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Sliding time window based WLAN (Wireless Local Area Network) indoor WKNN (Weighted K Nearest Neighbors) tracking method

A sliding time window, indoor positioning technology, applied in the field of pattern recognition, can solve the problems of severe jitter of estimated position coordinates and uneven estimated trajectory.

Inactive Publication Date: 2010-11-24
HARBIN INST OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] In order to solve the problem that in the existing WLAN indoor tracking method, the estimated trajectory is not smooth due to factors such as the limitation of terminal sampling rate and the difficulty of obtaining the motion state, that is, the problem that the estimated position coordinates shake violently, the present invention provides a sliding time window based Indoor WKNN Tracking Method for WLAN

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  • Sliding time window based WLAN (Wireless Local Area Network) indoor WKNN (Weighted K Nearest Neighbors) tracking method
  • Sliding time window based WLAN (Wireless Local Area Network) indoor WKNN (Weighted K Nearest Neighbors) tracking method
  • Sliding time window based WLAN (Wireless Local Area Network) indoor WKNN (Weighted K Nearest Neighbors) tracking method

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specific Embodiment approach 1

[0024] Specific implementation mode one: according to the instructions attached figure 1 , 2 , 3, 4 and 5 specifically describe the present embodiment, the WLAN indoor WKNN tracking method based on the sliding time window described in the present embodiment, its tracking process is:

[0025] Step 1: Set N evenly in the indoor positioning area of ​​the WLAN target terminal RP reference points, and arrange N in the indoor positioning area c APs, so that each reference point at least collects a signal strength RSS value from one AP;

[0026] Step 2: Select a reference point as the coordinate origin O to establish a two-dimensional rectangular coordinate system, and obtain N RP The coordinate positions of each reference point in the two-dimensional Cartesian coordinate system, and based on the coordinate position of each reference point and the signal strength RSS value from each access point AP collected by each reference point, a location fingerprint database is established ...

specific Embodiment approach 2

[0039] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in this embodiment, in step 38, the final estimated position required when obtaining the smooth estimated motion trajectory of the target terminal also includes Final estimated position at non time Among them, CR(a) x Represents the acquired x-direction coordinates of the final estimated position CR(a) of the target terminal at time a, CR(a) y Indicates the y-direction coordinates of the acquired final estimated position CR(a) of the target terminal at time a, CR(b) x Indicates the acquired x-direction coordinates of the final estimated position CR(b) of the target terminal at time b, CR(b) y Indicates the acquired y-direction coordinates of the final estimated position CR(b) of the target terminal at time b, the time non is adjacent to time a and time b respectively, and b

specific Embodiment approach 3

[0040] Specific implementation mode three: according to the instructions attached Figure 6 and 7 Describe this implementation mode in detail. This implementation mode is a further description of the specific implementation mode 1 or 2. In the specific implementation mode 1 or 2, in step 38, the position dispersion threshold where x g，p and y g，p Respectively represent the x-direction coordinates and y-direction coordinates of the position of the target terminal in the indoor positioning area of ​​the WLAN target terminal in the two-dimensional square area Γ when it jumps randomly for the pth time when the final estimated position of the target terminal is tested for the gth time, The indoor positioning area of ​​the WLAN target terminal is a square area Γ=χ×χ, where, N mobile Indicates the number of final estimated position coordinates of the target terminal, N rand Indicates the number of random jumps of the target terminal in the square area Γ, N text Indicates the ...

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Abstract

The invention discloses a sliding time window based WLAN (Wireless Local Area Network) indoor WKNN (Weighted K Nearest Neighbors) tracking method, relating to the field of mode identification and solving the problem of unsmooth estimated track, i.e. fierce shake of an estimated position coordinate, caused by elements of limited terminal sampling rate and difficult motion state obtain in the traditional WLAN indoor tracking method. The method comprises the steps of: firstly, acquiring signal samples and establishing a corresponding position fingerprint database; obtaining the pre-estimated position coordinate, motion rate and confidence region at different time of the terminal in real time with a WKNN positioning method according to a signal sample newly acquired; rejecting fault pre-estimated position points with abrupt signal intensity change in a pre-estimated position set compared with adjacent time position points according to a forward sliding time window threshold, a backward sliding time window threshold and a corresponding confidence region; and finally, connecting final estimated position points in chronological sequence to obtain the smooth estimated motion track for the terminal. The invention is suitable for indoor tracking and positioning.

Description

technical field [0001] The invention relates to the field of pattern recognition, in particular to an indoor WKNN tracking method of a WLAN based on a sliding time window. Background technique [0002] WLAN (Wireless Local Area Network) technology is a high-speed wireless IP network communication technology developed at the end of the 20th century. The technical standard number is IEEE 802.11. Since the standard came out, the wireless communication market and related location-based application services has grown by leaps and bounds. Under the existing WLAN indoor environment, users can realize high-speed access to the Internet anytime and anywhere with mobile portable computing devices, such as notebook computers, palmtop computers and personal digital assistants (PDAs). [0003] Moreover, indoor mobile users have increasingly strong demands for real-time and local location information, which also provides a broader market space for services and applications based on termin...

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Application Information

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IPC IPC(8): H04W8/16H04W64/00H04W84/12
Inventor 徐玉滨孟维晓周牧马琳谭学治吴少川
Owner HARBIN INST OF TECH
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