CN112188614B - Indoor positioning method and equipment - Google Patents
Indoor positioning method and equipment Download PDFInfo
- Publication number
- CN112188614B CN112188614B CN202010960446.4A CN202010960446A CN112188614B CN 112188614 B CN112188614 B CN 112188614B CN 202010960446 A CN202010960446 A CN 202010960446A CN 112188614 B CN112188614 B CN 112188614B
- Authority
- CN
- China
- Prior art keywords
- floor
- floors
- value
- determining
- positioning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000001514 detection method Methods 0.000 claims abstract description 42
- 238000012360 testing method Methods 0.000 claims abstract description 42
- 230000006870 function Effects 0.000 claims description 30
- 238000007689 inspection Methods 0.000 claims description 28
- 238000001914 filtration Methods 0.000 claims description 9
- 230000007423 decrease Effects 0.000 claims description 2
- 230000007547 defect Effects 0.000 abstract description 4
- 230000009191 jumping Effects 0.000 abstract 1
- 238000012353 t test Methods 0.000 description 12
- 238000000692 Student's t-test Methods 0.000 description 7
- 239000002537 cosmetic Substances 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000002829 reductive effect Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000000540 analysis of variance Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000002238 attenuated effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 210000002837 heart atrium Anatomy 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000002427 irreversible effect Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000036961 partial effect Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
Images
Classifications
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/318—Received signal strength
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The application discloses an indoor positioning method and equipment, which comprises the steps that a positioning node receives a plurality of signal strength RSSI values corresponding to each floor in a plurality of floors; extracting a plurality of RSSI values of a preset number in each floor within a preset time period at each preset time interval; determining a detection floor set corresponding to the positioning node according to a plurality of RSSI values extracted from each floor; according to a plurality of RSSI values corresponding to all the detection floors in the detection floor set, carrying out significance detection on the corresponding detection floors and determining positioned floors corresponding to the positioning nodes; and determining the position coordinates of the positioning nodes according to a minimum objective function algorithm based on the position coordinates of the target anchor points with the strongest RSSI values in the positioned floors. The invention realizes the determination of the positioned floor through the significance test, overcomes the defects of the jumping floor, utilizes the position information and the signal intensity value of a plurality of anchor points through minimizing the target function and improves the positioning precision.
Description
Technical Field
The present application relates to the field of wireless communication technologies, and in particular, to an indoor positioning method and device.
Background
Indoor positioning refers to the realization of position positioning in an indoor environment, and mainly adopts multiple technologies such as wireless communication, base station positioning, inertial navigation positioning and the like to form an indoor position positioning system. The indoor wireless positioning method mainly comprises a wireless network (Wi-Fi), Bluetooth, Radio Frequency Identification (RFID), a ZigBee and the like.
The RSSI value of the wireless signal received by the terminal is influenced by the complex indoor environment, so that great fluctuation exists, and the calculation of the indoor accurate position by the terminal is influenced. Therefore, the existing positioning algorithm based on the RSSI value has low precision, so that the precision of the whole positioning system is low, and the reliability of the whole positioning system is influenced.
Disclosure of Invention
The embodiment of the application provides an indoor positioning method and equipment, and solves the problem that an existing indoor positioning system based on RSSI (received signal strength indicator) is low in positioning accuracy.
In one aspect, an embodiment of the present application provides an indoor positioning method, including: the positioning node receives a plurality of signal strength RSSI values corresponding to each floor in a plurality of floors; the positioning node is in one of several floors, or between two adjacent floors; extracting a plurality of RSSI values of a preset number in each floor within a preset time period at each preset time interval; determining a detection floor set corresponding to the positioning node according to a plurality of RSSI values extracted from each floor; according to a plurality of RSSI values corresponding to all the detection floors in the detection floor set, carrying out significance detection on the corresponding detection floors and determining positioned floors corresponding to the positioning nodes; and determining the position coordinates of the positioning nodes according to a minimum objective function algorithm based on the position coordinates of the target anchor points with the strongest RSSI values in the positioned floors.
The embodiment of the application determines the positioned floor by carrying out significance test on the floor. Therefore, under the condition that the RSSI value fluctuation dynamic range of each anchor point is large, the method and the device improve the accuracy of floor identification and overcome the defect that the positioning node jumps floors. In addition, the position coordinates of the positioned nodes are determined through a minimum objective function algorithm. Therefore, the position information of a plurality of anchor points and the RSSI value corresponding to each anchor point are utilized, the low cost is realized, and the precision of the positioning result of the positioning node is improved.
In one example, according to a plurality of RSSI values corresponding to each detection floor in the detection floor set, the significance of the corresponding detection floor is detected, and the positioned floor corresponding to the positioning node is determined, which specifically includes: determining an original assumed floor according to the RSSI value of each inspection floor; the mean value of the RSSI values of the original assumed floor is more than or equal to a preset threshold value; determining a candidate hypothesis floor according to the RSSI value of the corresponding detection floor; the mean value of the RSSI values of the alternative assumed floors is smaller than a preset threshold value; determining quantiles of all the detection floors according to the detection level values and the degrees of freedom of a plurality of RSSI values in all the detection floors; the inspection level value is obtained according to the presetting; comparing the quantiles of all the test floors with the test statistics corresponding to all the test floors to determine a plurality of floors receiving the original hypothesis; the test statistic is obtained according to the presetting; and determining the positioned floor according to the test statistic of each floor in the plurality of floors of the original hypothesis.
According to the embodiment of the application, the floors receiving the original hypothesis are determined according to the RSSI values of all the detection floors, so that the possibility of floor misjudgment is reduced.
In one example, the method for determining the located floor according to the test statistic of each floor in the originally assumed floors includes: and determining the floor with the highest checking statistic value of each floor in the plurality of floors assumed originally as the positioned floor.
In one example, comparing the quantile of each test floor with the test statistic corresponding to each test floor to determine a plurality of floors that accept the original hypothesis, specifically comprising: and determining the quantiles of the test floors smaller than the test statistic corresponding to the test floors as a plurality of floors receiving the original hypothesis.
In one example, determining the position coordinates of the positioning node according to a minimum objective function algorithm based on the position coordinates of the target anchor point with the strongest RSSI value in the positioned floor specifically includes: taking the position coordinates of the target anchor point with the strongest RSSI value in the positioned floor as the initial values of the minimized target functions corresponding to the position coordinates of the positioning nodes; and determining the position coordinates of the positioning nodes according to the initial values and the minimized objective function.
According to the embodiment of the application, the position coordinate of the target anchor point with the strongest RSSI value in the positioned floor is used as the initial value of the minimized target function corresponding to the position coordinate of the positioning node, so that the robustness of the minimized target function algorithm is improved.
In one example, the minimization objective function algorithm is implemented by the following formula:
wherein x isiX-axis coordinate value, y, of target anchor position coordinates for a located flooriY-axis coordinate value of position coordinate of anchor point of positioned floor target, x is x-axis coordinate value of position coordinate of positioned node, y is y-axis coordinate value of position coordinate of positioned node, diTo locate the distance of the node position coordinates to the ith target anchor point,
and the weight from the position coordinate of the target anchor point of the positioned floor to the position coordinate of the positioning node.
In one example, the distance d from the positioning node position coordinates to the ith target anchor pointiThe method is realized by the following formula:
wherein d isiThe distance between the positioning node and the ith target anchor point is defined, RSSI is the signal strength value of the positioning node for receiving the ith target anchor point, p1 is the strength value of the signal received at the position of the positioning node, which is 1 meter away from the ith target anchor point, and p2 is a parameter that the signal strength decreases with the increase of the distance.
In one example, determining location coordinates of a positioning node according to a minimization of an objective function algorithm based on location coordinates of a strongest RSSI value target anchor point in a positioned floor previously comprises: based on the floor to be positioned, filtering anchors which do not belong to the corresponding RSSI value of the floor to be positioned according to the identifications of the anchors; and determining an adjacent anchor point set of the position coordinates of the target anchor point with the strongest RSSI value according to the position coordinates of the target anchor point with the strongest RSSI value in the positioned floor, and determining the position coordinates of the positioning node according to the position coordinates of the target anchor point with the strongest RSSI value and the position coordinates of the adjacent anchor point set.
In one example, the extracting of the predetermined time period and the predetermined number of RSSI values per floor at each predetermined time interval further comprises: and according to a Kalman filter, filtering a plurality of RSSI values corresponding to each floor in a plurality of floors.
According to the embodiment of the application, the RSSI value corresponding to each floor in a plurality of floors is filtered, so that the dynamic range of the RSSI values is reduced.
On the other hand, this application embodiment provides an indoor positioning device, includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any one of the indoor positioning methods described above.
According to the indoor positioning method and the indoor positioning equipment, when the floors are positioned through significance detection, the accuracy of floor identification is improved, and the influence of large fluctuation dynamic range of RSSI values of anchor points is reduced. When the positioning node is positioned, the method for optimizing the position coordinates of the positioning node by using the minimum objective function uses the information of a plurality of anchor points, enables the anchor points with close distances to have higher weight, converts the positioning problem of the positioning node into the optimization problem of the minimum objective function, and improves the positioning precision.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart of an indoor positioning method according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a floor determination method based on t-test according to an embodiment of the present application;
fig. 3 is a schematic diagram of a neighboring beacon set provided in an embodiment of the present application;
fig. 4 is a schematic diagram of an indoor positioning device provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. 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 application.
Under the condition that an atrium exists in a room or a hollow area exists in the room, adjacent floors are close to each other, and wireless signals can be directly transmitted through the hollow area, so that the signal attenuation is small, the adjacent floors are difficult to distinguish, and floor positioning is inaccurate. In order to solve the technical problems, the embodiment of the application provides an indoor positioning method for positioning floors in a building.
It should be noted that the embodiments of the present application do not limit the specific scenarios of the applications. For example, the location may be a mall, a casino, a hotel, or a floor where no entrance exists.
In addition, the anchor point related to the embodiment of the present application may be any one of a Wi-Fi device, a Zigbee device, and a bluetooth device, and the present application does not particularly limit this. For convenience of understanding and description, the following embodiments will be described in detail by taking a bluetooth beacon device as an example.
Further, in this embodiment of the application, a plurality of bluetooth beacon devices of the same specification may be arranged in each floor, and the specific arrangement number and arrangement density of the bluetooth beacon devices in each floor are determined according to a specific application scenario and positioning accuracy. And a plurality of bluetooth beacon devices are placed at a fixed position on each floor or at a position between adjacent floors, respectively, and set to a broadcast mode. And power-on, namely broadcasting, and the Bluetooth low energy host cannot be connected with the Bluetooth low energy host.
Furthermore, the hardware suitable for the positioning node in the embodiment of the present application may be a bluetooth beacon signal receiving terminal carried by a user, and the bluetooth beacon signal receiving terminal may be any electronic device supporting bluetooth beacon signal receiving, for example, a mobile phone, a smart phone, a notebook computer, a wearable device, and the like. Suitable hardware of the embodiments of the present application may also be a non-portable electronic device, for example, a smart television providing a camera function. For convenience of understanding and description, the following embodiments take a terminal device carried by an execution subject as an example for detailed description.
Because the bluetooth beacon device only transmits signals outwards, the bluetooth beacon device cannot push information to the terminal or receive messages. Therefore, the terminal needs to install the APP corresponding to the bluetooth beacon device.
For example, the user installs the APP in the market at the mobile phone terminal, the merchant deploys a beacon device at the corner of the cosmetics special cabinet, when the user approaches the cosmetics special cabinet, the terminal APP detects that the user is less than 5 meters away from the special cabinet at the background, at this moment, the mobile phone terminal APP initiates a latest cosmetic product introduction and benefit information notification, and then after the user opens the notification, the latest cosmetic product introduction and benefit information is popped up.
The method related to the embodiment of the present application may be implemented as a processor of a terminal device or as a server, which is not particularly limited in this application. For convenience of understanding and description, the following embodiments are described in detail by taking a processor of a terminal device as an example.
In the embodiment of the application, after the user installs the indoor positioning software in the terminal device, the terminal device receives signals sent by bluetooth beacon devices in the current floor of the user and other floors of the building where the user is located. And extracting a plurality of RSSI values of a preset number in each floor in a preset time period at each preset time interval, and determining a detection floor set based on the extracted plurality of RSSI values of each floor. It should be noted that the RSSI values involved in the embodiments of the present application are all negative values, and the inspection floors are several floors used for t inspection.
The terminal equipment determines the positioned floor by carrying out t test on the tested floor.
The significance test in the embodiments of the present application may be a t test, an analysis of variance, or the like, and the present application is not particularly limited thereto. For convenience of understanding and description, the significance test is described in detail in the following examples as an example of the t test. And the t test is mainly used for normal distribution with small sample content and unknown total standard deviation sigma. In one embodiment of the application, an assumption is made in advance about the overall distribution of the floors to be located, and then the RSSI values of the beacons of the test floors of the floors are used to determine whether the original assumption is reasonable.
In order to realize accurate indoor positioning, the embodiment of the application filters the beacon devices which do not belong to the positioned floor, and then determines the adjacent beacon set according to the position coordinates of the target beacon device with the strongest RSSI value of the positioned floor. Wherein the set of neighboring beacons includes a number of target beacon devices. And carrying out a minimum objective function algorithm on each target beacon of the adjacent beacon set so as to obtain the accurate position coordinates of the user indoors.
The technical solution of the present invention is further explained below.
Fig. 1 is a flowchart of an indoor positioning method according to an embodiment of the present application.
S101, receiving a plurality of signal strength RSSI values corresponding to each floor in a plurality of floors by a positioning node.
In the embodiment of the application, the terminal equipment receives a plurality of beacon RSSI values corresponding to each floor of a building where a user is located, and performs filtering processing on the plurality of RSSI values. In an example, kalman filtering processing is performed on a plurality of RSSI values, and for a specific implementation manner of the kalman filter, the embodiment of the present application is not particularly limited.
Wherein the terminal device is in one of several floors or between two adjacent floors. For example, the terminal device is on stairs of 2 th and 3 rd.
According to the embodiment of the application, the RSSI value corresponding to each floor in a plurality of floors is filtered, so that the dynamic range of the RSSI values is reduced.
S102, extracting a plurality of RSSI values of a preset number in each floor in a preset time period at each preset time interval.
For example, every 5 seconds, the terminal device collects beacon RSSI values for each floor in the building within 3 seconds, and randomly samples 20 RSSI values for each floor based on the floor number of each floor.
Note that the beacon anchor information of each floor includes a floor number of each floor.
In addition, due to the limited transmission distance of the bluetooth beacon device, most of the RSSI values received by the terminal device are the floor where the terminal device is located and the nearby floor. And each beacon may transmit 3-4 signals per second, so 3 seconds of each beacon may transmit a dozen or so signals. Therefore, the embodiment of the application does not need to consider the problem that the number of beacon devices on the floor where the terminal device is located is insufficient, so that the RSSI values of a plurality of beacons received by the terminal device are less than the preset number.
S103, determining a detection floor set corresponding to the positioning node according to the plurality of RSSI values extracted from each floor.
And within the preset quantity, regarding the floors with the quantity of the RSSI values of each floor exceeding the basic quantity, taking the floors as inspection floors, obtaining an inspection floor set corresponding to the terminal equipment, and abandoning other floors of the positioned floors. For example, 20 RSSI values are randomly sampled for each floor, and for floors where the number of RSSI values for each floor exceeds 10, the floor is taken as a check floor. In a mall with 5 floors, a terminal device randomly extracts 20 RSSI values of each floor within 3 seconds, the number of 2-floor RSSI values is 18, the number of 3-floor RSSI values is 20, the number of 4-floor RSSI values is 20, and the number of 5-floor RSSI values is 20, but the number of 1-floor RSSI values is 9, and then 2-floor, 3-floor, 4-floor, and 5-floor are taken as check floors, and it is considered that a user is not likely to be in 1-floor.
And S104, according to a plurality of RSSI values corresponding to all the detection floors in the detection floor set, carrying out significance detection on the corresponding detection floors, and determining positioned floors corresponding to the positioning nodes.
In the embodiment of the application, the positioned floor is determined by carrying out t test on each test floor in the test floor set. And how to perform t-test on each test floor is described with reference to fig. 2 and related contents.
The floor to be positioned is determined by performing t-test on the floor. Therefore, under the condition that the fluctuation dynamic range of the RSSI value of each beacon device is large, the accuracy of the positioned floor identification is improved, and the defect that the position coordinates of the terminal device jump floors is overcome.
And S105, determining the position coordinates of the positioning nodes according to a minimum target function algorithm based on the position coordinates of the target anchor points with the strongest RSSI values in the positioned floors.
In the embodiment of the application, after the floor to be positioned is determined, according to the position coordinates of the strongest RSSI value target beacon in the floor to be positioned, the adjacent beacon set of the position coordinates of the strongest RSSI value target beacon is determined.
As shown in fig. 3, fig. 3 is a schematic diagram of a neighboring beacon set according to an embodiment of the present application.
Specifically, firstly, the beacons not belonging to each floor are filtered through the identification information of the beacon anchor of each floor. For example, the beacons not belonging to the floor are filtered through the floor number of each floor carried by the beacon anchor point information of each floor. And secondly, filtering the beacons beyond the radius range of R meters by taking the position coordinates of the beacon with the strongest RSSI value in the floor as the circle center. And finally, filtering the over-small beacon RSSI value within the radius range of R meters by setting a threshold range, and determining an adjacent beacon set of the floor, namely a target beacon set of the floor.
It should be noted that, in this embodiment of the present application, the beacons that do not belong to the floor may also be filtered through the floor number of each floor carried by the beacon anchor point information of each floor. And then filtering the beacon RSSI value which is too small in the floor by setting a corresponding threshold range, and determining the target beacon of the floor. In this regard, the embodiments of the present application are not particularly limited.
Further, the position coordinate of the terminal equipment is determined according to the position coordinate of the target beacon with the strongest RSSI value, the position coordinate of the adjacent beacon set and a minimum objective function algorithm corresponding to the terminal equipment.
After determining the target beacon set of the positioned floor, the position coordinates of the target beacon with the strongest RSSI value in the positioned floor are used as the initial value of the minimization target function. And determining the position coordinates of the terminal equipment according to the initial value of the minimized objective function and the minimized objective function.
According to the embodiment of the application, the position coordinates of the target beacon with the strongest RSSI value in the floor to be positioned are used as the initial value of the minimized target function, so that the defect that an irreversible matrix is encountered in a least square method related to inversion operation is overcome, and the robustness of the algorithm is improved.
Specifically, the distance between the terminal device and each beacon is measured by using the principle that the radio signal is regularly attenuated as the distance increases, and the distance between the terminal device and each target beacon device of the floor to be located is obtained according to a formula. The formula of RSSI value versus transmission distance is as follows:
wherein d isiThe distance between the terminal device and the ith beacon device is defined as RSSI (received signal strength indicator) of the ith beacon device received by the terminal device, p1 is the strength value of a signal received by the terminal device at a distance of 1 meter from the ith beacon device, and p2 is a parameter of decreasing signal strength with increasing distance, namely a reduction factor.
And taking the position coordinates of the target beacon with the strongest RSSI value in the floor as an initial value of quasi-Newton iteration, bringing the position coordinates into a minimized target function through a quasi-Newton algorithm, stopping the machine after certain precision or iteration times are reached, and obtaining coordinates which are more accurate positioning coordinates. For example, the machine is shut down after 100 iterations.
The minimization objective function algorithm is realized by the following formula:
wherein x isiX-axis coordinate value, y, of i-target beacon location coordinates for a located flooriY-axis coordinate value of target beacon position coordinate of positioned floor i, x is x-axis coordinate value of terminal equipment position coordinate, y is y-axis coordinate value of terminal equipment position coordinate, diThe distance from the terminal device position coordinates to the ith target beacon,
minF (x, y) is the minimum value of the minimum objective function corresponding to the terminal device, and is the weight of the ith target beacon position coordinate of the positioned floor to the position coordinate of the terminal device.
According to the method for optimizing the position coordinates of the terminal equipment through the minimized objective function corresponding to the terminal equipment, the information of a plurality of beacons is used, the beacons close to the distance are enabled to have higher weight, the positioning problem of the terminal equipment is converted into the optimization problem of the minimized objective function, no complex program is needed, and the data processing efficiency and the positioning precision are improved.
The floor determination method based on t-test in the embodiment of the present application is described in detail next, as shown in fig. 2.
Fig. 2 is a flowchart of a floor determination method based on t-test according to an embodiment of the present application.
S201, the positioning node determines the original assumed floor according to the RSSI value of each detection floor.
In the embodiment of the application, the floor with the mean value of the RSSI of each detection floor being more than or equal to-80 is determined as the original assumed floor. That is, it is preset that there is a floor to be located at a floor where the mean value of the RSSI of each inspection floor is-80 or more.
S202, determining an alternative hypothesis floor according to the RSSI value of the corresponding detection floor.
In the embodiment of the application, the floor with the mean value of the RSSI of each inspection floor being less than-80 is determined as the alternative hypothesis floor. That is, it is preset that there is a floor to be located at a floor where the mean value of the RSSI of each inspection floor is less than-80.
And S203, determining the quantile of each inspection floor according to the inspection level value and the degrees of freedom of a plurality of RSSI values in each inspection floor.
In the embodiment of the present application, the inspection level α of the t-test is predetermined to be 0.05. It should be noted that the inspection level α is also referred to as a significance level. The original hypothesis is true and the conclusion of the t-test is that the original hypothesis is discarded, so the probability of such an error is denoted as α. That is, there is a 95% probability that the located floor is located in the original hypothetical floor set.
Further, the degree of freedom is n-1. Wherein n is the number of a plurality of RSSI values of each inspection floor. For example, in a mall with 5 floors, the terminal device is prepared to randomly extract 20 RSSI values for each floor within 3 seconds, and finally, if the number of 2-floor RSSI values is 18 and the number of 3-floor RSSI values is 20, the degree of freedom of several RSSI values in 2 floors is 18-1 to 17, and the degree of freedom of several RSSI values in 3 floors is 20-1 to 19.
Quantiles, also known as quantiles, refer to numerical points that divide the probability distribution range of a random variable into several equal parts. Quantile t1-αThe calculation of (n-1) requires two elements, namely the level of significance and the degree of freedom. In the embodiment of the application, the inspection level value is already given, the degree of freedom of each inspection floor is also calculated, and the quantiles of each inspection floor are obtained by inquiring the corresponding t distribution upper quantile numerical table.
And S204, comparing the quantile of each test floor with the test statistic corresponding to each test floor, and determining a plurality of floors receiving the original hypothesis.
The test statistic T is evidence for deciding whether the original hypothesis can be rejected, and is implemented by the following formula:
wherein,
the average value of a plurality of RSSI values of each test floor is s is the variance of a plurality of RSSI values of each test floor, mu is the average value of normal distribution, and n is the number of a plurality of RSSI values of each test floor.
And the floor with the grading value of each inspection floor smaller than the inspection statistic value corresponding to each inspection floor is used as the floor for receiving the original hypothesis.
S205, determining the positioned floor according to the test statistic of each floor in the plurality of floors assumed originally.
In the embodiment of the application, the floor with the maximum checking statistic value of each floor in a plurality of floors assumed originally is used as the floor where the terminal equipment is located.
Based on the same idea, some embodiments of the present application further provide a device corresponding to the above method.
Fig. 4 is a schematic diagram of an indoor positioning device provided in an embodiment of the present application. The indoor positioning apparatus 400 includes at least a receiver 410 and a processor 420.
The receiver 410 is configured to receive a plurality of RSSI values corresponding to each of a plurality of floors, and locate a node in one of the plurality of floors or between two adjacent floors.
The processor 420 is configured to extract a plurality of RSSI values of a preset number of times in a preset time period and on each floor every preset time interval; determining a detection floor set corresponding to the positioning node according to a plurality of RSSI values extracted from each floor; according to a plurality of RSSI values corresponding to all the detection floors in the detection floor set, carrying out significance detection on the corresponding detection floors and determining positioned floors corresponding to the positioning nodes; and determining the position coordinates of the positioning nodes according to a minimum objective function algorithm based on the position coordinates of the target anchor points with the strongest RSSI values in the positioned floors.
Some embodiments of the present application provide an apparatus corresponding to an indoor positioning method of fig. 1, where the apparatus stores one or more programs, and the one or more programs are executable by one or more processors to implement the indoor positioning method.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The devices and the methods provided by the embodiment of the application are in one-to-one correspondence, so the devices also have beneficial technical effects similar to the corresponding methods.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (6)
1. An indoor positioning method, characterized in that the method comprises:
the positioning node receives a plurality of signal strength RSSI values corresponding to each floor in a plurality of floors; the positioning node is in one of the floors or between two adjacent floors;
extracting a plurality of RSSI values of a preset number in each floor within a preset time period at each preset time interval;
determining a detection floor set corresponding to the positioning node according to a plurality of RSSI values extracted from each floor; within a preset number, regarding floors with the number of the RSSI values of each floor exceeding a basic number, taking the floors as inspection floors to obtain an inspection floor set;
according to a plurality of RSSI values corresponding to all the detection floors in the detection floor set, carrying out significance detection on the corresponding detection floors and determining positioned floors corresponding to the positioning nodes;
determining a neighboring anchor point set of the position coordinates of the strongest RSSI value target anchor point based on the position coordinates of the strongest RSSI value target anchor point in the positioned floor; determining the position coordinates of the positioning nodes according to the position coordinates of the target anchor point with the strongest RSSI value, the position coordinates of the adjacent anchor point set and a minimum target function algorithm;
the said a plurality of RSSI values that correspond according to each inspection floor in the said inspection floor set, carry on the significance test to the corresponding inspection floor, confirm the floor positioned that the said locating node corresponds to, include specifically:
determining an original assumed floor according to the RSSI value of each inspection floor; the mean value of the RSSI values of the original assumed floor is greater than or equal to a preset threshold value;
determining a candidate hypothesis floor according to the RSSI value of the corresponding detection floor; the mean value of the RSSI values of the alternative hypothesis floors is smaller than a preset threshold value;
determining quantiles of all the detection floors according to the detection level values and the degrees of freedom of a plurality of RSSI values in all the detection floors; the inspection level value is obtained according to the presetting;
comparing the quantiles of the test floors with the test statistics corresponding to the test floors to determine a plurality of floors receiving the original hypothesis; the test statistic is obtained according to the presetting;
determining the positioned floor according to the test statistic of each floor in the plurality of floors of the original hypothesis;
the determining the located floor according to the test statistic of each floor of the plurality of floors assumed originally specifically includes:
determining the floor with the maximum checking statistic value of each floor in the plurality of floors of the original hypothesis as the positioned floor;
the determining the position coordinates of the positioning nodes based on the position coordinates of the target anchor point with the strongest RSSI value in the positioned floor according to a minimized target function algorithm specifically comprises the following steps:
taking the position coordinate of the target anchor point with the strongest RSSI value in the positioned floor as an initial value of a minimized target function corresponding to the position coordinate of the positioning node;
determining the position coordinates of the positioning nodes according to the initial values and the minimized objective function;
the minimization objective function algorithm is realized by the following formula:
wherein x isiX-axis coordinate value, y, of target anchor position coordinates of the floor being locatediY-axis coordinate value of the position coordinate of the positioned floor target anchor point, x is the x-axis coordinate value of the position coordinate of the positioning node, y is the y-axis coordinate value of the position coordinate of the positioning node, diTo locate the distance of the node position coordinates to the ith target anchor point,
and the weight from the position coordinate of the target anchor point of the positioned floor to the position coordinate of the positioning node.
2. The indoor positioning method according to claim 1, wherein the comparing the quantile of each test floor with the test statistic corresponding to each test floor to determine the floors that accept the original hypothesis specifically comprises:
and determining the quantiles of the test floors to be a plurality of floors for receiving the original hypothesis, wherein the quantile of the test floors is smaller than the test statistic corresponding to the test floors.
3. The indoor positioning method of claim 1, wherein the distance d from the position coordinates of the positioning node to the ith target anchor pointiThe method is realized by the following formula:
wherein d isiThe distance between the positioning node and the ith target anchor point is defined as RSSI (received signal strength indicator) which is the signal strength value of the ith target anchor point received by the positioning node, p1 is the strength value of a signal received by the positioning node at a position 1 meter away from the ith target anchor point, and p2 is a parameter of which the signal strength decreases with the increase of the distance.
4. The indoor positioning method according to claim 1, further comprising:
based on the positioned floor, according to the identifications of a plurality of anchor points, filtering the anchor points which do not belong to the positioned floor and correspond to the RSSI value so as to determine the adjacent anchor point set.
5. The indoor positioning method according to claim 1, wherein the extracting a predetermined time period and a predetermined number of the plurality of RSSI values per floor per predetermined time interval further comprises:
and according to a Kalman filter, filtering a plurality of RSSI values corresponding to each floor in the plurality of floors.
6. An apparatus for indoor positioning, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the indoor positioning method of any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010960446.4A CN112188614B (en) | 2020-09-14 | 2020-09-14 | Indoor positioning method and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010960446.4A CN112188614B (en) | 2020-09-14 | 2020-09-14 | Indoor positioning method and equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112188614A CN112188614A (en) | 2021-01-05 |
| CN112188614B true CN112188614B (en) | 2022-03-29 |
Family
ID=73920854
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010960446.4A Active CN112188614B (en) | 2020-09-14 | 2020-09-14 | Indoor positioning method and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112188614B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113038367B (en) * | 2021-02-26 | 2022-07-12 | 山东鹰格信息工程有限公司 | Non-exposed space rapid positioning method, device and equipment based on 5G technology |
| CN113242523B (en) * | 2021-03-23 | 2022-12-09 | 杭州医惠物联网科技有限公司 | Indoor double-frequency RFID positioning multi-floor switching method |
| CN113068121A (en) * | 2021-03-31 | 2021-07-02 | 建信金融科技有限责任公司 | Positioning method, positioning device, electronic equipment and medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8385943B1 (en) * | 2012-05-02 | 2013-02-26 | YFIND Technologies Pte. Ltd. | Method and apparatus for determining location information of a position in a multi-storey building |
| CN105872972A (en) * | 2016-04-29 | 2016-08-17 | 武汉大学 | Self-adaptation AP selection method based on multi-target optimization |
| CN105898712A (en) * | 2016-06-15 | 2016-08-24 | 西北工业大学 | Stepwise indoor three-dimensional positioning method for use in multi-floor environment |
| CN106793085A (en) * | 2017-03-08 | 2017-05-31 | 南京信息工程大学 | Fingerprint positioning method based on normality assumption inspection |
| CN108616854A (en) * | 2017-03-28 | 2018-10-02 | 集速智能标签（上海）有限公司 | A kind of method and system of indoor positioning |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105467358A (en) * | 2014-08-25 | 2016-04-06 | 中兴通讯股份有限公司 | Indoor positioning method and indoor positioning device |
| CN104703143B (en) * | 2015-03-18 | 2018-03-27 | 北京理工大学 | A kind of indoor orientation method based on WIFI signal intensity |
| US20170300599A1 (en) * | 2016-04-18 | 2017-10-19 | University Of Southern California | System and method for calibrating multi-level building energy simulation |
| CN108989976B (en) * | 2018-06-04 | 2020-09-11 | 华中师范大学 | Fingerprint positioning method and system in intelligent classroom |
-
2020
- 2020-09-14 CN CN202010960446.4A patent/CN112188614B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8385943B1 (en) * | 2012-05-02 | 2013-02-26 | YFIND Technologies Pte. Ltd. | Method and apparatus for determining location information of a position in a multi-storey building |
| CN105872972A (en) * | 2016-04-29 | 2016-08-17 | 武汉大学 | Self-adaptation AP selection method based on multi-target optimization |
| CN105898712A (en) * | 2016-06-15 | 2016-08-24 | 西北工业大学 | Stepwise indoor three-dimensional positioning method for use in multi-floor environment |
| CN106793085A (en) * | 2017-03-08 | 2017-05-31 | 南京信息工程大学 | Fingerprint positioning method based on normality assumption inspection |
| CN108616854A (en) * | 2017-03-28 | 2018-10-02 | 集速智能标签（上海）有限公司 | A kind of method and system of indoor positioning |
Non-Patent Citations (1)
| Title |
|---|
| 相关性匹配蓝牙信标位置指纹库的室内定位;王艳丽等;《电讯技术》;20170228;第57卷(第2期);第145-150页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112188614A (en) | 2021-01-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112188614B (en) | Indoor positioning method and equipment | |
| US9918297B2 (en) | Location measuring method and apparatus using access point for wireless local area network service and method for estimating location coordinate of access point | |
| US8862154B2 (en) | Location measuring method and apparatus using access point for wireless local area network service | |
| US9560532B2 (en) | Signal strength profiling | |
| CN104853379B (en) | A kind of quality of wireless network appraisal procedure and device | |
| US9380472B2 (en) | Method and apparatus for updating access point information for location measurement | |
| US8947299B2 (en) | Location measuring method and apparatus using access point for wireless local area network service | |
| CN101923118B (en) | Building influence estimation apparatus and building influence estimation method | |
| US20150264536A1 (en) | Mobile device locating using limited access points | |
| EP3051892B1 (en) | Mobile device, location estimation method, and location estimation program | |
| US8942737B2 (en) | Location measuring method and apparatus using access point and lamp | |
| KR20090085937A (en) | Method, apparatus and system for mearsuring location based terminal by using u-pcell database | |
| CN106358155A (en) | Building method and device of radio frequency fingerprint database | |
| Schmidt et al. | A performance study of a fast-rate WLAN fingerprint measurement collection method | |
| CN109151845B (en) | Method, apparatus, electronic device and storage medium for identifying indoor hierarchical high-level cell | |
| CN115150739B (en) | Building positioning method and device, electronic equipment and storage medium | |
| CN105510941A (en) | Signal processing method and electronic equipment | |
| CN113645625B (en) | Pseudo base station positioning method, pseudo base station positioning device, electronic equipment and readable medium | |
| Huh et al. | Bluetooth-tracing rssi sampling method as basic technology of indoor localization for smart homes | |
| CN111405598A (en) | Indoor distribution signal leakage detection method and equipment | |
| CN113179533B (en) | Network problem positioning method, device, equipment, storage medium and program product | |
| CN112566013B (en) | Target equipment positioning method, device, equipment and computer storage medium | |
| KR101188195B1 (en) | Method for removing unnecessary access point information and apparatus therefor | |
| CN114095854A (en) | Indoor distribution network interference source positioning method based on MDT data and electronic equipment | |
| CN115097215B (en) | Radiation evaluation method, device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |