High-precision indoor wireless positioning method

Abstract

The invention discloses a high-precision indoor wireless positioning method. The method comprises the following steps: A, obtaining distances from all N anchor nodes to a measured node and coordinates of the anchor nodes at a moment t; B, mining spatial correlation of adjacent moments through Kalman filtering, defining Kalman state equation coefficients on three coordinates of x, y and z at the moment t, and summarizing vectors and matrixes at all moments into a specified set form; C, performing factor decomposition on the global probability distribution by utilizing a total probability formula and a Kalman state equation; D, modeling by utilizing the factor decomposition obtained in the step C, and dividing the factor graph model into a lattice network part, a Kalman network part and a Kalman parameter estimation part; and E, carrying out message calculation on the lattice network part, the Kalman network part and the Kalman parameter estimation part through a message transmission algorithm. According to the method, the high-precision position information of the indoor to-be-tested node can be obtained efficiently and accurately.

Description

technical field [0001] The invention relates to the field of positioning measurement, in particular to a high-precision indoor wireless positioning method. Background technique [0002] With the development of wireless communications and mobile networks, people's demand for positioning is increasing. However, due to factors such as signal occlusion, it is difficult to deploy general positioning technologies such as Beidou and GPS in indoor environments. [0003] Existing wireless positioning technologies for indoors mainly include ultra-wideband (UWB), WiFi and Beacon positioning technologies, etc., each of which has specific applicable scenarios. The basic idea of ​​the above technology is to obtain the distance between the measured node and the anchor node by calculating the signal arrival time or received power of the "anchor node" (positioning node with known layout coordinates), and then use the triangulation method, centroid algorithm or least squares The multiplicati...

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Problems solved by technology

[0005] 1. The three-point positioning algorithm needs to solve the equations, but due to the existence of measurement errors, it is often impossible to obtain a unique solution, and it needs to be approximated, which affects the positioning accuracy

[0006] 2. The centroid algorithm has poor positioning accuracy when the number of anchor nodes is small

[0007] 3. The least squares estimation needs to calculate the pseudo-inverse of the matrix, which has high computational complexity and cannot be solved when the matrix is ​​not full of rank.

[0008] 4. In the actual environment, the object to be measured is usually in a continuous moving state, and the coordinates at this moment usually have a large correlation with the position information at the previous moment, and the above three methods have not effectively utilized this correlation

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