WiFi indoor positioning method of semi-supervised manifold learning based on category matching

A manifold learning and indoor positioning technology, applied in the WiFi indoor positioning field of semi-supervised manifold learning, can solve the problems of difficult mobile terminal implementation, difficult real-time requirements, and difficult to obtain.

Active Publication Date: 2014-03-19
严格集团股份有限公司
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Problems solved by technology

[0010] The present invention is to solve the problem of the large Radio Map database existing in the existing WiFi indoor positioning method, and the difficulty in applying the online phase to obtain RSS data due to the high computational complexity of the online positioning phase, the difficulty in realizing it in the mobile terminal, and the difficulty in ensuring real-time positioning. In order to address issues such as gender requirements, a WiFi indoor positioning method based on semi-supervised manifold learning based on category matching is provided.

Method used

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  • WiFi indoor positioning method of semi-supervised manifold learning based on category matching
  • WiFi indoor positioning method of semi-supervised manifold learning based on category matching
  • WiFi indoor positioning method of semi-supervised manifold learning based on category matching

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specific Embodiment approach 1

[0040] Specific implementation mode one: The offline stage positioning process of the WiFi indoor positioning method based on category matching semi-supervised manifold learning in this embodiment is implemented in the following steps:

[0041] 1. Arrange APs in the indoor area to be positioned, so that the wireless signal covers the indoor area to be positioned, and complete the construction of the WiFi network;

[0042] Select and record the corresponding coordinates of the reference point in the indoor area to be positioned, measure and record the RSS signals of all APs received by the reference point in turn as location feature information, construct a Radio Map, and store the Radio Map;

[0043] 2. Use the GMST eigendimension estimation algorithm to analyze the eigendimension of the Radio Map constructed in step 1. The obtained eigendimension is used as one of the input parameters of the CM-SDE algorithm to determine the dimensionality of the Radio Map after dimension redu...

specific Embodiment approach 2

[0054] Specific embodiment two: the difference between this embodiment and specific embodiment one is: adopt GMST intrinsic dimension estimation algorithm to analyze the intrinsic dimension of the Radio Map constructed in step one in step two, its calculation formula is:

[0055] Geodesic minimum spanning tree algorithm

[0056] In the above formula A in represents the slope of the linear fitting expression y=ax+b of the minimum spanning tree.

[0057] Other steps and parameters are the same as those in Embodiment 1.

specific Embodiment approach 3

[0058] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: in step four, generate a new dimensionality-reduced signal coverage map RadioMap * And the V' expression in step 6 is:

[0059] Radio Map*=V′·X

[0060] X is a Radio Map that needs dimensionality reduction.

[0061] Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.

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Abstract

The invention discloses a WiFi indoor positioning method of semi-supervised manifold learning based on category matching, and relates to an indoor positioning method. The WiFi indoor positioning method disclosed by the invention is used for solving the problems that a Radio Map database is large and the like in an existing WiFi indoor positioning method. The WiFi indoor positioning method comprises the following steps: 1. collecting Radio Map; 2. carrying out intrinsic dimension analysis on the Radio Map; 3. carrying out clustering analysis on the Radio Map; 4. carrying out dimensionality reduction on the Radio Map; 5. adding RSS in the Radio Map to obtain Radio Mapul; and 6. carrying out dimensionality reduction on the Radio Mapul to obtain a characteristic transformation matrix V, and forming an online positioning database through the Radio Map * and V. The WiFi indoor positioning method also comprises the following steps: 1. online testing RSS; 2. carrying out dimensionality reduction on the RSS to obtain RSS *; 3. outputting a positioning result; and 4. updating the database. The WiFi indoor positioning method disclosed by the invention is applied to the field of network technology.

Description

technical field [0001] The invention relates to an indoor positioning method, in particular to a WiFi indoor positioning method based on category matching semi-supervised manifold learning. Background technique [0002] With the rapid development of wireless local area networks in the world and the widespread popularization of mobile terminal equipment, many technologies and applications related to indoor positioning have emerged in recent years. Due to the multipath effect, signal attenuation and the complexity of the indoor positioning environment, it is difficult for the indoor positioning method based on the traditional signal propagation model to meet the high-precision indoor positioning requirements. Although positioning methods based on Time of Arrival (Time of Arrival), Time Difference of Arrival (Time Difference of Arrival) and Angles of Arrival (Angles of Arrival) can basically meet the positioning accuracy requirements, they all require additional hardware equipm...

Claims

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Application Information

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IPC IPC(8): H04W16/20H04W64/00
Inventor 谭学治周才发马琳邓仲哲何晨光迟永钢魏守明
Owner 严格集团股份有限公司
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