CN103068039B - Wireless fidelity (WIFI) signal-based locating method for received signal strength indicator (RSSI) values - Google Patents

Abstract

The invention discloses a positioning method based on the RSSI value of a WiFi signal. For each known position point in a location to be positioned, the RSSI value of the WiFi signal is collected multiple times and uploaded to a server, and the server calculates the average value avg of the collected data and the standard deviation dev, stored in the database; collect the RSSI value of the WiFi signal at the point to be positioned, and upload it to the server; use the fingerprint recognition algorithm to match the RSSI value of the WiFi signal at the point to be positioned with the expected value; obtain the three most likely points The information is weighted and averaged according to the probability to obtain the coordinates of the positioning point. Compared with the prior art, the present invention improves the positioning accuracy, which can be accurate to 2 meters, and can fully meet the requirements of indoor positioning.

Description

A positioning method based on RSSI value of WiFi signal

technical field

The present invention relates to indoor WiFi positioning technology, in particular to a positioning method based on RSSI value (signal reception strength value) of WiFi signal.

Background technique

At present, most common WiFi indoor positioning technologies are based on IEEE802.11b/g protocol wireless local area network (WLAN) signal strength positioning technology. The basic principle of positioning technology based on signal strength is to calculate the distance between the signal receiver and the signal source according to the strength of the received signal, which is mainly divided into two categories: triangle strength algorithm and location fingerprint recognition algorithm. Among them, the triangle intensity algorithm has low precision, which is difficult to meet the indoor positioning requirements; and the common fingerprint recognition algorithm has the defect that the receiving equipment is different, which makes the received signal have errors.

Contents of the invention

In order to overcome the above-mentioned shortcomings and deficiencies of the prior art, the purpose of the present invention is to provide a positioning method based on the RSSI value of a WiFi signal, which improves the positioning accuracy.

The object of the present invention is achieved through the following technical solutions:

A positioning method based on the RSSI value of a WiFi signal, comprising the following steps:

(1) Set multiple WiFi hotspots in the location to be positioned, use a handheld terminal to collect the RSSI value of the WiFi signal multiple times at each known location point in the location to be positioned, and upload it to the server. The server calculates the collected data. The average avg and standard deviation dev are stored in the database;

(2) Use the handheld terminal to collect the RSSI value of the WiFi signal at the point to be located, and upload it to the server;

(3) For the normal distribution formula

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&mu;</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}$

Let μ=avg, σ=dev, substitute the RSSI value of the WiFi signal of the point to be located into x in the normal distribution formula to obtain the probability P _mn , where m represents the sequence number of the known location point, and n represents the sequence number of the WiFi hotspot;

P _m = P _m1 P _m2 ... P _mn ;

(4) Find three known locations in the database corresponding to the largest P _mn values, and set their coordinates as (x ₁ , y ₁ ), (x ₂ , y ₂ ), (x ₃ , y ₃ ), The corresponding P _mn values are P ₁ , P ₂ , and P ₃ respectively; the initial coordinates of the point to be positioned are obtained by the following formula:

$<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span>$

(5) In fact, the difference in probability is generally more than 10 orders of magnitude. Since the point with the highest probability in the above formula accounts for almost all the weights, the logarithm of the probability is corrected to obtain the corrected coordinates of the point to be located:

$<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}^{<span\; class="notranslate">\; ,</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}^{<span\; class="notranslate">\; ,</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; g</span>}\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; g</span>}\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; g</span>}\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span>$

In step (2), use the handheld terminal to collect the RSSI value of the WiFi signal at the point to be located, and upload it to the server, specifically:

The user first scans the QR code at the known location where the QR code is set, and the handheld terminal uploads the current location and the RSSI value of the WiFi signal measured to the server, and the server uses the RSSI value of the WiFi signal measured at this time as the current Based on the reference positioning data of the handheld terminal, compare this value with the RSSI value of the WiFi signal at the current location in the database, then subtract and take the average to obtain the offset Offset, and subtract the measured RSSI value of the WiFi signal during positioning Upload to the server after offset Offset.

The above positioning method based on the RSSI value of the WiFi signal also performs the following steps:

Two handheld terminals are bound, and the server calculates the distance between the two handheld terminals, and sends an alarm when the distance exceeds the threshold.

The multiple WiFi hotspots are arranged in a zigzag shape.

Compared with the prior art, the present invention has the following advantages and beneficial effects:

(1) By improving the traditional fingerprint-based positioning method, the present invention can improve the positioning accuracy to 2 meters, which can fully meet the requirements of indoor positioning.

(2) The present invention solves the problem of multiple terminals obtaining RSSI values at the same location by setting a two-dimensional code at a known location, scanning the two-dimensional code before positioning with a handheld terminal, and correcting the RSSI value of the WiFi signal measured by the handheld terminal. The question of difference.

(3) The wireless access point of the present invention is arranged in a zigzag shape with a better positioning effect.

Description of drawings

FIG. 1 is a flowchart of the work of the handheld terminal according to the embodiment of the present invention.

Fig. 2 is a working flow chart of the server of the embodiment of the present invention.

detailed description

The present invention will be described in further detail below in conjunction with the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

Example

The system for implementing the positioning method based on the RSSI value of the WiFi signal in this embodiment includes a handheld terminal, a wireless access point, and a server.

The positioning method based on the RSSI value of the WiFi signal in this embodiment includes the following steps:

(1) Set up multiple WiFi hotspots in large shopping malls; in order to improve positioning accuracy, multiple WiFi hotspots are placed in a zigzag line; The RSSI value of the WiFi signal is collected once and uploaded to the server. The server calculates the average avg and standard deviation dev of the collected data and stores them in the database.

(2) Use the handheld terminal to collect the RSSI value of the WiFi signal at the point to be located, and upload it to the server, specifically:

The user first scans the QR code at the known location where the QR code is set, and the handheld terminal uploads the current location and the RSSI value of the WiFi signal measured to the server, and the server uses the RSSI value of the WiFi signal measured at this time as the current Based on the reference positioning data of the handheld terminal, compare this value with the RSSI value of the WiFi signal at the current location in the database, then subtract and take the average to obtain the offset Offset, and subtract the measured RSSI value of the WiFi signal during positioning Upload to the server after offset Offset.

(3) For the normal distribution formula

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}\sqrt{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\frac{{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&mu;</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}}$

Let μ=avg, σ=dev, substitute the RSSI value of the WiFi signal of the point to be located into x in the normal distribution formula to obtain the probability P _mn , where m represents the sequence number of the known location point, and n represents the sequence number of the WiFi hotspot;

P _m =P _m1 ·P _m2 . . . P _mn .

(4) Find three known locations in the database corresponding to the largest P _mn values, and set their coordinates as (x ₁ , y ₁ ), (x ₂ , y ₂ ), (x ₃ , y ₃ ), The corresponding P _mn values are P ₁ , P ₂ , and P ₃ respectively; the initial coordinates of the point to be positioned are obtained by the following formula:

$<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span>$

(5) In fact, the difference in probability is generally more than 10 orders of magnitude. Since the point with the highest probability in the above formula accounts for almost all the weights, the logarithm of the probability is corrected to obtain the corrected coordinates of the point to be located:

$<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}^{<span\; class="notranslate">\; ,</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}^{<span\; class="notranslate">\; ,</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; g</span>}\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; g</span>}\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; g</span>}\frac{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; .</span>$

The principle of step (4) is: According to the Bayesian formula: D1, D2, ..., Dn is a division of the sample space S, if P(Di) represents the probability of event Di occurring, and P(Di)>0 (i=1, 2, . . . , n). For any event x, P(x)>0, then: Wherein, P(A) is taken as the probability that the RSS intensity value collected by the terminal at a certain point is equal to a certain value, and P(B) is the probability that the terminal is at a certain point. The probability of the user appearing at each point is the same, so for a certain system formula on the right side, except P(A|B _i ) is a fixed value, so if P(AB _i )>P(AB _j ), then there is P(B _i |A)>P(B _j |A). Therefore, as long as the point corresponding to max{P _m } is found, it is the most likely position of the point to be located.

As shown in Figure 1, the working process of the handheld terminal is as follows: After startup, when the interface initialization is completed, the registration information is sent to the server, and the server returns the ID client; after the terminal obtains the ID address, three processes are linked to run. Process 1: The handheld terminal keeps collecting the RSSI value of nearby WIFI and sends it to the server; Process 2: When the handheld terminal detects the location information returned by the server, it displays the current location. Process 3: When the user process button operation is detected, if it is a search request, it will be sent to the server, and the server will return the coordinates of the search target and the navigation route, and the handheld terminal will display the navigation route until the user reaches the target location; if it is a backtracking request, the request will also be sent to the server, and the server returns the walking path just now, so that the user can return along the original path. If the user performs parent-child binding, the handheld terminal will display the location information of the current user and the user bound to it. When the distance between the two locations exceeds the safety threshold, the parent terminal will send out an alarm until the user contacts the alarm.

As shown in Figure 2, the server has been monitoring UDP information after working. When receiving the RSSI value sent by the user, the average value avg and standard deviation dev of the RSSI value are matched with the coordinates in the database according to the above positioning method, and updated to obtain User location information, broadcast coordinate data; when a navigation request is obtained, the target coordinate search is performed, and the route search is performed in the map node file after the target coordinates are obtained, and the route closest to the destination is obtained and returned; the backtracking request is obtained from the route database The user's previous travel path returns the user's nearest path; when receiving the QR code correction information (including the user's current coordinate position) and the current RSSI value, the server compares the data with the reference RSSI value of the same point in the database, and calculates Correct the server data and return the user's current location information at the same time.

The above-mentioned embodiment is a preferred embodiment of the present invention, but the embodiment of the present invention is not limited by the embodiment, and any other changes, modifications, substitutions and combinations made without departing from the spirit and principle of the present invention , simplification, all should be equivalent replacement methods, and are all included in the protection scope of the present invention.

Claims (4)

1. based on a localization method for the RSSI value of WiFi signal, it is characterized in that, comprise the following steps:

(1) multiple Wi-Fi hotspot is set in place to be positioned, use handheld terminal to each known location point in place to be positioned, the RSSI value of multi collect WiFi signal, and upload onto the server, server to be averaged avg and standard deviation dev to the data collected, and deposits to database; Wherein, RSSI value is signal receiving strength value;

(2) use handheld terminal to gather the RSSI value of the WiFi signal in site undetermined, and upload onto the server;

(3) to normal state distribution formula

$f\left(x\right)=\frac{1}{\mathrm{\&sigma;}\sqrt{2\mathrm{\&pi;}}}{e}^{-\frac{{(x-\mathrm{\&mu;})}^2}{2{\mathrm{\&sigma;}}^2}}$

Make μ=avg, σ=dev, the RSSI value of the WiFi signal in site undetermined is substituted into the x in normal distribution formula, obtains probability P _mn, wherein m represents the sequence number of known location point, and n represents the sequence number of Wi-Fi hotspot;

P _m＝P _m1·P _m2...P _mn；

(4) corresponding P is found out in a database _mnbe worth three maximum known location points, if its coordinate is respectively (x ₁, y ₁), (x ₂, y ₂), (x ₃, y ₃), corresponding P _mnvalue is respectively P ₁, P ₂, P ₃; The initial coordinate in site undetermined is tried to achieve by following formula:

$(x,y)=\frac{P_1}{P_1+P_2+P_3}(x_1,y_1)+\frac{P_2}{P_1+P_2+P_3}(x_2,y_2)+\frac{P_3}{P_1+P_2+P_3}(x_3,y_3)$

(5) probability taken the logarithm revise, obtain the correction coordinate in site undetermined:

$(x^{,},y^{,})=(-\lg \frac{P_1+P_2+P_3}{P_1})(x_1,y_1)+(-\lg \frac{P_1+P_2+P_3}{P_2})(x_2,y_2)+(-\lg \frac{P_1+P_2+P_3}{P_3})(x_3,y_3).$

2. the localization method of the RSSI value based on WiFi signal according to claim 1, is characterized in that, step (2) described use handheld terminal gathers the RSSI value of the WiFi signal in site undetermined, and uploads onto the server, and is specially:

First user is being provided with the known location spot scan Quick Response Code of Quick Response Code, current location and the RSSI value of WiFi signal that measures are uploaded onto the server by handheld terminal, server is using the origin reference location data of the RSSI value of measured WiFi signal as current handheld terminal, the RSSI value of the WiFi signal of current location in this value and database is compared, then subtract each other, be averaged, obtain side-play amount Offset, upload onto the server after the RSSI value of surveyed WiFi signal being deducted side-play amount Offset during location.

3. the localization method of the RSSI value based on WiFi signal according to claim 1, is characterized in that, also carries out following steps:

Two handheld terminals are bound, and server calculates the distance of two handheld terminals, report to the police when distance exceedes threshold value.

4. the localization method of the RSSI value based on WiFi signal according to claim 1, is characterized in that, putting of described multiple Wi-Fi hotspot is put in broken line " it " word.