Position fingerprinting method, apparatus, device, and computer storage medium

Abstract

The application provides a position fingerprint positioning method, device, equipment and computer storage medium. The method positions the current position of a target user by a Kalman positioning module, and determines the obtained current position as an initial position of the target user; then, a speed parameter of the target user is acquired in real time, and it is judged whether the speed parameter is greater than a preset speed parameter; when the speed parameter is greater than the preset speed parameter, a predicted position and an observed position of the target user are respectively determined based on an extended Kalman filtering algorithm according to a PDR positioning module and the Kalman positioning module; and the obtained predicted position and observed position are fused to determine the final position of the target user. The method optimizes the positioning method of a mobile terminal, improves the positioning accuracy of the mobile terminal, reduces the positioning cumulative error generated in the positioning process of the mobile terminal, provides a new positioning method based on the fusion of sensor technology and the extended Kalman filtering algorithm, and improves the user experience.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of indoor positioning technology, and in particular to a location fingerprint positioning method, device, equipment and computer storage medium. BACKGROUND

[0002] Mobile positioning refers to a technology or service for obtaining the position information (latitude and longitude coordinates) of a mobile phone or terminal user through a specific positioning technology, marking the position of the positioned object on an electronic map. The positioning system server end is located in the operator core network, and the client end faces the smart phone users, supports 4G and 5G networks, and is widely used in smart phones, coordination systems, etc.

[0003] Among them, the mobile positioning technology includes fingerprint indoor positioning technology and pedestrian dead reckoning technology. The fingerprint indoor positioning technology is based on the communication data reported by the terminal. According to the first correspondence relationship between the grid of the building, the main adjacent area combination and the fingerprint information, the terminal is positioned indoors. The pedestrian dead reckoning technology can calculate the state of the user when walking to obtain the walking direction and distance of the user.

[0004] In the prior art, the fingerprint indoor positioning technology performs indoor positioning through the first correspondence relationship between the grid of the building, the main adjacent area combination and the fingerprint information. Since the accuracy of the grid can only reach meters, only rough positioning calculation can be performed. The gap between the grids is large, resulting in low positioning accuracy. When the user is in a moving state, the cumulative positioning error will gradually increase. The pedestrian dead reckoning technology can calculate the walking direction and distance of the user when walking, but it cannot position the initial position of the user. At the same time, it cannot eliminate the cumulative positioning error caused by multiple walks of the user. SUMMARY

[0005] The present application provides a location fingerprint positioning method, device, equipment and computer storage medium to solve the defects of low positioning accuracy of current positioning technology and inability to eliminate the cumulative positioning error caused by multiple positioning of the user.

[0006] In a first aspect, the present application provides a location fingerprint positioning method, comprising:

[0007] controlling a positioning module to position the current position of the target user, and determining the current position as the initial position of the target user, the positioning module comprising a PDR positioning module and a Kalman positioning module;

[0008] obtaining the speed parameter of the target user in real time, and determining whether the speed parameter is greater than a preset speed parameter, the speed parameter being used to indicate the movement of the target user;

[0009] When the speed parameter is greater than the preset speed parameter, the predicted position and observed position of the target user are determined based on the extended Kalman filter algorithm according to the PDR positioning module and the Kalman positioning module.

[0010] The final location of the target user is determined based on the predicted location and the observed location.

[0011] Optionally, determining the predicted location and observed location of the target user based on the extended Kalman filter algorithm using the PDR positioning module and the Kalman positioning module respectively includes:

[0012] The movement parameters of the target user are determined according to the PDR positioning module. The movement parameters are used to indicate the parameter information generated during the process of the target user moving from the initial position to the final position.

[0013] Based on the movement parameters, the PDR positioning module is controlled to predict the location of the target user to obtain the predicted location of the target user.

[0014] Obtain the target signal parameters corresponding to the predicted location of the target user;

[0015] The target signal parameters are matched with the signal parameters in the fingerprint database, and the observation location of the target user is determined based on the matching results.

[0016] Optionally, controlling the PDR positioning module to predict the location of the target user includes:

[0017] The location of the target user is predicted using the following formula:

[0018] X k =f k (X k-1 )+W k-1

[0019] Among them, X k The location information of the target user at time k is calculated and processed by the PDR positioning module.

[0020] f k (X k-1 ) represents the nonlinear relationship between the state vector at time k-1 and the state vector at time k;

[0021] W k-1 The input Gaussian white noise to the system at time k-1;

[0022] The process of matching the target signal parameters with signal parameters in the fingerprint database includes:

[0023] The target signal parameter is matched with the signal parameter in the fingerprint database by using the following formula:

[0024] Z k = h k (X k ) + V k

[0025] Wherein, Z k is the position information of the target user at time k obtained after calculation and processing by the Kalman positioning module;

[0026] h k (X k ) is the corresponding position information in the fingerprint database at time k;

[0027] V k is the observed Gaussian distribution white noise at time k.

[0028] Optionally, the final position of the target user is determined according to the predicted position and the observed position, comprising:

[0029] updating the predicted position and the observed position;

[0030] determining the measurement gain of the current positioning module according to the updating result, the measurement gain being used to indicate that the predicted position and the observed position are mutually complementary and corrected;

[0031] determining the final position of the target user according to the measurement gain.

[0032] Optionally, the final position of the target user is determined according to the measurement gain, comprising:

[0033] the final position of the target user is determined by using the following formula:

[0034]

[0035] Wherein, is the position information of the target user at time k obtained after fusion processing of the predicted position and the observed position;

[0036] is the predicted value of the position information at time k obtained by the PDR positioning module according to the position information at time k-1;

[0037] K k is the measurement gain of the fusion processing of the predicted position and the observed position;

[0038] Z kcalculating the position information of the target user at time k after processing the position information of the target user at time k-1 obtained by the Kalman positioning module.

[0039] calculating the position information of the target user at time k after processing the position information of the target user at time k-1 obtained by the Kalman positioning module.

[0040] Optionally, the method further comprises:

[0041] acquiring the position information of the target user according to a preset period when the speed parameter is not greater than the preset speed parameter;

[0042] determining the initial positions of a plurality of target users according to a plurality of position information acquired within the preset period;

[0043] calculating a plurality of initial positions to obtain new position information, and determining the new position information as the new initial position of the target user.

[0044] Optionally, the method further comprises:

[0045] after determining the final position of the target user, reacquiring the speed parameter of the target user, and determining whether the speed parameter is greater than the preset speed parameter;

[0046] when the speed parameter is not greater than the preset speed parameter, determining the final position as the initial position when the PDR positioning module is positioned again, and updating the initial position according to the preset period.

[0047] In a second aspect, the application provides a position fingerprint positioning device, comprising:

[0048] a control module configured to control a positioning module to position a current position of a target user, wherein the positioning module comprises a PDR positioning module and a Kalman positioning module.

[0049] a determination module configured to determine the current position as an initial position of the target user.

[0050] an acquisition module configured to acquire a speed parameter of the target user in real time, wherein the speed parameter is used to indicate the movement of the target user.

[0051] a judgment module configured to determine whether the speed parameter is greater than a preset speed parameter.

[0052] the determination module is further configured to determine a predicted position and an observed position of the target user based on an extended Kalman filtering algorithm according to the PDR positioning module and the Kalman positioning module when the speed parameter is greater than the preset speed parameter.

[0053] The determining module is further configured to determine a final position of the target user according to the predicted position and the observed position.

[0054] Optionally, the determining module is further configured to determine a movement parameter of the target user according to the PDR positioning module, the movement parameter being used to indicate parameter information generated in a process in which the target user moves from the initial position to the final position.

[0055] The position fingerprint positioning apparatus further includes a processing module.

[0056] The processing module is configured to control the PDR positioning module to perform a prediction process on the position of the target user according to the movement parameter, to obtain a predicted position of the target user.

[0057] The obtaining module is further configured to obtain a target signal parameter corresponding to the predicted position of the target user.

[0058] The processing module is further configured to perform a matching process on the target signal parameter and a signal parameter in a fingerprint database.

[0059] The determining module is further configured to determine an observed position of the target user according to a matching process result.

[0060] Optionally, the processing module is further configured to perform a prediction process on the position of the target user by using the following formula:

[0061] X k =f k (X k-1 )+W k-1

[0062] wherein X k is position information of the target user at time k obtained after calculation and processing of the PDR positioning module;

[0063] f k (X k-1 ) is a nonlinear relationship from a state vector at time k-1 to a state vector at time k;

[0064] W k-1 is a Gaussian white noise input by a system at time k-1;

[0065] The processing module is further configured to perform a matching process on the target signal parameter and the signal parameter in the fingerprint database by using the following formula:

[0066] Z k =h k (X k )+V k

[0067] wherein, Z k is the position information of the target user at time k obtained after calculation and processing by the Kalman positioning module;

[0068] h k (X k ) is the corresponding position information in the fingerprint database at time k;

[0069] V k is the observed Gaussian distributed white noise at time k.

[0070] Optionally, the processing module is further configured to update the predicted position and the observed position.

[0071] The determining module is further configured to determine a measurement gain of the positioning module according to a result of the updating, the measurement gain being used to indicate that the predicted position and the observed position are mutually complementary and corrected.

[0072] The determining module is further configured to determine a final position of the target user according to the measurement gain.

[0073] Optionally, the determining module is further configured to determine the final position of the target user according to the following formula:

[0074]

[0075] wherein, is the position information of the target user at time k obtained after fusion processing of the predicted position and the observed position;

[0076] is a predicted value of the position information of the target user at time k obtained by the PDR positioning module according to the position information at time k-1;

[0077] K k is a measurement gain of the fusion processing of the predicted position and the observed position;

[0078] Z k is the position information of the target user at time k obtained after calculation and processing by the Kalman positioning module;

[0079] is a predicted value of the position information of the target user at time k obtained by the Kalman positioning module according to the position information at time k-1.

[0080] Optionally, the obtaining module is further configured to obtain the position information of the target user according to a preset period when the speed parameter is not greater than the preset speed parameter.

[0081] The determining module is further configured to determine initial positions of the target users according to the plurality of position information obtained in the preset period.

[0082] The processing module is further configured to perform calculation processing on the plurality of initial positions to obtain new position information.

[0083] The determining module is further configured to determine the new position information as a new initial position of the target user.

[0084] Optionally, the obtaining module is further configured to re-obtain the speed parameter of the target user after determining the final position of the target user.

[0085] The judging module is further configured to judge whether the speed parameter is greater than the preset speed parameter.

[0086] The determining module is further configured to determine the final position as the initial position when the PDR positioning module is positioned again when the speed parameter is not greater than the preset speed parameter.

[0087] The processing module is further configured to update the initial position according to the preset period.

[0088] In a third aspect, a location fingerprint positioning device is provided, comprising:

[0089] a memory;

[0090] a processor;

[0091] The memory stores computer-executed instructions.

[0092] The processor executes the computer-executed instructions stored in the memory to implement the location fingerprint positioning method in the first aspect and various possible implementation manners of the first aspect.

[0093] In a fourth aspect, a computer storage medium is provided, which stores computer-executed instructions, and the computer-executed instructions are executed by a processor to implement the location fingerprint positioning method in the first aspect and various possible implementation manners of the first aspect.

[0094] The position fingerprint positioning method provided in the application comprises the following steps: positioning the current position of a target user by a Kalman positioning module, and determining the obtained current position as the initial position of the target user; acquiring the speed parameter of the target user in real time, and judging whether the speed parameter is greater than a preset speed parameter; when the speed parameter is greater than the preset speed parameter, determining the predicted position and the observed position of the target user based on an extended Kalman filtering algorithm according to a PDR positioning module and the Kalman positioning module; and fusing the obtained predicted position and observed position to determine the final position of the target user. The method optimizes the positioning method of a mobile terminal, improves the positioning accuracy of the mobile terminal, reduces the positioning cumulative error generated in the positioning process of the mobile terminal, provides a new positioning method based on the fusion of sensor technology and the extended Kalman filtering algorithm, and improves the user experience. BRIEF DESCRIPTION OF DRAWINGS

[0095] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles of the application.

[0096] Figure 1 A scene schematic diagram of the position fingerprint positioning method provided in the application;

[0097] Figure 2 A flowchart of the position fingerprint positioning method provided in the application Figure 1 ;

[0098] Figure 3 A flowchart of the position fingerprint positioning method provided in the application Figure 2 ;

[0099] Figure 4 A structure schematic diagram of the position fingerprint positioning device provided in the application;

[0100] Figure 5 A structure schematic diagram of the position fingerprint positioning device provided in the application.

[0101] The specific embodiments of the application have been shown in the above-described drawings, and will be described in more detail hereinafter. The drawings and the written description are not intended to limit the scope of the inventive concept in any way, but to illustrate the inventive concept to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0102] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The description of the exemplary embodiments is intended to apply to all alternative embodiments, as would be understood by one skilled in the art. To this end, the following description is not to be construed in a limiting sense as the exemplary embodiments merely illustrate known processes, devices, and systems.

[0103] The terms "first", "second", "third", "fourth", and the like used in the description and the claims herein, and above accompanying drawings, if any, are used to distinguish between similar objects, and are not necessarily used to describe a particular sequential or chronological order. It is to be understood that the use of the terms so construed can be interchanged, under appropriate circumstances, to refer to an embodiment of the application described herein by the description and drawings.

[0104] In the embodiments of the present application, the word "exemplary" or "for example" is used to mean an example, an illustration, or another instance or illustration. Any embodiment or design described herein as "exemplary" or "for example" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, the exemplary word is intended to present concepts in a concrete manner.

[0105] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards, and provide corresponding operation portal for user to choose authorization or refusal.

[0106] First, the terms involved in the present application are explained.

[0107] PDR: Pedestrain Dead Reckoning, is a process of detecting and dividing gait according to motion sensing information of pedestrians, and calculating the distance and direction of travel, so as to estimate the future arrival position; or a relative position positioning technology, which can provide continuous position information of moving body at any time; PDR uses inertial sensing (accelerometer, gyroscope, sometimes also uses magnetometer) to estimate speed and direction, the basic model of walking includes step number, step length and direction; in the case where the step length and step number are known, the travel distance of the pedestrian can be calculated, and a complete dead reckoning result can be obtained by combining the travel direction.

[0108] Kalman filtering algorithm: Kalman filtering, Kalman filtering, an algorithm for optimal estimation of system state by using linear system state equation and observing data of system input and output. Since the observation data includes the influence of noise and interference in the system, the optimal estimation can also be regarded as a filtering process.

[0109] Mobile positioning refers to a technology or service for obtaining the position information (latitude and longitude coordinates) of a mobile phone or terminal user through a specific positioning technology, marking the position of the positioned object on an electronic map. The positioning system server is located in the operator core network, and the client faces the smart phone users, supports 4G, 5G network, and is widely used in smart phones, coordination systems, etc.

[0110] Among them, the mobile positioning technology includes fingerprint indoor positioning technology and pedestrian dead reckoning technology, the fingerprint indoor positioning technology is based on the communication data reported by the terminal; according to the first correspondence relationship between the grid of the building, the main adjacent area combination and the fingerprint information, and the communication data, the terminal is positioned indoors; the pedestrian dead reckoning technology can calculate the state of the user when walking to obtain the walking direction and distance of the user.

[0111] The fingerprint indoor positioning technology mainly achieves indoor positioning through the detection and matching of position fingerprints, and the position fingerprints can be of various types. Any "position unique" (helpful for distinguishing positions) feature can be used as a position fingerprint, such as the multipath structure of the communication signal at a certain position, whether an access point or base station can be detected at a certain position, the received signal strength of the signal from the base station detected at a certain position, the round trip time or delay of the signal when communicating at a certain position, etc. These can be used as a position fingerprint, or they can be combined as a position fingerprint.

[0112] In the prior art, the fingerprint indoor positioning technology performs indoor positioning through the first correspondence relationship between the grid of the building, the main adjacent area combination and the fingerprint information. Since the accuracy of the grid can only reach meters, only rough positioning calculation can be performed, and the gap between the grids is large, resulting in low positioning accuracy. Moreover, when the user is in a moving state, the cumulative positioning error will gradually increase; the pedestrian dead reckoning technology can calculate the walking direction and distance of the user when walking, but it cannot position the initial position of the user, and it cannot eliminate the cumulative positioning error generated after the user walks multiple times.

[0113] In view of the above problems, the present application provides a position fingerprint positioning method.

[0114] Figure 1 is a scene diagram of the position fingerprint positioning method provided by the present application. As Figure 1As shown in the figure, the mobile terminal 1 and the plurality of base station networks 2 are in communication connection; the mobile terminal 1 can receive the characteristic signals sent from the plurality of base station networks 2, and the plurality of base station networks 2 can detect the characteristic signals of all positions in the detection area. The mobile terminal 1 may, for example, be a user terminal provided with a PDR positioning module and a Kalman positioning module, and the plurality of base station networks 2 may, for example, be a base station network composed of 6 base stations. The number of base stations contained in the plurality of base station networks is not specially limited in the present application.

[0115] The present application provides a position fingerprint positioning method, which positions the initial position of a user through a Kalman positioning module and provides the initial position for a PDR positioning module; detects whether the user is in a moving state through the PDR positioning module; if the user is not in the moving state, updates the initial position of the user through the Kalman positioning module; if the user is in the moving state, performs path deduction through the PDR positioning module and the Kalman positioning module respectively, and fuses the position information obtained by the two to obtain the final position of the user. The method optimizes the positioning method of the mobile terminal, improves the positioning accuracy of the mobile terminal, reduces the positioning cumulative error generated in the positioning process of the mobile terminal, provides a new positioning method based on the fusion of sensor technology by the extended Kalman filtering algorithm, and improves the user experience.

[0116] The technical solutions of the present application and how the technical solutions solve the above technical problems will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0117] Figure 2 Flowchart of the position fingerprint positioning method provided by the embodiments of the present application Figure 1 The execution subject of the present embodiment may, for example, be a user terminal provided with a PDR positioning module and a Kalman positioning module. As shown in the figure, Figure 2 The position fingerprint positioning method provided by the present embodiment comprises:

[0118] S201: Control the positioning module to position the current position of the target user, and determine the current position as the initial position of the target user.

[0119] The initial position is used to indicate the position information of the starting point when the target user starts to move, and the positioning module comprises a PDR positioning module and a Kalman positioning module.

[0120] It can be understood that the mobile terminal is provided with a plurality of data modules, which can detect the signal parameters of the target user's moving speed, moving distance and position, wherein the PDR positioning module can detect the speed information of the target user, so as to determine whether the target user is in a moving state, and the moving path of the target user can also be deduced based on the PDR sensor positioning; the Kalman positioning module can receive the signal parameters of the position where the target user is located, and the moving path of the target user can also be deduced based on the extended Kalman filtering algorithm; the Kalman positioning module is based on "position fingerprint" to realize the positioning of the target user, and the "position fingerprint" is the signal parameters of the signal from the base station detected at different positions, and the signal parameters at different positions have uniqueness, so the positioning of the target user can be realized according to the signal parameters of the current position where the target user is located; and when the target user is in a stationary state, the accuracy of positioning the target user based on "position fingerprint" is more accurate.

[0121] The existing positioning technology is realized based on a mobile network, that is, a cellular network; the cellular network is mainly composed of the following three parts: a mobile station, a base station subsystem and a network subsystem; the mobile station is a network terminal device; the base station subsystem includes a mobile base station, a wireless transceiver device, a dedicated network, a wireless digital device and the like; the subsystem can be regarded as a converter between a wireless network and a wired network; since the signal coverage of each communication base station of the network coverage presents a hexagon, which is similar to a honeycomb, it is called a cellular network; according to the shape characteristics of the cellular network, in order to improve the positioning accuracy, not only the signal parameters of one base station can be obtained, but also the signal parameters of multiple base stations can be obtained; the multiple base stations can form a corresponding base station network, so that all positions in the area covered by the base station network have one-to-one corresponding signal parameters; thus, the mobile terminal can obtain the signal parameters in the multiple base station networks corresponding to the current position through the Kalman positioning module, and the multiple base station networks can be, for example, a base station network composed of 6 base stations.

[0122] The Kalman positioning module is controlled to obtain the signal parameters of the current position of the target user, and according to the one-to-one correspondence between the signal parameters and the position information, the position information of the target user is determined, that is, the positioning of the current position of the target user is realized, and the current position is determined as the initial position of the target user; at this time, the initial position can also be used as the starting point of the PDR positioning module for moving path deduction, that is, the PDR positioning module determines the initial position as the initial position, so as to improve the efficiency of the PDR positioning module for positioning the position of the target user.

[0123] For example, the user terminal is configured with a positioning module, and the positioning module can include a PDR positioning module and a Kalman positioning module; the Kalman positioning module is used to obtain the received signal strength corresponding to the current position of the target user in a base station network composed of six base stations: "(RSSI1, RSSI2, RSSI3, RSSI4, RSSI5, RSSI6)", according to the association between the received signal strength and the position information, the position information corresponding to the received signal strength (RSSI1, RSSI2, RSSI3, RSSI4, RSSI5, RSSI6) is determined, and the position information is determined as the initial position of the target user, and the initial position is also determined as the initial position of the PDR positioning module.

[0124] S202: Real-time acquisition of the speed parameter of the target user, and determination of whether the speed parameter is greater than a preset speed parameter.

[0125] Wherein, the speed parameter is used to indicate the movement of the target user, and the preset speed parameter can be 0.7 m / s for example 2 ; the predicted position is used to indicate the position information obtained by the PDR positioning module for path derivation of the target user, and the observed position is used to indicate the position information obtained by the Kalman positioning module for path derivation of the target user.

[0126] It can be understood that the PDR positioning module is provided with a speed sensor, which can detect the movement of the target user, and the speed sensor can be an acceleration sensor, which can detect the acceleration of the target user during movement, thereby reflecting the movement of the target user. The speed parameter obtained by the speed sensor can reflect the movement of the target user, and the embodiment does not make special limitation on the speed parameter.

[0127] The speed sensor provided in the PDR positioning module is used to acquire the speed parameter of the target user in real time, and determine whether the speed parameter is greater than a preset speed parameter.

[0128] If the speed parameter is greater than the preset speed parameter, it indicates that the target user is in a moving state, and at this time, the path of the target user is deduced through the PDR positioning module and the Kalman positioning module; the position information of the target user at the next moment can be predicted according to the position information and the speed parameter obtained at the last moment through the PDR positioning module, and the predicted position information is the predicted position; the signal parameters of a plurality of base station networks at the position of the target user are obtained through the Kalman positioning module, and the current signal parameters are observed according to the signal parameters corresponding to all positions in the current plurality of base station networks, so as to obtain the observed position of the target user by the Kalman positioning module, that is, the predicted position and the observed position of the target user are determined based on the extended Kalman filtering algorithm through the PDR positioning module and the Kalman positioning module respectively.

[0129] If the speed parameter is not greater than the preset speed parameter, it indicates that the target user is not in a moving state, that is, the target user stays at the initial position, and at this time, the path of the target user does not need to be deduced, and only the initial position of the target user needs to be updated through the Kalman positioning module.

[0130] For example, the acceleration of the target user is obtained in real time through the acceleration sensor, and whether the target user is currently in a moving state is judged according to the acceleration; if the current speed parameter is 1m / s 2 , 1m / s 2 > 0.7m / s 2 , it indicates that the target user is in a moving state, and at this time, the path of the target user is deduced through the PDR positioning module and the Kalman positioning module, and two position information corresponding to the PDR positioning module and the Kalman positioning module are obtained; if the current speed parameter is 0.1m / s 2 , 0.1m / s 2 < 0.7m / s 2 , it indicates that the target user is not in a moving state, and at this time, the target user stays at the initial position, and only the initial position of the target user needs to be updated through the Kalman positioning module.

[0131] S203: When the speed parameter is greater than the preset speed parameter, the predicted position and the observed position of the target user are determined based on the extended Kalman filtering algorithm through the PDR positioning module and the Kalman positioning module respectively.

[0132] S204: The final position of the target user is determined according to the predicted position and the observed position.

[0133] Among them, the final position is used to indicate the destination of the target user at this time.

[0134] The predicted position determined by the PDR positioning module is sent to the Kalman positioning module, and the predicted position and the observed position are fused by the Kalman positioning module; and the final position of the target user is determined according to the position information obtained after the fusion processing.

[0135] For example, the predicted position determined by the PDR positioning module can be PDR coordinates, and the observed position determined by the Kalman positioning module can be Kalman coordinates. The PDR coordinates are sent to the Kalman positioning module, the Kalman positioning module is controlled to fuse the PDR coordinates and the Kalman coordinates, the final position of the target user, i.e., the final coordinates, is determined according to the position information obtained after the fusion processing, and the specific form of the position information can be coordinates, for example.

[0136] It can be understood that when the target user is indoors, the position information of the target user represented by the latitude and longitude data is not accurate when the target user is positioned indoors by using the positioning module, and there is a positioning error. Therefore, the corresponding coordinate system can be constructed according to the current position of the user, and the position information of the target user is represented by the corresponding coordinate information, so as to improve the positioning accuracy of the target user when the target user is positioned indoors, and the position fingerprint positioning method can also be used for outdoor positioning of the target user. The establishment of the coordinate system is not specially limited, and the application scenario of the position fingerprint positioning method is not specially limited.

[0137] The position fingerprint positioning method provided in the embodiment controls the Kalman positioning module to position the current position of the target user, and determines the obtained current position as the initial position of the target user; then the speed parameter of the target user is obtained in real time, and it is judged whether the speed parameter is greater than a preset speed parameter; when the speed parameter is greater than the preset speed parameter, the predicted position and the observed position of the target user are determined based on the PDR positioning module and the Kalman positioning module based on the extended Kalman filtering algorithm; the obtained predicted position and observed position are fused, so as to determine the final position of the target user. The method improves the positioning accuracy of the mobile terminal, reduces the positioning cumulative error generated in the positioning process of the mobile terminal, provides a new positioning method based on the fusion of sensor technology based on the extended Kalman filtering algorithm, and improves the user experience.

[0138] Figure 3 Flowchart of the position fingerprint positioning method provided in the embodiment Figure 2 . As shown in Figure 3 , the embodiment is based on Figure 2 the embodiment, and the position fingerprint positioning method is described in detail. The position fingerprint positioning method shown in the embodiment includes:

[0139] S301: The control positioning module locates the current position of the target user, and determines the current position as the initial position of the target user.

[0140] Step S301 is similar to step S201 described above, and will not be repeated here.

[0141] S302: Real-time acquisition of the speed parameter of the target user, and determining whether the speed parameter is greater than the preset speed parameter.

[0142] The speed parameter of the target user is acquired in real time by the speed sensor provided in the PDR positioning module, and it is determined whether the speed parameter is greater than the preset speed parameter.

[0143] If the speed parameter is not greater than the preset speed parameter, it indicates that the target user is not in a moving state, i.e., the target user stays at the initial position, and at this time, the path of the target user does not need to be derived, and only the initial position of the target user is updated by the Kalman positioning module.

[0144] If the speed parameter is greater than the preset speed parameter, it indicates that the target user is in a moving state, and at this time, the path of the target user is derived by the PDR positioning module and the Kalman positioning module, i.e., the predicted position and the observed position of the target user are determined based on the extended Kalman filtering algorithm according to the PDR positioning module and the Kalman positioning module.

[0145] For example, the acceleration of the target user is acquired in real time by the acceleration sensor, and it is determined whether the target user is currently in a moving state according to the acceleration; if the currently acquired speed parameter is 0.1 m / s 2 , 0.1 m / s 2 <0.7 m / s 2 , it indicates that the target user is not in a moving state, at this time, the target user stays at the initial position, and only the initial position of the target user is updated by the Kalman positioning module; if the currently acquired speed parameter is 1 m / s 2 , 1 m / s 2 >0.7 m / s 2 , it indicates that the target user is in a moving state, at this time, the path of the target user is derived by the PDR positioning module and the Kalman positioning module, and two position information corresponding to the PDR positioning module and the Kalman positioning module are obtained.

[0146] S303: When the speed parameter is not greater than the preset speed parameter, the position information of the target user is acquired according to a preset period.

[0147] S304: According to the plurality of position information acquired in the preset period, the initial positions of the plurality of target users are determined.

[0148] S305: Perform calculation processing on the multiple initial positions to obtain new position information, and determine the new position information as the new initial position of the target user.

[0149] The preset period can be, for example, 5 minutes.

[0150] When the speed parameter is not greater than the preset speed parameter, the Kalman positioning module acquires, at a preset period, a signal parameter of a position currently occupied by the target user, and matches the signal parameter with a signal parameter in the fingerprint database, so as to determine the position information of the target user at present, that is, to update the initial position of the target user at the preset period, and the position information acquired at the preset period can be multiple; according to the multiple position information acquired at the preset period, the initial positions of the target user at different times can be determined; calculation processing is performed on the multiple initial positions to obtain new position information, and the new position information is determined as the new initial position of the target user.

[0151] For example, the Kalman positioning module acquires, at a frequency of 5 minutes / time, a received signal strength of a position currently occupied by the target user, and matches the received signal strength with a received signal strength in the fingerprint database, so as to obtain a coordinate of the position currently occupied by the target user, and the coordinate can be (x, y); the Kalman positioning module acquires multiple coordinate information, and performs mean value processing on the multiple coordinate information to obtain a final coordinate Then the new initial position of the target user is The application does not specially limit the calculation processing method of the multiple initial positions.

[0152] Optionally, the generation of the fingerprint database comprises:

[0153] Acquire a locatable area of the positioning module; control the Kalman positioning module to collect signal parameters in the locatable area; and establish a fingerprint database corresponding to the locatable area according to the signal parameters.

[0154] The locatable area refers to an area that can be covered by multiple base station networks, the signal parameters are used to indicate characteristic parameters of the multiple base station networks, and the signal parameters can be, for example, received signal strengths; the fingerprint database is used to indicate data information in which all geographical positions in the locatable area correspond to received signal strengths one by one.

[0155] It can be understood that, before positioning the target user, a database containing position fingerprints, that is, a fingerprint database, needs to be constructed; and the fingerprint database takes signal parameters of multiple base station networks as “position fingerprints”, so that the fingerprint database contains data information in which all positions in an area currently covered by the multiple base station networks correspond to signal parameters one by one, that is, a set of the data information constitutes the fingerprint database.

[0156] Obtain the area covered by the plurality of base station networks where the current mobile terminal is located, and determine the area as the locatable area of the positioning module; control the Kalman positioning module to collect signal parameters in the locatable area; and establish the association between the geographical position and the signal parameters according to the signal parameters, thereby generating a fingerprint database corresponding to the locatable area.

[0157] S306: When the speed parameter is greater than the preset speed parameter, determine the movement parameter of the target user according to the PDR positioning module.

[0158] S307: Control the PDR positioning module to perform prediction processing on the position of the target user according to the movement parameter, and obtain the predicted position of the target user.

[0159] The movement parameter includes the step length of the target user and the number of steps of the target user.

[0160] It can be understood that a plurality of sensors are arranged in the PDR positioning module, including a speed sensor and a direction sensor, which can detect the moving direction and speed of the target user, thereby determining the movement of the target user, and the speed sensor can be an acceleration sensor, for example.

[0161] The PDR positioning module updates the position information of the target user according to the position information, the speed parameter and the movement parameter obtained at the moment, thereby realizing the prediction processing on the position information of the target user at the next moment, and the predicted position information is the predicted position.

[0162] Preferably, the control of the PDR positioning module to perform prediction processing on the position of the target user includes:

[0163] The position of the target user is predicted by using the following formula:

[0164] X k =f k (X k-1 )+W k-1 (1)

[0165] X k is the position information of the target user at time k obtained by the PDR positioning module;

[0166] f k (X k-1 ) is a nonlinear relationship from the state vector at time k-1 to the state vector at time k;

[0167] W k-1 is the Gaussian white noise input by the system at time k-1.

[0168] S308: Obtain a target signal parameter corresponding to the predicted position of the target user.

[0169] S309: Match the target signal parameter with signal parameters in a fingerprint database, and determine an observation position of the target user according to a matching result.

[0170] The target signal parameter is used to indicate a plurality of signal parameters acceptable to a base station network at the predicted position obtained by the PDR positioning module.

[0171] After the PDR positioning module determines the predicted position of the target user, the Kalman positioning module obtains a target signal parameter corresponding to the predicted position. The Kalman positioning module obtains signal parameters of a plurality of base station networks at the position where the target user is located, and performs matching processing on the currently obtained target signal parameter according to signal parameters corresponding to all positions in the current plurality of base station networks, so as to obtain an observation position of the target user by the Kalman positioning module.

[0172] Preferably, the Kalman positioning module obtains received signal strengths of a plurality of base station networks at the position where the target user is located, and performs matching processing on the currently obtained received signal strengths according to received signal strengths corresponding to all positions in the current plurality of base station networks, so as to obtain an observation position of the target user by the Kalman positioning module. The obtained observation position can be expressed by the following formula:

[0173] Z k =h k (X k )+V k (2)

[0174] Z k is position information of the target user at time k obtained by the Kalman positioning module after calculation and processing;

[0175] h k (X k ) is corresponding position information in the fingerprint database at time k;

[0176] V k is observation Gaussian white noise at time k.

[0177] S310: Update the predicted position and the observation position.

[0178] S311: Determine a measurement gain of the positioning module according to a result of the update.

[0179] The measurement gain is used to indicate that the predicted position and the observation position complement and correct each other.

[0180] After obtaining the predicted position determined by the PDR positioning module and the observed position determined by the Kalman positioning module, the predicted position information at the current time determined by the PDR positioning module is updated in time to obtain the position information at the next time predicted by the PDR positioning module; according to the position information at the next time determined by the PDR positioning module, the Kalman positioning module obtains the signal parameters corresponding to the position, and compares the signal parameters with the signal parameters in the fingerprint database, so as to obtain the position information at the next time predicted by the Kalman positioning module, that is, to realize the update of the predicted position and the observed position in time; the predicted position and the observed position are updated in time by using the following formula:

[0181]

[0182]

[0183]

[0184]

[0185]

[0186] In the formula, is the predicted position information of the target user obtained at k-1 time;

[0187] is the observed position information obtained according to the predicted position information of the target user;

[0188] φ k is an n-order state transition matrix;

[0189] H k is an m*n dimensional observation matrix;

[0190] is the predicted variance matrix of the state vector;

[0191] Q k is the system noise at k time.

[0192] According to the currently obtained H k and the current measurement gain of the positioning module set on the mobile terminal can be determined, and the measurement gain is determined by using the following formula:

[0193]

[0194] In the formula, K k is the current measurement gain of the positioning module.

[0195] S312: determining the final position of the target user according to the measurement gain.

[0196] According to the currently determined measurement gain, the predicted position determined by the PDR positioning module and the observed position determined by the Kalman positioning module are corrected, and according to the result of the correction processing, the final position of the target user is determined.

[0197] Preferably, according to the measurement gain, the final position of the target user is determined, comprising:

[0198] The final position of the target user is determined by using the following formula:

[0199]

[0200] Wherein, is the position information of the target user at time k obtained after fusion processing of the predicted position and the observed position;

[0201] is the predicted value of the position information at time k obtained by the PDR positioning module according to the position information at time k-1;

[0202] K k is the measurement gain of the fusion processing of the predicted position and the observed position;

[0203] Z k is the position information of the target user at time k obtained after calculation processing by the Kalman positioning module;

[0204] is the predicted value of the position information at time k obtained by the Kalman positioning module according to the position information at time k-1.

[0205] Optionally, the method further comprises:

[0206] After determining the final position of the target user, the speed parameter of the target user is reacquired, and it is judged whether the speed parameter is greater than the preset speed parameter; when the speed parameter is not greater than the preset speed parameter, the final position is determined as the initial position when the PDR positioning module is positioned again, and the initial position is updated according to the preset period.

[0207] After determining the final position of the target user, the speed parameter of the target user is reacquired, and it is judged whether the speed parameter is greater than a preset speed parameter; when the speed parameter is greater than the preset speed parameter, it indicates that the target user is still in a moving state, at this time, the position information of the target user at the next time is continuously predicted according to the PDR positioning module and the Kalman positioning module, the predicted position and the observed position of the target user are respectively determined based on the extended Kalman filtering algorithm, and thus the final position of the target user is determined; when the speed parameter is not greater than the preset speed parameter, the final position is determined as the initial position when the PDR positioning module is positioned again, and the signal parameter of the position where the target user is currently located is acquired according to the preset period, the signal parameter is matched with the signal parameter in the fingerprint database, and thus the current position information of the target user is determined; according to the plurality of position information acquired in the preset period, the initial positions of the target user at different times are determined; the plurality of initial positions are calculated and processed to obtain new position information, and the new position information is determined as the new initial position of the target user, and the update of the initial position is realized.

[0208] The position fingerprint positioning method provided by the embodiment controls the Kalman positioning module to position the current position of the target user, and determines the obtained current position as the initial position of the target user; then the speed parameter of the target user is acquired in real time, and it is judged whether the speed parameter is greater than a preset speed parameter; when the speed parameter is not greater than the preset speed parameter, the initial position of the target user is updated according to a preset period; when the speed parameter is greater than the preset speed parameter, the moving parameter of the target user is determined according to the PDR positioning module, and the PDR positioning module is controlled to predict and process the position of the target user to obtain the predicted position of the target user; the target signal parameter corresponding to the predicted position of the target user is acquired, the target signal parameter is matched with the signal parameter in the fingerprint database, and the observed position of the target user is determined according to the matching processing result; after obtaining the predicted position and the observed position of the target user, the corresponding position information is respectively updated, and the measurement gain of the current positioning module is determined according to the update result. The method optimizes the positioning method of the mobile terminal, improves the positioning accuracy of the mobile terminal, reduces the positioning cumulative error generated in the positioning process of the mobile terminal, provides a new positioning method based on the fusion of sensor technology based on the extended Kalman filtering algorithm, and improves the user experience.

[0209] Figure 4 The structure diagram of the position fingerprint positioning device provided by the present application is shown in the figure. Figure 4 As shown in the figure, the present application provides a position fingerprint positioning device, which comprises:

[0210] The control module 401 is used to control the positioning module to locate the current location of the target user. The positioning module includes a PDR positioning module and a Kalman positioning module.

[0211] The determination module 402 is used to determine the current position as the initial position of the target user.

[0212] The acquisition module 403 is used to acquire the speed parameters of the target user in real time, and the speed parameters are used to indicate the movement of the target user.

[0213] The judgment module 404 is used to determine whether the speed parameter is greater than the preset speed parameter.

[0214] The determining module 402 is further configured to, when the speed parameter is greater than the preset speed parameter, determine the predicted position and observed position of the target user based on the extended Kalman filter algorithm according to the PDR positioning module and the Kalman positioning module.

[0215] The determining module 402 is further configured to determine the final location of the target user based on the predicted location and the observed location.

[0216] Optionally, the determining module 402 is further configured to determine the movement parameters of the target user based on the PDR positioning module, wherein the movement parameters are used to indicate parameter information generated during the process of the target user moving from the initial position to the final position.

[0217] The location fingerprint positioning device further includes a processing module 405.

[0218] The processing module 405 is used to control the PDR positioning module to perform prediction processing on the target user's position according to the movement parameters, so as to obtain the predicted position of the target user.

[0219] The acquisition module 403 is also used to acquire target signal parameters corresponding to the predicted location of the target user.

[0220] The processing module 405 is further configured to match the target signal parameters with the signal parameters in the fingerprint database.

[0221] The determining module 402 is further configured to determine the observation location of the target user based on the matching processing result.

[0222] Optionally, the processing module 405 is further configured to predict the location of the target user using the following formula:

[0223] X k =f k (X k-1)+W k-1

[0224] wherein, X k is the position information of the target user at time k obtained after calculation and processing by the PDR positioning module;

[0225] f k (X k-1 ) is a nonlinear relationship from the state vector at time k-1 to the state vector at time k;

[0226] W k-1 is the Gaussian white noise of the system input at time k-1;

[0227] The processing module 405 is further configured to perform matching processing on the target signal parameter and the signal parameter in the fingerprint database by using the following formula:

[0228] Z k =h k (X k )+V k

[0229] wherein, Z k is the position information of the target user at time k obtained after calculation and processing by the Kalman positioning module;

[0230] h k (X k ) is the corresponding position information in the fingerprint database at time k;

[0231] V k is the observation Gaussian distributed white noise at time k.

[0232] Optionally, the processing module 405 is further configured to perform updating processing on the predicted position and the observed position.

[0233] The determining module 402 is further configured to determine a measurement gain of the positioning module according to the result of the updating processing, wherein the measurement gain is used to indicate that the predicted position and the observed position are mutually complementary and corrected.

[0234] The determining module 402 is further configured to determine the final position of the target user according to the measurement gain.

[0235] Optionally, the determining module 402 is further configured to determine the final position of the target user by using the following formula:

[0236]

[0237] wherein, is the position information of the target user at time k obtained after fusion processing of the predicted position and the observed position.

[0238] a predicted value of position information at time k obtained by the PDR positioning module according to position information at time k-1;

[0239] K k a measurement gain for fusing the predicted position and the observed position;

[0240] Z k position information at time k of the target user obtained by the Kalman positioning module after calculation and processing.

[0241] a predicted value of position information at time k obtained by the PDR positioning module according to position information at time k-1.

[0242] Optionally, the acquisition module 403 is further configured to acquire the position information of the target user according to a preset period when the speed parameter is not greater than the preset speed parameter.

[0243] The determination module 402 is further configured to determine initial positions of a plurality of target users according to a plurality of pieces of position information acquired within the preset period.

[0244] The processing module 405 is further configured to perform calculation and processing on a plurality of initial positions to obtain new position information.

[0245] The determination module 402 is further configured to determine the new position information as a new initial position of the target user.

[0246] Optionally, the acquisition module 403 is further configured to reacquire the speed parameter of the target user after determining the final position of the target user.

[0247] The judgment module 404 is further configured to judge whether the speed parameter is greater than the preset speed parameter.

[0248] The determination module 402 is further configured to determine the final position as the initial position when the PDR positioning module is positioned again when the speed parameter is not greater than the preset speed parameter.

[0249] The processing module 405 is further configured to update the initial position according to the preset period.

[0250] Figure 5 A structural schematic diagram of a position fingerprint positioning device provided by the present application. As shown in Figure 5 The present application provides a position fingerprint positioning device 500, which comprises a receiver 501, a transmitter 502, a processor 503, and a memory 504.

[0251] a receiver 501 configured to receive instructions and data;

[0252] a transmitter 502 configured to transmit instructions and data;

[0253] a memory 504 configured to store computer-executable instructions;

[0254] a processor 503 configured to execute the computer-executable instructions stored in the memory 504 to implement each step of the position fingerprinting method performed by the position fingerprinting device described above. For details, please refer to the related description in the foregoing position fingerprinting method embodiments.

[0255] Optionally, the memory 504 can be independent or integrated with the processor 503.

[0256] When the memory 504 is independent, the electronic device further includes a bus configured to connect the memory 504 and the processor 503.

[0257] The present application also provides a computer-readable storage medium, which stores computer-executable instructions. When the processor executes the computer-executable instructions, the position fingerprinting method performed by the position fingerprinting device described above is implemented.

[0258] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all described as a series of action combinations, but those skilled in the art should know that the present application is not limited to the action order described, because according to the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0259] It should be further noted that, although each step in the flowchart is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise stated in this article, the execution of these steps has no strict order limit, and these steps can be executed in other order. Moreover, at least part of the steps in the flowchart can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with other steps or sub-steps or stages of other steps.

[0260] Those of skill in the art would understand that all or a portion of the steps, the functions of the modules / units in the systems, devices, etc. disclosed herein can be embodied in software, firmware, hardware, or any suitable combination thereof. In hardware implementation, the division of the functions between the modules / units referred to in the above description does not necessarily correspond to the division made in the physical components; for example, one physical component can serve multiple functions, or one function or step can be performed by several physical components working in cooperation. Certain physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software can be distributed on computer readable media, which can comprise any available media or technology that can be accessed by a computer. As a non-limiting example, computer readable media can include computer storage media and communication media. Computer storage media includes volatile and non- volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Further, as a non-limiting example, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.

[0261] In the above embodiments, the description of each embodiment focuses on different aspects, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments. Each technical feature of the above embodiments can be combined arbitrarily. In order to make the description brief, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not exist contradictions, they should be considered as the scope of the disclosure.

[0262] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the application be limited only by the scope of the claims, including any appropriate equivalents. The specification and examples given are exemplary only. The true scope and spirit of the application are indicated by the following claims.

[0263] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments, and the above examples are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method of position fingerprinting, characterized in that, The method comprises: controlling a positioning module to locate a current position of a target user, and determining the current position as an initial position of the target user, the positioning module comprising a PDR positioning module and a Kalman positioning module; acquiring a speed parameter of the target user in real time, and determining whether the speed parameter is greater than a preset speed parameter, the speed parameter being used to indicate a movement condition of the target user; when the speed parameter is greater than the preset speed parameter, determining a movement parameter of the target user according to the PDR positioning module, the movement parameter being used to indicate parameter information generated in a process in which the target user moves from the initial position to a final position; performing prediction processing on the position of the target user according to the movement parameter by using the following formula: wherein, calculating, for the PDR positioning module, post-processing obtained position information of the target user k at the moment; a nonlinear relationship from a state vector at k-1 to a state vector at k; a Gaussian white noise input to the system at k-1; when the speed parameter is greater than the preset speed parameter, acquiring a target signal parameter corresponding to a predicted position of the target user according to the Kalman positioning module; performing matching processing on the target signal parameter and a signal parameter in a fingerprint database by using the following formula: wherein, calculating the position information of the target user k at the time k after processing for the Kalman positioning module; corresponding position information in the fingerprint database at time k; the observation Gaussian distribution white noise at time k; determining an observed position of the target user according to a matching processing result; performing updating processing on the predicted position and the observed position; determining a measurement gain of the positioning module at present according to a result of the updating processing, the measurement gain being used to indicate that the predicted position and the observed position complement and correct each other; determining a final position of the target user according to the measurement gain.

2. The method of claim 1, wherein, The determining of the final position of the target user according to the measurement gain comprises: determining the final position of the target user by using the following formula: wherein, is the position information of the target user k at time t after fusion processing of the predicted position and the observed position. a k-time position information prediction value obtained by the PDR positioning module according to the position information at k-1 time; a measurement gain for the fusion processing of the predicted position and the observed position; calculating position information of the target user k at a time after processing for the Kalman positioning module; is the position information prediction value of the Kalman positioning module at time k according to the position information at time k-1.

3. The method of claim 1, wherein, The method further comprises: when the speed parameter is not greater than the preset speed parameter, acquiring position information of the target user according to a preset period; determining multiple initial positions of the target user according to multiple pieces of position information acquired within the preset period; performing calculation processing on the multiple initial positions to obtain new position information, and determining the new position information as new initial position of the target user.

4. The method of claim 1, wherein, The method further comprises: after the final position of the target user is determined, reacquiring a speed parameter of the target user, and determining whether the speed parameter is greater than the preset speed parameter; when the speed parameter is not greater than the preset speed parameter, determining the final position as the initial position when the PDR positioning module is positioned again, and updating the initial position according to a preset period.

5. A position fingerprinting apparatus, characterized in that The device comprises: a control module configured to control a positioning module to locate a current position of a target user, the positioning module comprising a PDR positioning module and a Kalman positioning module; a determination module configured to determine the current position as an initial position of the target user; an acquisition module configured to acquire a speed parameter of the target user in real time, the speed parameter being used to indicate a movement condition of the target user; a judgment module configured to determine whether the speed parameter is greater than a preset speed parameter. The determining module is further configured to determine a movement parameter of the target user according to the PDR positioning module when the speed parameter is greater than the preset speed parameter, the movement parameter being used to indicate parameter information generated in a process in which the target user moves from the initial position to a final position. The control module is further configured to perform prediction processing on the position of the target user according to the movement parameter by using the following formula: wherein, calculating, for the PDR positioning module, the position information of the target user k at the moment; a nonlinear relationship from the state vector at the moment k-1 to the state vector at the moment k; a Gaussian white noise input to the system at the moment k-1; The obtaining module is further configured to obtain a target signal parameter corresponding to the predicted position of the target user according to the Kalman positioning module when the speed parameter is greater than the preset speed parameter. The control module is further configured to perform matching processing on the target signal parameter and a signal parameter in a fingerprint database by using the following formula: wherein, calculating the position information of the target user k at the time point after processing for the Kalman positioning module; corresponding position information in the fingerprint database at time point k; the observed Gaussian white noise at time point k; The determining module is further configured to determine an observed position of the target user according to a result of the matching processing. The control module is further configured to perform updating processing on the predicted position and the observed position. The determining module is further configured to determine a measurement gain of the positioning module at present according to a result of the updating processing, the measurement gain being used to indicate that the predicted position and the observed position are mutually complementary and corrected. The determining module is further configured to determine a final position of the target user according to the measurement gain.

6. A position fingerprinting device, characterized by Comprise: a memory; a processor; wherein the memory stores computer-executed instructions; the processor executes the computer-executed instructions stored in the memory to implement the position fingerprint positioning method in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executed instructions, and the computer-executed instructions are executed by the processor to implement the position fingerprint positioning method in any one of claims 1 to 4.

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