CN110611952B - Fingerprint matching and positioning method - Google Patents

Fingerprint matching and positioning method Download PDF

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CN110611952B
CN110611952B CN201910890201.6A CN201910890201A CN110611952B CN 110611952 B CN110611952 B CN 110611952B CN 201910890201 A CN201910890201 A CN 201910890201A CN 110611952 B CN110611952 B CN 110611952B
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CN110611952A (en
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姜楠
徐光明
李芳�
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Beijing Muxing Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/003—Locating users or terminals or network equipment for network management purposes, e.g. mobility management locating network equipment
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination

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Abstract

The invention discloses a fingerprint matching and positioning method, which comprises the following steps: acquiring RSSI reference values and MAC addresses of access points; secondly, the user terminal obtains the RSSI measured value and the MAC address of each access point; respectively judging whether the dispersion between the RSSI measurement vector and the RSSI reference vector corresponding to the k reference point positions is in a threshold range and whether the number n of access points in the RSSI measurement vector and the RSSI reference vector is greater than a preset minimum number of access points; step four, calculating the difference value between the RSSI measured value and the RSSI reference value of each common access point, and eliminating abnormal access points; step five, repeatedly executing the step three and the step four; and step six, executing a position fingerprint matching algorithm to obtain the coordinate position of the user mobile terminal. The invention can effectively eliminate the influence caused by the fluctuation or the abnormity of the RSSI measured value of one or more access points, and improve the precision and the reliability of the position fingerprint matching and positioning result.

Description

Fingerprint matching and positioning method
Technical Field
The invention relates to the field of service of internet of things, indoor positioning and based on positions. In particular, it relates to a fingerprint matching and positioning method.
Background
Statistically, over 80% of the time of modern urban populations spent in indoor environments, indoor location service needs have become increasingly widespread and important. Based on the indoor position information, the system can provide abundant and various position-Based services (LBS) for common people and various professionals under various scenes such as markets, parking lots, libraries, conference exhibition halls, office buildings, hospitals, schools, museums and the like, and can improve the social productivity and promote the economic development and simultaneously ensure the property and life safety of people.
Among various existing indoor positioning technologies, location fingerprint matching (Finger Print) is a common method, which is based on wireless communication and network technologies, and has the characteristics of easy implementation, low cost, low requirement on Access Point (hereinafter referred to as AP) time synchronization accuracy, and the like, can be implemented based on different Wireless Local Area Network (WLAN) sensors such as Wi-Fi and Bluetooth, and is widely used in many indoor positioning scenes. The basic principle of the location fingerprint matching method is to abstract and formally describe the characteristics of the location environment, describe location information in the location environment by using Received Signal Strength Indication (RSSI) sequences of each AP in the location environment, and acquire the RSSI sequences to establish a location fingerprint database (Data base). When the user actually positions and uses the RSSI sequence measured in real time by the user, the RSSI sequence is matched with the position fingerprint information in the position fingerprint database, and the result with the optimal matching similarity is selected as the self position estimation. This type of process essentially comprises two stages: an off-line training phase and an on-line positioning phase, as shown in the attached figure 1 of the specification.
The off-line training stage aims at establishing a position fingerprint database, positioning system deployment personnel traverse all positions in a positioning environment, RSSI values from different APs are collected at each reference position, and multimedia connection (MAC) addresses, RSSI values and position information of reference points of the APs form a related array to be stored in the position fingerprint database. In the on-line positioning stage, the user measures the MAC addresses and RSSI values of all APs in real time, the MAC addresses and RSSI values are used as input data of a position fingerprint matching algorithm, and position estimation is carried out by using a specific matching algorithm.
The Nearest Neighbor (NN) is the most basic and commonly used matching algorithm in the location fingerprint matching method. Based on the analogy learning principle, the similarity matching is carried out by using a real-time measurement value in a positioning stage and a sampling database (namely, position fingerprints) in a training stage, and the similarity between the positioning fingerprint and the position fingerprint is described by using Euclidean Distance (Euclidean Distance). And finally, taking the coordinates of the position fingerprint with the highest similarity as an estimated position. Defining an RSSI measurement vector R at time ttRSSI reference vector R with reference point location j (j ═ 1,2, …, k) in fingerprint databasejBetween the Euclidean distance Dist (R)t，Rj) Comprises the following steps:
Dist(Rt，Rj)＝||Rt-Rj||2 (1)
wherein R isj＝(RSSI1，j，RSSI2，j，…，RSSIn，j) Representing RSSI reference vectors from n access points received at reference point location j in the location fingerprint database; rt＝(RSSI1，RSSI2，…，RSSIn) The RSSI measurement vectors from n aps measured by the ue at time t are shown.
Finally, the position of the reference point with the minimum Euclidean distance is taken as the user estimated position L, namely:
Figure BSA0000190654730000021
on the basis, a K-Nearest Neighbor algorithm (KNN) and a weighted K-Nearest Neighbor algorithm (KNN) are proposed in the following, and the coordinate mean value or the weighted coordinate mean value of K reference point positions is selected as the final estimated position, as shown in formula (3) and formula (4):
Figure BSA0000190654730000031
Figure BSA0000190654730000032
in equations (3) and (4), k is the number of selected neighboring reference points. Weight coefficient wjGenerally, the reference point can be determined by the magnitude of the euclidean distance value of each reference point, as shown in formula (5):
Figure BSA0000190654730000033
the performance of location fingerprint matching algorithms such as KNN and WKNN is mainly affected by the complexity of radio signal transmission in indoor environments. On one hand, radio signals of sensors such as Wi-Fi and Bluetooth can generate various diffraction, refraction and scattering effects due to shielding of objects such as walls, doors and windows, furniture and human bodies, so that unknown errors are brought to RSSI (received signal strength indicator) measurement results of users; on the other hand, the RSSI measurement background environments in the offline training stage and the user online positioning stage are not completely consistent due to the influence of personnel movement and other indoor object movement and transition. Therefore, when RSSI measurements of one or more access points in a location area are abnormal or have errors, large errors may be generated in the location fingerprint matching process, and ultimately the user location performance and the user experience may be affected.
Disclosure of Invention
In view of this, an embodiment of the present invention provides a fingerprint matching and positioning method, which is used to solve the problem that a large error is generated in the position fingerprint matching process due to an abnormal RSSI measurement value or an error of an access point.
In one aspect, an embodiment of the present invention provides a fingerprint matching and positioning method, where the method includes: step one, performing off-line training, collecting RSSI reference values and MAC addresses of access points at each reference point position of a positioning area, and storing an RSSI reference vector consisting of the RSSI reference values of each reference point position and the MAC addresses of each reference point position in a position fingerprint database; secondly, performing online positioning, searching wireless signals of each access point at a user terminal, obtaining an RSSI (received signal strength indicator) measurement value and an MAC (media access control) address of each access point, and obtaining a measurement vector consisting of the RSSI measurement value and the MAC address; selecting k reference point positions from the reference point positions, and respectively judging whether the dispersion between the RSSI measurement vector and the RSSI reference vector corresponding to each reference point position in the k reference point positions is within a threshold range and whether the number n of access points in the RSSI measurement vector and the RSSI reference vector is greater than a preset minimum number of access points, wherein k is greater than or equal to 2, and the preset minimum number of access points is greater than or equal to 3; step four, for the reference point position where the dispersion is not in the threshold range and the number n of access points in the RSSI measurement vector and the RSSI reference vector is greater than the preset minimum number of access points, calculating the difference value between the RSSI measurement value and the RSSI reference value of each common access point of the RSSI measurement vector and the RSSI reference vector, determining the access point with the maximum difference value as an abnormal access point, and eliminating the abnormal access point; step five, repeatedly executing the step three and the step four, and executing the step six when the dispersion is in the threshold range or the number of the remaining access points is less than or equal to the preset minimum number of the access points; and step six, executing a position fingerprint matching algorithm by using the MAC addresses and RSSI reference values of the rest access points of the k reference point positions after the abnormal access point is eliminated and the corresponding MAC addresses and RSSI measured values of the rest access points at the user terminal after the abnormal access point is eliminated, so as to obtain the coordinate position of the user mobile terminal.
According to some embodiments, wherein the first step further comprises: continuously collecting a certain number of instantaneous RSSI values for each access point at each reference point position, and carrying out mean value calculation on the instantaneous RSSI values so as to obtain the RSSI reference value of each reference point position.
According to some embodiments, wherein step three further comprises: by the formula
Figure BSA0000190654730000041
Calculating RSSI measurement vector R measured by user terminal at t momenttRSSI reference vector R with reference point location j in location fingerprint databasejDispersion between σ (R)t，Rj) Wherein j is 1,2, …, k; rj＝(RSSI1，j，RSSI2，j，…，RSSIn，j) Representing RSSI reference vectors from n access points received at reference point location j in the location fingerprint database; rt＝(RSSI1，RSSI2，…，RSSIn) The RSSI measurement vectors from n aps measured by the ue at time t are shown.
According to some embodiments, wherein in the sixth step, the location fingerprint matching algorithm is a combination of one or more of a neighbor method, a K-neighbor algorithm, and a weighted K-neighbor algorithm.
By utilizing the fingerprint matching and positioning method provided by the invention, the RSSI measured values of all APs participating in position fingerprint matching operation are detected by introducing a Fault Detection and Exclusion (FDE) theory, and abnormal measured values in the RSSI measured values are judged and eliminated, so that the influence caused by fluctuation or abnormality of the RSSI measured values of one or more access points can be effectively eliminated, and the precision and reliability of a position fingerprint matching and positioning result are improved.
Drawings
FIG. 1 is a schematic diagram of a location fingerprint matching method;
fig. 2 is a flow chart of a fingerprint matching positioning method according to the present invention.
Detailed Description
For a better understanding of the technical aspects of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings. Embodiments of the present disclosure are described in further detail below with reference to the figures and the detailed description, but the present disclosure is not limited thereto.
The use of "first," "second," and similar terms in this disclosure is not intended to indicate any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element preceding the word covers the element listed after the word, and does not exclude the possibility that other elements are also covered. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
All terms (including technical or scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs unless specifically defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
The invention provides a fingerprint matching and positioning method, which comprises the following steps:
step one, performing off-line training, collecting RSSI reference values and MAC addresses of access points at each reference point position of a positioning area, and storing an RSSI reference vector consisting of the RSSI reference values of each reference point position and the MAC addresses of each reference point position in a position fingerprint database;
secondly, performing online positioning, searching wireless signals of each access point at a user terminal, obtaining an RSSI (received signal strength indicator) measurement value and an MAC (media access control) address of each access point, and obtaining a measurement vector consisting of the RSSI measurement value and the MAC address;
selecting k reference point positions from the reference point positions, and respectively judging whether the dispersion between the RSSI measurement vector and the RSSI reference vector corresponding to each reference point position in the k reference point positions is within a threshold range and whether the number n of access points in the RSSI measurement vector and the RSSI reference vector is greater than a preset minimum number of access points, wherein for two-dimensional plane positioning, the number of the selected reference point positions is generally not less than 2, namely k is greater than or equal to 2, and the preset minimum number of access points is generally greater than or equal to 3;
step four, for the reference point position where the dispersion is not in the threshold range and the number n of access points in the RSSI measurement vector and the RSSI reference vector is greater than the preset minimum number of access points, calculating the difference value between the RSSI measurement value and the RSSI reference value of each common access point of the RSSI measurement vector and the RSSI reference vector, determining the access point with the maximum difference value as an abnormal access point, and eliminating the abnormal access point;
step five, repeatedly executing the step three and the step four, and executing the step six when the dispersion is in the threshold range or the number of the remaining access points is less than or equal to the preset minimum number of the access points;
and step six, executing a position fingerprint matching algorithm by using the MAC addresses and RSSI reference values of the rest access points of the k reference point positions after the abnormal access point is eliminated and the corresponding MAC addresses and RSSI measured values of the rest access points at the user terminal after the abnormal access point is eliminated, so as to obtain the coordinate position of the user mobile terminal.
Specifically, information interaction is performed between a user terminal and an AP in a wireless local area network such as WiFi or bluetooth through a communication connection. The AP which is in communication connection with the user terminal can be appointed, and if the AP is not appointed at a certain time, the user terminal selects the AP with the highest signal strength to establish communication connection by taking the signal strength of the nearby AP as a standard. However, unlike the way in which the user terminal associates with the AP to implement communication, in order to implement the location fingerprint matching and positioning function, the user terminal needs to detect and sense the signal strength of all APs in the area where the user terminal is located. In the IEEE-related protocol, the ue may use two modes, Passive Scanning (Passive Scanning) and Active Scanning (Active Scanning), to obtain the RSSI information of each access AP. The passive scan mode has an advantage in that power consumption can be reduced, but since the AP may be set not to transmit Beacons frames, the subscriber terminal may not be able to obtain RSSI information for all APs. For this reason, when positioning applications are performed, the active scanning mode is generally used for multiple options to acquire RSSI information.
In the active scanning mode, the ue will scan all radio channels. In each channel, the user terminal will send a Probe Request (Probe Request) frame in a broadcast manner. As the user terminal receives Probe Request (Probe Request) frames returned by APs operating on various channels, the active scanning process is finished, and the user terminal acquires all observable AP lists including MAC addresses and RSSI values of various APs required for positioning.
In the first step, the RSSI reference vector and the MAC address of each AP are mainly collected, so as to establish a location fingerprint database. This requires traversing all locations in the location area, collecting RSSI reference values from different APs at each reference point location, storing the multimedia connection (MAC) address and RSSI reference values of each AP in a location fingerprint database as a set. Preferably, data sampling can be performed for each reference point position (sampling point) at a certain distance (typically several meters) as a sampling interval. Further preferably, in order to eliminate the influence of personnel movement, operation of equipment with points, signal fluctuation and the like on the measured RSSI value, a certain number of instantaneous RSSI values are continuously collected at each reference point position and subjected to mean calculation, so as to obtain the RSSI reference value of each reference point position.
In the second step, the user terminal searches for wireless signals of each surrounding AP in the positioning area, and measures the RSSI measurement value and the MAC address of each AP in real time at the user terminal, where the RSSI measurement value of each AP may constitute an RSSI measurement vector (RSSI measurement value sequence) measured at the user terminal.
And in the third step, k reference point positions are selected, and whether the dispersion between the RSSI measurement vector measured at the user terminal and the RSSI reference vector corresponding to each reference point position in the k reference point positions is within a threshold value range or not is judged. Preferably, the RSSI measurement vector R measured by the ue at time t can be calculated by equation (6)tRSSI reference vector R with reference point location j (j ═ 1,2, …, k) in location fingerprint databasejDispersion between σ (R)t，Rj)
Figure BSA0000190654730000081
Wherein R isj＝(RSSI1，j，RSSI2，j，…，RSSIn，j) Representing RSSI reference vectors from n access points received at reference point location j in a location fingerprint database, where RSSI1，jRepresents the RSSI reference, RSSI, received from the 1 st access point at reference point location j in the location fingerprint database2，jRepresents the RSSI reference value from the 2 nd access point received at reference point position j in the location fingerprint database, and so on, the RSSIn，jRepresenting the RSSI reference value from the nth access point received at reference point location j in the location fingerprint database; rt＝(RSSI1，RSSI2，…，RSSIn) And the RSSI measurement vector is obtained by the user terminal at the time t and comes from n access points in total, wherein the RSSI1Represents the RSSI measured value from the 1 st access point measured by the user terminal at the time t, RSSI2Represents the RSSI measured value from the 2 nd access point measured by the user terminal at the time t, and so onnWhich represents the RSSI measurement from the nth ap measured by the ue at time t.
RSSI measurement vector R measured by user terminal at time ttWith the RSSI reference vector R of the reference point location j in the location fingerprint databasejDispersion between σ (R)t，Rj) As a testChecking the amount of the sample, determining whether the checking amount is within a predetermined threshold range, and RtAnd RjIs greater than a predetermined minimum number of access points, the threshold range may be determined according to the measurement accuracy of the APs and the actual test experience value, and may generally be several dBm to ten-odd dBm.
In step four, if the detected amount is not within the predetermined threshold range and RtAnd RjIf the number n of the APs in the set is larger than the preset minimum number of access points, calculating the difference between the RSSI measurement value and the RSSI reference value for each common access point of the RSSI measurement vector and the RSSI reference vector, confirming the access point with the maximum difference as an abnormal access point, and rejecting the abnormal access point. In general, the equation | R can be expressed byt(i)-Rj(i) I or (R)t(i)-Rj(i))2A difference between the RSSI measurement value and the RSSI reference value is calculated. Wherein R ist(i) Is RtRSSI measurements of the ith (i ═ 1,2, …, n) access point; rj(i) Is RjRSSI reference value of the ith (i ═ 1,2, …, n) access point.
In step five, the third step and the fourth step are repeatedly executed until the dispersion is within the threshold range or the number of remaining access points is less than or equal to a predetermined minimum number of access points.
In the sixth step, a position fingerprint matching algorithm is executed by using the MAC addresses and RSSI reference values of the remaining access points of the k reference point positions after the abnormal access point is eliminated and the MAC addresses and RSSI measured values of the remaining access points at the user terminal after the abnormal access point is eliminated. The position coordinates of the user terminal can be obtained by using a Neighbor method, a K-Nearest Neighbor (KNN) algorithm, a weighted K-Nearest Neighbor (WKNN) algorithm, and the like in the prior art. It should be noted that, for each of the k reference point locations, steps three to five will be performed separately, so that for different reference point locations, different abnormal access points may be rejected for the reference point location and the location at the user terminal. Thus in this step six, the remaining access points for each of the k reference point locations are the same as the remaining access points at the corresponding user terminal, but the remaining access points for different ones of the k reference point locations may be different. And performing a position fingerprint matching algorithm by using the MAC addresses and RSSI reference values of the rest access points of the k reference point positions and the MAC addresses and RSSI measurement values of the rest access points at the user terminal corresponding to the rest access points after the abnormal access points are eliminated.
By utilizing the fingerprint matching and positioning method provided by the invention, the RSSI measured values of all APs participating in position fingerprint matching operation are detected by introducing a Fault Detection and Exclusion (FDE) theory, and abnormal measured values in the RSSI measured values are judged and eliminated, so that the influence caused by fluctuation or abnormality of the RSSI measured values of one or more APs can be effectively eliminated, and the precision and reliability of a position fingerprint matching and positioning result are improved.
While the embodiments of the present invention have been described in detail, the present invention is not limited to these specific embodiments, and those skilled in the art can make various modifications and modifications of the embodiments based on the concept of the present invention, which fall within the scope of the present invention as claimed.

Claims (3)

1. A fingerprint matching positioning method, the method comprising:
step one, performing off-line training, collecting RSSI reference values and MAC addresses of access points at each reference point position of a positioning area, and storing RSSI reference vectors formed by the RSSI reference values of the reference point positions and the MAC address reference values of the reference point positions in a position fingerprint database;
secondly, performing online positioning, searching wireless signals of each access point at a user terminal, obtaining an RSSI (received signal strength indicator) measurement value and an MAC (media access control) address of each access point, and obtaining a measurement vector consisting of the RSSI measurement value and the MAC address;
selecting k reference point positions from the reference point positions, and respectively judging whether the dispersion between the RSSI measurement vector and the RSSI reference vector corresponding to each reference point position in the k reference point positions is within a threshold range and whether the number n of access points in the RSSI measurement vector and the RSSI reference vector is greater than a preset minimum number of access points, wherein k is greater than or equal to 2, and the preset minimum number of access points is greater than or equal to 3;
step four, for the reference point position where the dispersion is not in the threshold range and the number n of access points in the RSSI measurement vector and the RSSI reference vector is greater than the preset minimum number of access points, calculating the difference value between the RSSI measurement value and the RSSI reference value of each common access point of the RSSI measurement vector and the RSSI reference vector, determining the access point with the maximum difference value as an abnormal access point, and eliminating the abnormal access point;
step five, repeatedly executing the step three and the step four, and executing the step six when the discrete degree is in the threshold range or the number of the remaining access points is less than or equal to the preset minimum number of the access points;
step six, using the MAC addresses and RSSI reference values of the rest access points of the k reference point positions after the abnormal access points are eliminated and the corresponding MAC addresses and RSSI measured values of the rest access points at the user terminal after the abnormal access points are eliminated to execute a position fingerprint matching algorithm to obtain the coordinate position of the user mobile terminal,
wherein the third step further comprises:
by the formula
Figure FDA0002886721120000021
Calculating RSSI measurement vector R measured by user terminal at t momenttRSSI reference vector R with reference point location j in location fingerprint databasejDispersion between σ (R)t,Rj) Wherein j is 1,2, …, k; rj＝(RSSI1,j,RSSI2,j,…,RSSIn,j) Representing RSSI reference vectors from n access points received at reference point location j in the location fingerprint database; rt＝(RSSI1,RSSI2,…,RSSIn) The RSSI measurement vectors from n aps measured by the ue at time t are shown.
2. The fingerprint matching positioning method according to claim 1, wherein said step one further comprises:
continuously collecting a certain number of instantaneous RSSI values for each access point at each reference point position, and carrying out mean value calculation on the instantaneous RSSI values so as to obtain the RSSI reference value of each reference point position.
3. The fingerprint matching positioning method according to claim 1, wherein in said step six:
the position fingerprint matching algorithm is one or more of a combination of a neighbor method, a K-neighbor algorithm and a weighted K-neighbor algorithm.
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