSI samples, which greatly restricts the popularization and application of large-scale indoor positioning.

The workload of reference point sample collection mainly depends on the density of reference points and the sampling time. The reference point collection interval is large and the sampling time is short, which can reduce manpower and material resources, but it may not be able to guarantee the fineness of the RSS signal to describe the physical location space.

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