CN111182460A - Hybrid indoor positioning method and device, computer equipment and storage medium - Google Patents
Hybrid indoor positioning method and device, computer equipment and storage medium Download PDFInfo
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- CN111182460A CN111182460A CN202010044778.8A CN202010044778A CN111182460A CN 111182460 A CN111182460 A CN 111182460A CN 202010044778 A CN202010044778 A CN 202010044778A CN 111182460 A CN111182460 A CN 111182460A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/80—Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
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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
Abstract
The embodiment of the invention discloses a hybrid indoor positioning method, a hybrid indoor positioning device, computer equipment and a storage medium. The method comprises the following steps: establishing a fingerprint database; if the terminal receives a positioning request sent by a user, judging whether all gateway routes can receive signals sent by ibeacon equipment of the terminal; if all gateway routes can receive signals sent by the ibeacon equipment of the terminal, acquiring a first to-be-detected RSSI vector of the terminal, wherein the component of the first to-be-detected RSSI vector is the RSSI of the signals sent by the ibeacon equipment of the terminal and received by each gateway route; inputting the first RSSI vector to be measured into a preset ANN model, and taking an output result of the ANN model as a first prediction coordinate of the terminal; acquiring a second prediction coordinate of the terminal according to the first RSSI vector to be measured and a preset KNN algorithm; and determining the position of the terminal according to the first prediction coordinate and the second prediction coordinate, and sending the position of the terminal to the terminal. The scheme has low cost and high positioning precision.
Description
Technical Field
The present invention relates to the field of indoor positioning technologies, and in particular, to a hybrid indoor positioning method and apparatus, a computer device, and a storage medium.
Background
With the rapid increase of data services and media services, the demands of people on positioning and navigation are increasing day by day, and the birth of GPS positioning is a benchmark started in the modern positioning technology era. However, the GPS positioning has large interference and large error in an indoor environment, and the positioning precision is difficult to meet the actual requirement.
Therefore, positioning solutions related to indoor aspects are emerging, such as ultrasonic positioning, bluetooth positioning, infrared positioning, and radio frequency identification positioning.
The key of indoor positioning is positioning according to the signal difference of known point location beacons, and the core algorithm comprises trilateral positioning, three-point positioning, triangular positioning, hyperbolic positioning, elliptic positioning and the like. Some high performance positioning schemes employ UWB or AOA techniques, however these methods are limited by the high cost of the corresponding equipment and applications.
ibeacon is a low power consumption bluetooth technology, and RSSI (Received Signal strength indication) is a positioning algorithm based on the relation between distance and Signal strength. The intensity RSSI of the signal transmitted by the iBeacon is positioned through the Bluetooth equipment to receive the feedback signal, and then the positioning can be carried out. This positioning method has cost advantages but positioning accuracy is not as good as UWB and AOA techniques.
Disclosure of Invention
The embodiment of the invention provides a hybrid indoor positioning method and device, computer equipment and a storage medium, and aims to solve the problems of low indoor positioning accuracy and high cost in the prior art.
In a first aspect, an embodiment of the present invention provides a hybrid indoor positioning method, which includes:
establishing a fingerprint library, wherein the fingerprint library comprises RSSI vectors of reference points, and the components of the RSSI vectors are the RSSI of signals sent by beacon equipment at the reference points and received by each gateway route and the coordinates of the reference points;
if the terminal receives a positioning request sent by a user, judging whether all gateway routes can receive signals sent by ibeacon equipment of the terminal;
if all gateway routes can receive signals sent by the ibeacon equipment of the terminal, acquiring a first to-be-detected RSSI vector of the terminal, wherein the component of the first to-be-detected RSSI vector is the RSSI of the signals sent by the ibeacon equipment of the terminal and received by each gateway route;
inputting the first RSSI vector to be measured into a preset ANN model, and taking an output result of the ANN model as a first prediction coordinate of a terminal, wherein the ANN model is trained by data in the fingerprint database in advance;
acquiring a second prediction coordinate of the terminal according to the first RSSI vector to be measured and a preset KNN algorithm;
and determining the position of the terminal according to the first prediction coordinate and the second prediction coordinate, and sending the position of the terminal to the terminal.
In a second aspect, an embodiment of the present invention further provides a hybrid indoor positioning apparatus, including:
the system comprises an establishing unit, a fingerprint database and a processing unit, wherein the fingerprint database comprises RSSI vectors of reference points, and the components of the RSSI vectors are the RSSI of signals sent by beacon equipment at the reference points and received by each gateway route and the coordinates of the reference points;
the terminal comprises a judging unit and a judging unit, wherein the judging unit is used for judging whether all gateway routes can receive signals sent by ibeacon equipment of the terminal if the terminal receives a positioning request sent by a user;
a first obtaining unit, configured to obtain a first to-be-measured RSSI vector of a terminal if all gateway routes can receive a signal sent by an ibeacon device of the terminal, where a component of the first to-be-measured RSSI vector is an RSSI of a signal sent by the ibeacon device of the terminal and received by each gateway route;
the input unit is used for inputting the first RSSI vector to be measured into a preset ANN model, and taking an output result of the ANN model as a first prediction coordinate of a terminal, wherein the ANN model is trained by data in the fingerprint database in advance;
the second obtaining unit is used for obtaining a second prediction coordinate of the terminal according to the first RSSI vector to be measured and a preset KNN algorithm;
and the sending unit is used for determining the position of the terminal according to the first predicted coordinate and the second predicted coordinate and sending the position of the terminal to the terminal.
In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the above method when executing the computer program.
In a fourth aspect, the present invention also provides a computer-readable storage medium, which stores a computer program, and the computer program can implement the above method when being executed by a processor.
The invention combines the neural network of deep learning and the standard indoor positioning fingerprint KNN algorithm to obtain a high-precision low-cost hybrid indoor positioning algorithm. The hybrid algorithm is applied to ibeacon's RSSI data and is therefore low cost (1/5 of the prior art). Meanwhile, the accuracy of the mixed algorithm is obviously higher than that of the traditional algorithm (such as trilateration or single fingerprint KNN) using RSSI positioning.
During real-time positioning, the invention provides a dynamic routing KNN model for flexible routing antenna conversion, so that a hybrid positioning algorithm can deal with partial gateway routing faults or a state that a signal cannot be effectively received at a longer distance, and still can stably position and ensure higher positioning precision.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a hybrid indoor positioning method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of matrix H;
fig. 3 is a schematic diagram of an indoor positioning front-end and background hardware system according to an embodiment of the present invention;
fig. 4 is a test result diagram of a hybrid indoor positioning method according to the present invention;
FIG. 5 is a schematic block diagram of a computer apparatus provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Referring to fig. 1, fig. 1 is a flow chart illustrating a hybrid indoor positioning method according to an embodiment of the present invention. As shown, the method includes the following steps S1-S6.
S1, establishing a fingerprint library, wherein the fingerprint library comprises RSSI vectors of reference points, and the components of the RSSI vectors are the RSSI of signals sent by beacon equipment at the reference points received by each gateway route and the coordinates of the reference points.
KNN is mainly predicted based on a sampled database, also known as fingerprint library in the field of indoor positioning and hence also known as location fingerprinting technology.
In the embodiment of the invention, a fingerprint database is established first. The fingerprint library comprises RSSI vectors of reference points, and the components of the RSSI vectors are the RSSI of signals sent by beacon equipment at the reference points and received by each gateway route and the coordinates of the reference points. beacon equipment is the equipment that sends out the signal. RSSI (Received Signal Strength Indication) is used to characterize Signal Strength.
In an embodiment, the step S1 specifically includes: acquiring the RSSI of a signal sent by beacon equipment at a reference point received by each gateway route; and storing the RSSI of the signal sent by the beacon equipment at the reference point and the position coordinate of the reference point, which are received by each gateway route, into a preset matrix as the RSSI vector of the reference point.
In specific implementation, a gateway route is arranged at a known key position (such as four corners of a square house) in an indoor positioning space, and the gateway route can receive the signal strength RSSI sent by beacon equipment in the area.
And selecting MK reference points in the indoor positioning area, acquiring RSSI of each gateway route in a signal coverage area at each reference point, and storing the geographical coordinates of the reference points into a matrix H.
The matrix H is shown in fig. 2, and in fig. 2, M is a serial number of a reference point; b isMjRSSI of the jth gateway route collected at the reference point; x is the number ofMIs the abscissa at reference point M; y is_MIs the ordinate at reference point M.
S2, if the terminal receives the location request sent by the user, it determines whether all gateway routes can receive the signal sent by the ibeacon device of the terminal.
In a specific implementation, if the terminal receives a positioning request sent by a user, it is determined whether all gateway routes can receive a signal sent by an ibeacon device of the terminal.
S3, if all gateway routes can receive the signal sent by the ibeacon device of the terminal, obtaining a first RSSI vector to be measured of the terminal, where a component of the first RSSI vector to be measured is an RSSI of the signal sent by the ibeacon device of the terminal and received by each gateway route.
In specific implementation, if all gateway routes can receive signals sent by ibeacon equipment of a terminal, a KNN + ANN hybrid indoor positioning method of a fixed route is adopted.
First, a first to-be-measured RSSI vector of a terminal is obtained, and a component of the first to-be-measured RSSI vector is an RSSI of a signal sent by an ibeacon device of the terminal and received by each gateway route. Specifically, the RSSI of a signal sent by the ibeacon device of the terminal and received by each gateway route is obtained, and a first RSSI vector to be measured is determined according to the obtained RSSI.
And S4, inputting the first RSSI vector to be measured into a preset ANN model, and taking the output result of the ANN model as a first prediction coordinate of the terminal, wherein the ANN model is trained by data in the fingerprint database in advance.
In specific implementation, the first to-be-measured RSSI vector is input into a preset ANN model, and an output result of the ANN model is used as a first predicted coordinate of the terminal, wherein the ANN model is trained by data in the fingerprint database in advance.
It should be noted that the ANN is a computing unit that simulates biological neurons for signal processing and transmission, and has the ability to fit functions and perform approximate estimation. Artificial neural networks are computed from a large number of artificial neuron connections. The ANN can be enabled to have the function of predicting the position of the terminal according to the first RSSI vector to be measured by training data in the fingerprint database.
And S5, acquiring a second prediction coordinate of the terminal according to the first RSSI vector to be measured and a preset KNN algorithm.
In specific implementation, a second prediction coordinate of the terminal is obtained according to the first RSSI vector to be measured and a preset KNN algorithm.
In an embodiment, the step S5 specifically includes:
and S51, respectively calculating Euclidean distances between the first RSSI vector to be measured and RSSI vectors of all reference points in the fingerprint database.
In specific implementation, the following formula is used
Calculating Euclidean distance between the first RSSI vector to be measured and RSSI vectors of all reference points in a fingerprint database, wherein BMIs a component of the RSSI vector, bMIs a component of the first RSSI vector to be measured.
And S52, selecting a preset number of reference points as target reference points according to the sequence of the Euclidean distance from small to large.
In a specific implementation, a preset number (for example, 3) of reference points are selected as the target reference points in the descending order of the euclidean distance.
And S53, acquiring a coordinate mean value of each target reference point, and taking the coordinate mean value as the second prediction coordinate.
In specific implementation, a coordinate mean value of each target reference point is obtained, and the coordinate mean value is used as the second prediction coordinate.
And S6, determining the position of the terminal according to the first predicted coordinate and the second predicted coordinate, and sending the position of the terminal to the terminal.
In specific implementation, the position of the terminal is determined according to the first prediction coordinate and the second prediction coordinate, and the position of the terminal is sent to the terminal, so that the position of the terminal is positioned.
In an embodiment, the step S6 specifically includes: and taking the average value of the first prediction coordinate and the second prediction coordinate as the position coordinate of the terminal.
With continued reference to fig. 1, in one embodiment, the method further comprises:
s7, if not all gateway routes can receive signals sent by ibeacon equipment of the terminal, selecting a preset number of RSSI from the RSSI of the gateway routes which can receive the signals sent by beacon equipment according to the sequence from large to small, determining a second RSSI vector to be detected according to the selected RSSI, and acquiring a third position coordinate of the terminal according to the second RSSI vector to be detected and a preset KNN algorithm.
In specific implementation, if not all gateway routes can receive signals sent by ibeacon equipment of the terminal, a way of dynamically selecting a route is adopted, and an indoor positioning method based on KNN is selected. Specifically, a preset number of RSSIs are selected from the RSSIs of the gateway route capable of receiving the signal sent by the beacon device in a descending order, a second RSSI vector to be detected is determined according to the selected RSSIs, and a third position coordinate of the terminal is obtained according to the second RSSI vector to be detected and a preset KNN algorithm.
It should be noted that the preset number can be determined by those skilled in the art according to practical situations, and usually, the preset number is less than or equal to the total number of routes, but needs to be greater than 3, for example, the preset number can be set to 5.
S8, filtering the third position coordinate to obtain the position coordinate of the terminal, wherein the filtering is KALMAN filtering or weight filtering.
in a specific embodiment, the Final output is represented by the following formula α (x)t,yt)+(1-α)(xt-1,yt-1) And (6) carrying out filtering processing.
Wherein x ist、ytCoordinates, x, predicted for the execution of the algorithm at the current momentt-1、yt-1for the coordinates predicted by the algorithm at the previous time, α is a smoothing weight, and is generally set to a value between 0 and 1, with lower values being more stable but with higher delays.
The invention combines the neural network of deep learning and the standard indoor positioning fingerprint KNN algorithm to obtain a high-precision low-cost hybrid indoor positioning algorithm. The hybrid algorithm is applied to the RSSI data of IBEACON, so the cost is low (1/5 of the prior art). Meanwhile, the accuracy of the mixed algorithm is obviously higher than that of the traditional algorithm (such as trilateration or single fingerprint KNN) using RSSI positioning.
During real-time positioning, the invention provides a dynamic routing KNN model for flexible routing antenna conversion, so that a hybrid positioning algorithm can deal with partial gateway routing faults or a state that a signal cannot be effectively received at a longer distance, and still can stably position and ensure higher positioning precision.
Referring to fig. 3, the method is laid out in an indoor exhibition hall environment with a size of 8m X10 m, wherein 7 gateways are laid out in the centers of two sides of four corners in the environment, the gateways send signals to a background, the background arranges the signals into structured data in an input format and then sends a positioning request to an algorithm, the algorithm receives the input and calculates positioning coordinates, and the background receives the positioning coordinates (x, y) returned by the algorithm and returns the positioning coordinates to a front-end user through logic smoothing processing.
The positioning result is shown in the following fig. 4, both the average positioning accuracy and the maximum error are significantly better than the scheme (positioning accuracy is 1-5m) based on RSSI positioning only in the same type in the market, so that the method has the advantages of price and positioning accuracy.
Corresponding to the mixed indoor positioning method, the invention also provides a mixed indoor positioning device. The hybrid indoor positioning device comprises a unit for executing the hybrid indoor positioning method, and the device can be configured in a desktop computer, a tablet computer, a portable computer, and other terminals. Specifically, the hybrid indoor positioning device includes:
the system comprises an establishing unit, a fingerprint database and a processing unit, wherein the fingerprint database comprises RSSI vectors of reference points, and the components of the RSSI vectors are the RSSI of signals sent by beacon equipment at the reference points and received by each gateway route and the coordinates of the reference points;
the terminal comprises a judging unit and a judging unit, wherein the judging unit is used for judging whether all gateway routes can receive signals sent by ibeacon equipment of the terminal if the terminal receives a positioning request sent by a user;
a first obtaining unit, configured to obtain a first to-be-measured RSSI vector of a terminal if all gateway routes can receive a signal sent by an ibeacon device of the terminal, where a component of the first to-be-measured RSSI vector is an RSSI of a signal sent by the ibeacon device of the terminal and received by each gateway route;
the input unit is used for inputting the first RSSI vector to be measured into a preset ANN model, and taking an output result of the ANN model as a first prediction coordinate of a terminal, wherein the ANN model is trained by data in the fingerprint database in advance;
the second obtaining unit is used for obtaining a second prediction coordinate of the terminal according to the first RSSI vector to be measured and a preset KNN algorithm;
and the sending unit is used for determining the position of the terminal according to the first predicted coordinate and the second predicted coordinate and sending the position of the terminal to the terminal.
In one embodiment, the hybrid indoor positioning apparatus further comprises:
the screening unit is used for selecting a preset number of RSSI (received signal strength indicator) from the RSSI of the gateway route which can receive the signal sent by the beacon equipment according to the sequence from large to small if all the gateway routes can not receive the signal sent by the ibeacon equipment of the terminal, determining a second RSSI vector to be detected according to the selected RSSI vector, and acquiring a third position coordinate of the terminal according to the second RSSI vector to be detected and a preset KNN (K nearest neighbor) algorithm;
and the filtering unit is used for carrying out filtering processing on the third position coordinate to obtain the position coordinate of the terminal, and the filtering processing is KALMAN filtering processing or weight filtering processing.
In one embodiment, the establishing unit includes:
the third acquisition unit is used for acquiring the RSSI of the signal sent by beacon equipment at the reference point received by each gateway route;
and the storage unit is used for storing the RSSI of the signal sent by the beacon equipment and received by each gateway route at the reference point and the position coordinate of the reference point as the RSSI vector of the reference point into a preset matrix.
In one embodiment, the second obtaining unit includes:
the calculating unit is used for calculating Euclidean distances between the first RSSI vector to be measured and RSSI vectors of all reference points in a fingerprint database respectively;
the sorting unit is used for selecting a preset number of reference points as target reference points according to the sequence of the Euclidean distances from small to large;
and the fourth acquisition unit is used for acquiring the coordinate mean value of each target reference point and taking the coordinate mean value as the second prediction coordinate.
In one embodiment, the sending unit includes:
and the averaging unit is used for taking the average value of the first prediction coordinate and the second prediction coordinate as the position coordinate of the terminal.
It should be noted that, as will be clear to those skilled in the art, the concrete implementation process of the hybrid indoor positioning apparatus and each unit may refer to the corresponding description in the foregoing method embodiment, and for convenience and brevity of description, no further description is provided herein.
The hybrid indoor positioning apparatus described above may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 5.
Referring to fig. 5, fig. 5 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a terminal or a server, where the terminal may be an electronic device with a communication function, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, and a wearable device. The server may be an independent server or a server cluster composed of a plurality of servers.
Referring to fig. 5, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, causes the processor 502 to perform a hybrid indoor positioning method.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for running the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 may be enabled to execute a hybrid indoor positioning method.
The network interface 505 is used for network communication with other devices. Those skilled in the art will appreciate that the configuration shown in fig. 5 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation of the computer device 500 to which the present application may be applied, and that a particular computer device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
Wherein the processor 502 is configured to run the computer program 5032 stored in the memory to implement the following steps:
establishing a fingerprint library, wherein the fingerprint library comprises RSSI vectors of reference points, and the components of the RSSI vectors are the RSSI of signals sent by beacon equipment at the reference points and received by each gateway route and the coordinates of the reference points;
if the terminal receives a positioning request sent by a user, judging whether all gateway routes can receive signals sent by ibeacon equipment of the terminal;
if all gateway routes can receive signals sent by the ibeacon equipment of the terminal, acquiring a first to-be-detected RSSI vector of the terminal, wherein the component of the first to-be-detected RSSI vector is the RSSI of the signals sent by the ibeacon equipment of the terminal and received by each gateway route;
inputting the first RSSI vector to be measured into a preset ANN model, and taking an output result of the ANN model as a first prediction coordinate of a terminal, wherein the ANN model is trained by data in the fingerprint database in advance;
acquiring a second prediction coordinate of the terminal according to the first RSSI vector to be measured and a preset KNN algorithm;
and determining the position of the terminal according to the first prediction coordinate and the second prediction coordinate, and sending the position of the terminal to the terminal.
In one embodiment, processor 502 further implements the steps of:
if not, all gateway routes can receive signals sent by ibeacon equipment of the terminal, selecting a preset number of RSSI from the RSSI of the gateway routes which can receive the signals sent by beacon equipment according to the sequence from large to small, determining a second RSSI vector to be detected according to the selected RSSI, and acquiring a third position coordinate of the terminal according to the second RSSI vector to be detected and a preset KNN algorithm;
and filtering the third position coordinate to obtain the position coordinate of the terminal, wherein the filtering is KALMAN filtering or weight filtering.
In an embodiment, when the processor 502 implements the step of establishing the fingerprint database, the following steps are specifically implemented:
acquiring the RSSI of a signal sent by beacon equipment at a reference point received by each gateway route;
and storing the RSSI of the signal sent by the beacon equipment at the reference point and the position coordinate of the reference point, which are received by each gateway route, into a preset matrix as the RSSI vector of the reference point.
In an embodiment, when the step of obtaining the second predicted coordinate of the terminal according to the first RSSI vector to be measured and the preset KNN algorithm is implemented, the processor 502 specifically implements the following steps:
respectively calculating Euclidean distances between the first RSSI vector to be measured and RSSI vectors of all reference points in a fingerprint database;
selecting a preset number of reference points as target reference points according to the sequence of the Euclidean distances from small to large;
and acquiring a coordinate mean value of each target reference point, and taking the coordinate mean value as the second prediction coordinate.
In an embodiment, when the processor 502 implements the step of determining the location of the terminal according to the first predicted coordinate and the second predicted coordinate, the following steps are specifically implemented:
and taking the average value of the first prediction coordinate and the second prediction coordinate as the position coordinate of the terminal.
It should be understood that, in the embodiment of the present Application, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It will be understood by those skilled in the art that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program instructing associated hardware. The computer program may be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
Accordingly, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program. The computer program, when executed by a processor, causes the processor to perform the steps of:
establishing a fingerprint library, wherein the fingerprint library comprises RSSI vectors of reference points, and the components of the RSSI vectors are the RSSI of signals sent by beacon equipment at the reference points and received by each gateway route and the coordinates of the reference points;
if the terminal receives a positioning request sent by a user, judging whether all gateway routes can receive signals sent by ibeacon equipment of the terminal;
if all gateway routes can receive signals sent by the ibeacon equipment of the terminal, acquiring a first to-be-detected RSSI vector of the terminal, wherein the component of the first to-be-detected RSSI vector is the RSSI of the signals sent by the ibeacon equipment of the terminal and received by each gateway route;
inputting the first RSSI vector to be measured into a preset ANN model, and taking an output result of the ANN model as a first prediction coordinate of a terminal, wherein the ANN model is trained by data in the fingerprint database in advance;
acquiring a second prediction coordinate of the terminal according to the first RSSI vector to be measured and a preset KNN algorithm;
and determining the position of the terminal according to the first prediction coordinate and the second prediction coordinate, and sending the position of the terminal to the terminal.
In an embodiment, the processor, in executing the computer program, further implements the steps of:
if not, all gateway routes can receive signals sent by ibeacon equipment of the terminal, selecting a preset number of RSSI from the RSSI of the gateway routes which can receive the signals sent by beacon equipment according to the sequence from large to small, determining a second RSSI vector to be detected according to the selected RSSI, and acquiring a third position coordinate of the terminal according to the second RSSI vector to be detected and a preset KNN algorithm;
and filtering the third position coordinate to obtain the position coordinate of the terminal, wherein the filtering is KALMAN filtering or weight filtering.
In an embodiment, when the processor executes the computer program to implement the step of establishing the fingerprint database, the following steps are specifically implemented:
acquiring the RSSI of a signal sent by beacon equipment at a reference point received by each gateway route;
and storing the RSSI of the signal sent by the beacon equipment at the reference point and the position coordinate of the reference point, which are received by each gateway route, into a preset matrix as the RSSI vector of the reference point.
In an embodiment, when the processor executes the computer program to implement the step of obtaining the second predicted coordinate of the terminal according to the first RSSI vector to be measured and the preset KNN algorithm, the following steps are specifically implemented:
respectively calculating Euclidean distances between the first RSSI vector to be measured and RSSI vectors of all reference points in a fingerprint database;
selecting a preset number of reference points as target reference points according to the sequence of the Euclidean distances from small to large;
and acquiring a coordinate mean value of each target reference point, and taking the coordinate mean value as the second prediction coordinate.
In an embodiment, when the processor executes the computer program to implement the step of determining the location of the terminal according to the first predicted coordinate and the second predicted coordinate, the processor specifically implements the following steps:
and taking the average value of the first prediction coordinate and the second prediction coordinate as the position coordinate of the terminal.
The storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, which can store various computer readable storage media.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.
The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be merged, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, while the invention has been described with respect to the above-described embodiments, it will be understood that the invention is not limited thereto but may be embodied with various modifications and changes.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A hybrid indoor positioning method, comprising:
establishing a fingerprint library, wherein the fingerprint library comprises RSSI vectors of reference points, and the components of the RSSI vectors are the RSSI of signals sent by beacon equipment at the reference points and received by each gateway route and the coordinates of the reference points;
if the terminal receives a positioning request sent by a user, judging whether all gateway routes can receive signals sent by ibeacon equipment of the terminal;
if all gateway routes can receive signals sent by the ibeacon equipment of the terminal, acquiring a first to-be-detected RSSI vector of the terminal, wherein the component of the first to-be-detected RSSI vector is the RSSI of the signals sent by the ibeacon equipment of the terminal and received by each gateway route;
inputting the first RSSI vector to be measured into a preset ANN model, and taking an output result of the ANN model as a first prediction coordinate of a terminal, wherein the ANN model is trained by data in the fingerprint database in advance;
acquiring a second prediction coordinate of the terminal according to the first RSSI vector to be measured and a preset KNN algorithm;
and determining the position of the terminal according to the first prediction coordinate and the second prediction coordinate, and sending the position of the terminal to the terminal.
2. The hybrid indoor positioning method of claim 1, further comprising:
if not, all gateway routes can receive signals sent by ibeacon equipment of the terminal, selecting a preset number of RSSI from the RSSI of the gateway routes which can receive the signals sent by beacon equipment according to the sequence from large to small, determining a second RSSI vector to be detected according to the selected RSSI, and acquiring a third position coordinate of the terminal according to the second RSSI vector to be detected and a preset KNN algorithm;
and filtering the third position coordinate to obtain the position coordinate of the terminal, wherein the filtering is KALMAN filtering or weight filtering.
3. The hybrid indoor positioning method as claimed in claim 1, wherein the creating a fingerprint library comprises:
acquiring the RSSI of a signal sent by beacon equipment at a reference point received by each gateway route;
and storing the RSSI of the signal sent by the beacon equipment at the reference point and the position coordinate of the reference point, which are received by each gateway route, into a preset matrix as the RSSI vector of the reference point.
4. The hybrid indoor positioning method as claimed in claim 1, wherein the obtaining a second predicted coordinate of the terminal according to the first RSSI vector to be measured and a preset KNN algorithm comprises:
respectively calculating Euclidean distances between the first RSSI vector to be measured and RSSI vectors of all reference points in a fingerprint database;
selecting a preset number of reference points as target reference points according to the sequence of the Euclidean distances from small to large;
and acquiring a coordinate mean value of each target reference point, and taking the coordinate mean value as the second prediction coordinate.
5. The hybrid indoor positioning method as claimed in claim 1, wherein the determining the location of the terminal according to the first predicted coordinate and the second predicted coordinate comprises:
and taking the average value of the first prediction coordinate and the second prediction coordinate as the position coordinate of the terminal.
6. A hybrid indoor positioning apparatus, comprising:
the system comprises an establishing unit, a fingerprint database and a processing unit, wherein the fingerprint database comprises RSSI vectors of reference points, and the components of the RSSI vectors are the RSSI of signals sent by beacon equipment at the reference points and received by each gateway route and the coordinates of the reference points;
the terminal comprises a judging unit and a judging unit, wherein the judging unit is used for judging whether all gateway routes can receive signals sent by ibeacon equipment of the terminal if the terminal receives a positioning request sent by a user;
a first obtaining unit, configured to obtain a first to-be-measured RSSI vector of a terminal if all gateway routes can receive a signal sent by an ibeacon device of the terminal, where a component of the first to-be-measured RSSI vector is an RSSI of a signal sent by the ibeacon device of the terminal and received by each gateway route;
the input unit is used for inputting the first RSSI vector to be measured into a preset ANN model, and taking an output result of the ANN model as a first prediction coordinate of a terminal, wherein the ANN model is trained by data in the fingerprint database in advance;
the second obtaining unit is used for obtaining a second prediction coordinate of the terminal according to the first RSSI vector to be measured and a preset KNN algorithm;
and the sending unit is used for determining the position of the terminal according to the first predicted coordinate and the second predicted coordinate and sending the position of the terminal to the terminal.
7. The hybrid indoor positioning apparatus of claim 6, further comprising:
the screening unit is used for selecting a preset number of RSSI (received signal strength indicator) from the RSSI of the gateway route which can receive the signal sent by the beacon equipment according to the sequence from large to small if all the gateway routes can not receive the signal sent by the ibeacon equipment of the terminal, determining a second RSSI vector to be detected according to the selected RSSI vector, and acquiring a third position coordinate of the terminal according to the second RSSI vector to be detected and a preset KNN (K nearest neighbor) algorithm;
and the filtering unit is used for carrying out filtering processing on the third position coordinate to obtain the position coordinate of the terminal, and the filtering processing is KALMAN filtering processing or weight filtering processing.
8. The hybrid indoor positioning apparatus of claim 6, wherein the establishing unit comprises:
the third acquisition unit is used for acquiring the RSSI of the signal sent by beacon equipment at the reference point received by each gateway route;
and the storage unit is used for storing the RSSI of the signal sent by the beacon equipment and received by each gateway route at the reference point and the position coordinate of the reference point as the RSSI vector of the reference point into a preset matrix.
9. A computer arrangement, characterized in that the computer arrangement comprises a memory having stored thereon a computer program and a processor implementing the method according to any of claims 1-5 when executing the computer program.
10. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when being executed by a processor, is adapted to carry out the method according to any one of claims 1-5.
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