Positioning and prediction method of radio signal iso-intensity boundary value based on mobile vector set

Abstract

The application discloses a positioning and prediction method of radio signal equal-intensity boundary value based on a mobile vector set, comprising the following steps: constructing an offline map of an indoor scene in a server; dividing the scene into grids, collecting signal intensity values of Bluetooth beacons at the center of each grid, dividing the signal intensity values into intensity value regions, and obtaining intensity boundary lines after numerical fitting; a user carries a wireless device to move in the scene, cuts the intensity boundary lines, and obtains a dynamically updated mobile vector set; at each time, a position estimation region is constructed according to position estimation results of multiple mobile vectors; and multiple position estimation results are fused through Bayesian estimation, that is, the position at the current time is predicted by combining historical data before the current time. The application adopts a strategy of mapping first and then positioning, the user downloads a scene map through the server, and realizes information fusion calculation through a positioning algorithm, so that accurate positioning and prediction of the user are realized.

Description

Technical Field

[0001] This invention relates to the field of positioning and navigation, and in particular to a positioning and prediction method based on the equal strength boundary values ​​of wireless signals using a motion vector set. Background Technology

[0002] With the increasing demand for indoor positioning, such as shopping mall guides, finding cars or exits in underground parking lots, and navigation in large museums or airports, it is necessary to obtain the user's current location information.

[0003] Currently, there are many indoor positioning solutions, such as those using Wi-Fi positioning technology, UWB positioning technology, and laser positioning technology. However, due to limitations such as complex indoor environments, positioning accuracy and efficiency, equipment costs, and scalability, these solutions cannot completely solve the positioning problem in indoor scenarios.

[0004] Therefore, a pressing issue for those skilled in the art is how to develop a low-cost, efficient, and highly accurate indoor positioning method and system that utilizes the user's own equipment without requiring additional devices, in order to meet the growing demand. Summary of the Invention

[0005] The technical problem to be solved by this invention is to address the shortcomings of current indoor positioning methods or systems, such as the need for users to equip themselves with additional equipment and low positioning accuracy and efficiency, by providing a positioning and prediction method based on the boundary values ​​of equal strength wireless signals using motion vector sets.

[0006] The technical solution adopted by this invention to solve its technical problem is:

[0007] This invention provides a method for localization and prediction of wireless signal strength boundary values ​​based on mobile vector sets, comprising the following steps:

[0008] Offline phase:

[0009] An offline map of an indoor scene is built on the server, including: a bottom-level vector 2D map, a middle-level scene feature layer, and a high-level retrieval information layer;

[0010] Multiple Bluetooth beacons are set up in the scene. The scene is divided into grids according to the offline map. The signal strength value of the Bluetooth beacons in the scene is collected at the center point of each grid. The signal strength value is divided into intensity value regions. After numerical fitting, the intensity boundary line is obtained.

[0011] Online phase:

[0012] As the user moves within the scene carrying a wireless device, the intensity boundary line is cut to obtain a dynamically updated set of movement vectors.

[0013] Spacing constraints are applied based on the positions of the user's wireless device and human body mass in the scene. The feasible solution for the spacing constraints is obtained by combining the signal attenuation model and the minimum error optimization model.

[0014] Based on the set of moving vectors, a regionalization method is introduced. At each time step, a location estimation region is constructed based on the location estimation results of multiple moving vectors. The location estimation region represents the possible range of location estimation. Combined with the feasible solution of the spacing constraint, the multiple location estimation results are fused by Bayesian estimation, that is, the location at the current time is predicted by combining historical data before the current time.

[0015] Furthermore, the offline map of the present invention specifically includes:

[0016] The underlying scene vector 2D map is used to store the basic structure and scale information of the scene, which is obtained by traversing and mapping the scene using a laser surveying instrument;

[0017] The middle layer is the scene feature layer, which includes wireless signal fingerprint features and geomagnetic signal fingerprint features of sampling points in the scene;

[0018] The higher-level retrieval information layer is used to associate the lower-level and middle-level information, and to call the lower-level and middle-level information when performing location calculations.

[0019] Furthermore, the method for obtaining the intensity boundary line according to the present invention includes:

[0020] Divide the scene into a grid, and determine the grid size and the coordinates of the grid center point;

[0021] The strength value of the Bluetooth beacon signal in the scene is collected at the center point of each grid, and the data is collected multiple times. After removing the values ​​with strength values ​​less than a certain threshold, the data with strength values ​​is initially filtered by a median filter and then stored.

[0022] The minimum and maximum strength values ​​are taken and divided into k equal parts to construct an equal strength region for each Bluetooth beacon;

[0023] Select grids with intensity values ​​equal to the boundaries of regions with equal intensity, and connect the center points of the grids to obtain k+1 boundary lines of regions with equal intensity. Use a numerical fitting tool to perform polynomial fitting on the boundary lines to obtain the curve function of the intensity boundary lines.

[0024] Furthermore, the curve function for obtaining the intensity boundary line in this invention is specifically as follows:

[0025]

[0026]

[0027] Among them, b0 to b N The coefficients of the curve function; v p and v q Let x be the strength value corresponding to the equal strength boundary, and N be the order of the polynomial. After obtaining the polynomial expressions for k+1 boundary lines, the equal strength line corresponding to any strength is calculated by interpolation. p ,y p The intensity value is v p Points on the isointense lines, x q ,y q The intensity value is v q Points on the isointense lines, x i ,y i The intensity value is v i Points on the isointense lines satisfy the following relationship:

[0028]

[0029] This allows us to calculate the polynomial expression for the strength boundary line of equal strength corresponding to any strength.

[0030] Furthermore, the method for obtaining the movement vector set according to the present invention includes:

[0031] As a user moves with a wireless device within the scene, cutting through the intensity boundary line, there are n movement vectors when there are n Bluetooth beacons in the scene; where Let k-1 be the signal strength of the nth Bluetooth beacon received in the scene. Let k be the signal strength of the scene receiving the nth Bluetooth beacon. Let be the difference between the signal strength value of the nth Bluetooth beacon received at time k and the signal strength value of the nth Bluetooth beacon received at time k-1; then:

[0032]

[0033]

[0034]

[0035] …

[0036]

[0037] Let A be the set of movement vectors generated by cutting boundary lines of different intensities. A is a dynamically updated set:

[0038] .

[0039] Furthermore, the method of the present invention for obtaining a feasible solution to the spacing constraint includes:

[0040] For the i-th Bluetooth beacon, the user's mobile device receives a total of 3 signal strength values, which are the signal strength values ​​received by the mobile device's built-in Bluetooth, v_i ... pi The signal v received by the left and right Bluetooth earbuds li and v ri ; calculate v li and v ri mean v mi The intensity value received by a human body particle:

[0041]

[0042] The final signal strength value in the scene where the mobile device is located is:

[0043]

[0044] The signal strength difference between the mobile device and the human body mass point is used as a positioning constraint for calculation. Let the coordinates of the mobile device be (x...). s y s The coordinates of the human body mass point are (x b y b It contains the following 3 constraints:

[0045] Given the constraint of the Bluetooth beacon signal strength received by the mobile device's built-in Bluetooth, the strength value of the mobile device at the i-th Bluetooth beacon in the scene is v. i On the isointense lines:

[0046]

[0047] If the strength of the Bluetooth beacon signal received by a human mass point is constrained, then its position will lie on the isointense line corresponding to that strength:

[0048]

[0049] The distance between the human body mass point and the mobile device is constrained to be one arm's length. A signal attenuation model is introduced for calculation.

[0050]

[0051] Using the minimum error optimization model, feasible solutions under three constraints are obtained:

[0052]

[0053] in, This is the signal attenuation coefficient corresponding to distance, with units of dB / m. d is an empirical coefficient, N1 represents the distance between the human body mass point and the mobile device, N2 represents the number of Bluetooth signal sources directly measured through the mobile device itself, and N2 represents the number of Bluetooth signal sources indirectly measured through the device on the human body mass point.

[0054] Furthermore, the regionalization method of the present invention specifically includes:

[0055] At each time step, based on the estimation results of multiple movement vectors, a location estimation region R is constructed, which represents the possible range of the location estimation:

[0056]

[0057] x m y m Represents the coordinates of the m-th point within region R.

[0058] Find the target point Minimum distance to the curve:

[0059]

[0060] Choose a starting point for x. calculate At the current point The gradient is found by identifying the direction of the fastest ascent or descent at that point, and a learning rate β is set to update the value of x:

[0061]

[0062] Repeat the gradient calculation and update steps until the end for The point that reaches approximately the minimum value for( The shortest distance to the curve;

[0063] The coordinates of the target point are calculated from the coordinates of point RP in the set of coordinate points of region R, which are closest to the nearest intensity boundary line.

[0064] .

[0065] Furthermore, the method of the present invention for predicting the position at the current moment by combining historical data prior to the current moment includes:

[0066] The current coordinate estimate is Assuming the scenario allows for sampling time The time interval is during continuous sampling. If the motion is a linear motion with constant acceleration during the time interval, then:

[0067]

[0068] The observation equation is:

[0069]

[0070] in, P obsk (x,y) It is a feasible solution to the spacing constraint. The value at time k;

[0071] Modeling equations:

[0072]

[0073]

[0074] Where w and u are both noise, respectively obeying and Q represents noise. The covariance matrix; G represents the noise. The covariance matrix;

[0075]

[0076] Prior estimate:

[0077]

[0078] The prior covariance matrix of the error:

[0079]

[0080] In a scenario with n Bluetooth beacons, n estimated values ​​are calculated using a Kalman filter. The n estimates are then fused using Bayesian estimation to obtain the final estimated value. In other words, the position at time k is predicted using historical data from times k-1 and k-2.

[0081] This invention provides a localization and prediction system based on the equal strength boundary value of wireless signals using a motion vector set, for implementing a localization and prediction method based on the equal strength boundary value of wireless signals using a motion vector set, comprising:

[0082] The offline map building unit is used to build an offline map of an indoor scene on the server. It includes: a bottom-level vector 2D map, a middle-level scene feature layer, and a high-level retrieval information layer; the scene is divided into grids, the signal strength value of Bluetooth beacons in the scene is collected at the center point of each grid, the signal strength value is divided into intensity value regions, and the intensity boundary line is obtained after numerical fitting.

[0083] The online prediction unit is used to cut the intensity boundary line and obtain a dynamically updated set of movement vectors as the user moves with their wireless device in the scene. Spacing constraints are applied based on the positions of the user's wireless device and human body mass point in the scene, and a feasible solution for the spacing constraints is obtained by combining a signal attenuation model and a minimum error optimization model. Based on the movement vector set, a regionalization method is introduced to construct a position estimation region at each time step based on the position estimation results of multiple movement vectors. This position estimation region represents the possible range of position estimation. The feasible solution for the spacing constraints is obtained by fusing multiple position estimation results through Bayesian estimation, i.e., predicting the position at the current moment by combining historical data prior to the current moment.

[0084] The present invention provides a computer-readable storage medium storing a computer program for implementing the above-described method for positioning and predicting wireless signal strength boundary values ​​based on a mobile vector set when executed by a processor.

[0085] The beneficial effects of this invention are:

[0086] 1. The method of this invention employs a strategy of first building a map and then locating the user. First, a scene map is built offline and stored on a server. Then, the user downloads the scene map from the server and runs the location software. The location software integrates the location algorithm of this invention. By collecting the current Bluetooth signal and fusing it with the information stored in the scene map, the user's location information can be obtained and output for the user's use.

[0087] 2. In the method of this invention, using the center point of the grid instead of the entire grid fails to obtain the intensity value information of the grid edges. Therefore, when constructing equal-intensity regions, it is necessary to optimize the grid corners to ensure that the edges of the equal-intensity regions are smooth arcs. By selecting grids with intensity values ​​equal to the boundaries of the equal-intensity regions and connecting their center points, k+1 equal-intensity region boundary lines can be obtained. Using numerical fitting tools, polynomial fitting can be performed on the boundary lines to obtain the boundary line curve function. In this way, accurate and smooth boundary line curves can be obtained.

[0088] 3. In the method of this invention, the user terminal receives three signal strength values: the signal received by the built-in Bluetooth of the mobile device, the signal received by the left and right Bluetooth earbuds, and three constraints are considered when calculating the position: the AP signal strength constraint received by the built-in Bluetooth of the mobile device, the AP signal strength constraint received by the human body mass point, and the distance constraint between the human body mass point and the smartphone. This method collectively constrains the positions of the mobile device and the human body mass point in the scene, and a feasible solution under the three constraints is obtained using a minimum error optimization model.

[0089] 4. In the method of this invention, the position at the next moment is optimized and estimated based on the obtained set of movement vectors A and a prediction method based on historical data. This method makes full use of movement information based on multiple AP points. Due to the existence of movement vectors, the position estimation has a certain degree of uncertainty. To represent this uncertainty, this invention introduces a regionalization method. At each moment, a position estimation region R is constructed based on the estimation results of multiple movement vectors. This region represents the possible range of position estimation, and accurate position prediction is achieved through this method. Attached Figure Description

[0090] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0091] Figure 1 This is a positioning diagram according to an embodiment of the present invention;

[0092] Figure 2 This is the signal equal intensity boundary map constructed in this embodiment of the invention;

[0093] Figure 3 This is a flowchart of the localization and prediction system according to an embodiment of the present invention. Detailed Implementation

[0094] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0095] Example 1

[0096] like Figures 1-3 As shown, the wireless signal equal strength boundary value localization and prediction system and method based on mobile vector sets according to embodiments of the present invention includes the following two stages:

[0097] I. Offline Phase:

[0098] 1. Construct an offline map of the indoor scene on the server, including: a bottom-level vector 2D map, a middle-level scene feature layer, and a high-level retrieval information layer;

[0099] In a preferred embodiment of the present invention, the offline map specifically includes:

[0100] Offline mapping is used to construct scene maps, which consist of a bottom-level scene vector 2D map, a middle-level scene feature layer, and a high-level retrieval information layer.

[0101] The underlying scene vector 2D map is used to store the basic structure and scale information of the scene, which is obtained by traversing and mapping the scene using a laser mapping instrument.

[0102] The middle layer of scene features includes wireless signal (Bluetooth, WiFi, etc.) fingerprint features and geomagnetic signal fingerprint features of sampling points in the scene.

[0103] The higher-level retrieval information layer is used to associate the lower-level and middle-level information, making it convenient to call the lower-level and middle-level information when performing location calculations.

[0104] The following section provides a detailed explanation of the construction of the scene feature layer in the middle layer.

[0105] 2. Set up multiple Bluetooth beacons in the scene, divide the scene into grids according to the offline map, collect the signal strength value of the Bluetooth beacons in the scene at the center point of each grid, divide the signal strength value into intensity value regions, and obtain the intensity boundary line after numerical fitting;

[0106] In a preferred embodiment of the present invention, the method for obtaining the intensity boundary line after numerical fitting specifically includes:

[0107] Step 1: Divide the scene into a grid, and determine the grid size and the coordinates of the grid center point.

[0108] Step 2: Collect the signal value of the Bluetooth beacon in the scene at each grid center point, and perform multiple collections. After removing values ​​with an intensity value less than -85dB, use a median filter to perform preliminary filtering on the data, and then store the data in the storage mode {[x i ,y i ];[RSSI1,RSSI2,…,RSSIn]}, where [x i ,y i [RSS1, RSSI2, ..., RSSIn] represents the two-dimensional coordinates of the center point of the i-th grid in the entire scene, and [RSSI1, RSSI2, ..., RSSIn] represents the RSSI values ​​of the first to nth APs received.

[0109] Step 3: After initial fingerprint data collection, we can gain a preliminary understanding of the RSSI distribution of each AP point received by each grid point. We then take the minimum and maximum values, divide the data into k equal parts, and construct equal-intensity regions for each AP. Let the maximum value be v. max Minimum value v min To construct regions of equal intensity appropriately, the following strategy is adopted:

[0110]

[0111] In a preferred embodiment of the present invention, after dividing each AP in the scene using the above strategy, each AP has k equal-intensity regions in the scene. By dividing the Bluetooth signal strength value collected at the center point of each grid according to the intensity value region of the equal-intensity region, k Bluetooth beacon equal-intensity regions composed of grids can be obtained.

[0112] Since the gridded data acquisition in steps 1 and 2 is a discrete acquisition method, on the one hand, the density of acquisition points is limited by the grid size; on the other hand, using the grid center point to represent the entire grid makes it impossible to obtain the intensity value information of the grid edges. Therefore, when constructing equal-intensity regions, it is also necessary to optimize the grid corners so that the edges of the equal-intensity regions are smooth arcs.

[0113] Therefore, by selecting grids with intensity values ​​equal to the boundaries of regions of equal intensity and connecting the grid center points, k+1 boundary lines of regions of equal intensity can be obtained. Using numerical fitting tools, a polynomial fit can be performed on the boundary lines to obtain the boundary line curve function.

[0114]

[0115]

[0116] Among them, v p and v q Let x be the intensity value corresponding to the isointense boundary, and N be the order of the polynomial. Generally, N=3, i.e., a cubic polynomial fit. After obtaining the polynomial expressions for k+1 boundary lines, the isointense line corresponding to any intensity can be calculated using interpolation. Let x be the intensity value corresponding to the isointense boundary. p ,y p The intensity value is v p Points on the isointense lines, x q ,y q The intensity value is v q Points on the isointense lines, x i ,y i The intensity value is v i Points on the isointense lines satisfy the following relationship.

[0117]

[0118] The polynomial expression for the isobaric line corresponding to any intensity can be calculated using the above formula.

[0119] II. Online Phase:

[0120] 1. As the user moves within the scene while carrying a wireless device, the intensity boundary line is cut to obtain a dynamically updated set of movement vectors;

[0121] When a user moves within the scene, the fitted equal-intensity boundary lines in the scene are cut. If there are n action points (APs) in the scene, then there are n movement vectors; where... Let k-1 be the signal strength received from the nth AP in the scene, and similarly... Let k be the signal strength received by the scene from the nth AP. The difference between the signal strength value of the nth AP received at time k and the signal strength value of the nth AP received at time k-1;

[0122]

[0123]

[0124]

[0125] …

[0126]

[0127] Let A be the set of movement vectors generated by cutting lines of different intensities. A is a dynamically updated set:

[0128]

[0129] 2. Based on the positions of the user's wireless device and human body mass in the scene, distance constraints are applied. Combined with the signal attenuation model and the minimum error optimization model, feasible solutions to the distance constraints are obtained.

[0130] In a preferred embodiment of the present invention, the mobile device is a user's smartphone. The signal strength difference between the smartphone and the human body mass point is used as a positioning constraint for calculation. Let the coordinates of the smartphone be (x... s y s The coordinates of the human body mass point are (x b y b It includes the following constraints:

[0131] Constraint 1: Given the constraint on the signal strength of the AP received by the smartphone's built-in Bluetooth, the signal strength value of the smartphone at the i-th AP in the scene is v. i On the isointense lines:

[0132]

[0133] Constraint 2: If the AP signal strength received by the human mass point is constrained, then its position will lie on the isointense line of the corresponding strength.

[0134]

[0135] Constraints, human mass point, and smartphone distance constraints, with a distance of approximately one arm's length, are used to calculate signal attenuation using a signal attenuation model:

[0136]

[0137] in, This is the signal attenuation coefficient corresponding to distance, with units of dB / m. is an empirical coefficient, and d is the distance between the human body mass point and the smartphone.

[0138] The above three constraints collectively constrain the positions of the smartphone and the human body within the scene. In reality, it is difficult to obtain a single solution that perfectly satisfies all three constraints. Therefore, a minimum error optimization model is used to obtain a feasible solution under all three constraints.

[0139]

[0140] 3. Based on the set of moving vectors, a regionalization method is introduced. At each time step, a location estimation region is constructed based on the location estimation results of multiple moving vectors. The location estimation region represents the possible range of location estimation. Combined with the feasible solution of the spacing constraint, the multiple location estimation results are fused by Bayesian estimation, that is, the location at the current time is predicted by combining historical data before the current time.

[0141] Based on the obtained set of movement vectors A, a prediction method based on historical data is used to optimize the estimation of the position at the next moment. This method makes full use of movement information based on multiple AP points. However, due to the existence of movement vectors, the position estimation has a certain degree of uncertainty. To represent this uncertainty, we introduce a regionalization method. At each moment, based on the estimation results of multiple movement vectors, a position estimation region R is constructed, which represents the possible range of the position estimation.

[0142]

[0143] Find the target point Minimum distance to the curve:

[0144]

[0145] Choose a starting point for x. calculate At the current point The gradient is found by identifying the direction of the fastest ascent or descent at that point, and a learning rate β is set to update the value of x:

[0146]

[0147] Repeat the gradient calculation and update steps until the end for The point that reaches approximately the minimum value for( The shortest distance to the curve.

[0148] Iterate through the RP points in R and calculate the coordinates of the grid closest to the nearest contour line to obtain the target point coordinates:

[0149]

[0150] Each movement vector cuts through isointense lines of different access points (APs) in the scene. This property can be used to predict the user's next location using a Kalman filter.

[0151] The current coordinate estimate is Assuming the scenario allows for sampling time Let the time interval be assuming continuous sampling The model represents a linear motion with constant acceleration within the time interval:

[0152]

[0153] Observation equation:

[0154]

[0155] Among them, P obsk (x,y) is The value at time k;

[0156] Modeling equations:

[0157]

[0158]

[0159] Where w and u are both noise, and respectively obey the rules of noise. and

[0160]

[0161] Prior estimate:

[0162]

[0163] The prior covariance matrix of the error:

[0164]

[0165] In a scenario with n APs, n estimates can be calculated using the Kalman filter. The n estimates are then fused using Bayesian estimation to obtain the final estimate, which means that the position at time k is predicted using historical data from times k-1 and k-2.

[0166] Example 2

[0167] This invention provides a localization and prediction system based on the equal strength boundary value of wireless signals using a motion vector set, for implementing a localization and prediction method based on the equal strength boundary value of wireless signals using a motion vector set, comprising:

[0168] The offline map building unit is used to build an offline map of an indoor scene on the server. It includes: a bottom-level vector 2D map, a middle-level scene feature layer, and a high-level retrieval information layer; the scene is divided into grids, the signal strength value of Bluetooth beacons in the scene is collected at the center point of each grid, the signal strength value is divided into intensity value regions, and the intensity boundary line is obtained after numerical fitting.

[0169] The online prediction unit is used to cut the intensity boundary line and obtain a dynamically updated set of movement vectors as the user moves with their wireless device in the scene. Spacing constraints are applied based on the positions of the user's wireless device and human body mass point in the scene, and a feasible solution for the spacing constraints is obtained by combining a signal attenuation model and a minimum error optimization model. Based on the movement vector set, a regionalization method is introduced to construct a position estimation region at each time step based on the position estimation results of multiple movement vectors. This position estimation region represents the possible range of position estimation. The feasible solution for the spacing constraints is obtained by fusing multiple position estimation results through Bayesian estimation, i.e., predicting the position at the current moment by combining historical data prior to the current moment.

[0170] Example 3

[0171] This invention provides a positioning and prediction system based on the boundary value of wireless signal equal strength using a mobile vector set. The system consists of a user device, a Bluetooth signal base station, and a server. After downloading the scene map stored in the server, the user can calculate their current location information by sensing the surrounding Bluetooth signals.

[0172] The user equipment can receive Bluetooth signals in the scene, and the server stores scene map information, including vector map information and Bluetooth boundary information. In this embodiment of the method, a strategy of first building the map and then locating the user is adopted. First, the scene map is built offline and stored on the server. Then, the user downloads the scene map from the server and runs the location software. The location software integrates the location algorithm of this invention. By collecting the current Bluetooth signal and fusing it with the information stored in the scene map, the user's location information can be obtained and output for the user's use.

[0173] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0174] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method of locating and predicting radio signal isopleth boundaries based on a set of movement vectors, characterized by, The method comprises the following steps: An offline stage: Constructing an offline map of the indoor scene in the server, comprising: a bottom layer of a vector two-dimensional map, a middle layer of a scene feature layer, and a high layer of a retrieval information layer; Setting a plurality of Bluetooth beacons in the scene, performing grid division on the scene according to the offline map, collecting signal strength values of the Bluetooth beacons in the scene at each grid center point, and performing region division on the signal strength values to obtain strength boundary lines after numerical fitting; An online stage: The user carries a wireless device to move in the scene, cuts the strength boundary lines to obtain a dynamically updated moving vector set; According to the positions of the wireless device and the human body particle in the scene, a distance constraint is performed, a signal attenuation model and a minimum error optimization model are combined to calculate a feasible solution of the distance constraint; Based on the moving vector set, a regionalization method is introduced, at each time, a position estimation region is constructed according to the position estimation results of a plurality of moving vectors, the position estimation region represents a possible range of position estimation; the plurality of position estimation results are fused through Bayesian estimation in combination with the feasible solution of the distance constraint, that is, the position at the current time is predicted in combination with the historical data before the current time.

2. The mobile vector set based wireless signal isocenter boundary value positioning and prediction method according to claim 1, characterized in that, The offline map specifically comprises: The bottom layer of the scene vector two-dimensional map is used for storing the basic structure and scale information of the scene and is obtained by traversing and mapping the scene by using a laser mapper; The middle layer of the scene feature layer comprises wireless signal fingerprint features and geomagnetic signal fingerprint features of sampling points in the scene; The high layer of the retrieval information layer is used for associating the bottom layer information and the middle layer information, and the bottom layer information and the middle layer information are called during positioning calculation.

3. The mobile vector set based wireless signal isocenter boundary value positioning and prediction method according to claim 1, characterized in that, The method for obtaining the strength boundary lines comprises: Grid division is performed on the scene to determine the grid size and the grid center point coordinates; The strength values of the Bluetooth beacon signals in the scene are collected at each grid center point, and the strength values are stored after preliminary filtering by using a median filter after multiple collections and removal of values less than a certain threshold; The minimum value and the maximum value of the strength values are taken, k equidivisions are performed, and the equal-strength regions of each Bluetooth beacon are constructed; The grids with the strength values equal to the equal-strength region boundaries are selected, and the grid center points are connected to obtain k+1 equal-strength region boundary lines; a numerical fitting tool is used to perform polynomial fitting on the boundary lines to obtain a curve function of the strength boundary lines.

4. The positioning and prediction method of radio signal iso-intensity boundaries based on a set of movement vectors according to claim 3, characterized in that, The curve function of the strength boundary lines is specifically: wherein b0 to b N represent the coefficients of the curve function; v p represents the intensity value of the equal-intensity boundary; N represents the order of the polynomial; after obtaining the polynomial expression of the k+1 boundary lines, the equal-intensity line corresponding to any intensity value is calculated by the interpolation method, and it is assumed that x q represents the point on the equal-intensity line with the intensity value v p ; x p represents the point on the equal-intensity line with the intensity value v p ; x q represents the point on the equal-intensity line with the intensity value v q ; x q represents the point on the equal-intensity line with the intensity value v i ; and x i represents the point on the equal-intensity line with the intensity value v i , which satisfy the following relationship: Therefore, the polynomial expression of the equal-strength strength boundary line corresponding to any strength can be calculated.

5. The mobile vector set based wireless signal isocenter boundary value positioning and prediction method according to claim 1, characterized in that, The method for obtaining the moving vector set comprises: When the user carries the wireless device to move in the scene, the intensity boundary line is cut, and there are n moving vectors when there are n Bluetooth beacons in the scene; wherein is the signal strength of the n-th Bluetooth beacon received by the scene at k-1 moment, and similarly is the signal strength of the n-th Bluetooth beacon received by the scene at k moment, is the difference between the signal strength value of the n-th Bluetooth beacon received at k moment and the signal strength value of the n-th Bluetooth beacon received at k-1 moment; then: … The moving vectors generated by cutting different strength boundary lines are collectively denoted as A, and A is a dynamically updated set: 。 6. The mobile vector set based wireless signal isocenter boundary value positioning and prediction method according to claim 4, characterized in that, The method for obtaining the feasible solution of the distance constraint comprises: For the i-th Bluetooth beacon, the user's mobile device receives the strength values of 3 signals, which are v pi , v li and v ri , received by the mobile device's built-in Bluetooth, the left ear Bluetooth headset and the right ear Bluetooth headset respectively; the average of v li and v ri is calculated as v mi , which is the strength value received by the human body point: The signal strength value of the moving device in the scene is finally: The signal strength difference formed by the mobile device and the human body point is taken as a positioning constraint for calculation, and the coordinates of the mobile device are (x s , y s ), the coordinates of the human body point are (x b , y b ), and the following 3 constraints are included: The strength of the Bluetooth beacon signal received by the mobile device built-in Bluetooth is constrained, then the position of the mobile device in the scene is on the strength value v of the i-th Bluetooth beacon i isoelectric line: The human body particle receives the Bluetooth beacon signal strength constraint, and the position is on the equal-strength line corresponding to the strength: The distance constraint between the human body particle and the moving device is that the distance is an arm length, and a signal attenuation model is introduced for calculation: A minimum error optimization model is used to obtain the feasible solution under the three constraints: wherein, is the signal attenuation and distance corresponding coefficient, unit: db / m, is the empirical coefficient, d is the distance between the human body mass point and the mobile device; N1 represents the number of Bluetooth signal sources directly measured by the mobile device itself, and N2 represents the number of Bluetooth signal sources indirectly measured by the device on the human body mass point.

7. The mobile vector set based wireless signal isocenter boundary value positioning and prediction method according to claim 1, characterized in that, The regionalization method specifically comprises: At each time, a position estimation region R is constructed according to the estimation results of the plurality of movement vectors, and the region represents a possible range of position estimation: x m , y m represent the coordinates of the mth point in the R region; seek target point minimum distance to curve: Choose a starting point for x Compute At the current point The gradient finds the direction in which the function increases or decreases the most rapidly at that point. Set a learning rate β to update the value of x: The gradient calculation and update steps are repeated eventually To The point of the approximation minimum, To the shortest distance to the curve; ) The coordinates of the grid closest to the nearest intensity boundary line are calculated as the target point coordinates: 。 8. The mobile vector set based wireless signal isocenter boundary value positioning and prediction method according to claim 7, characterized in that, The method for predicting the position at the current time based on the historical data before the current time comprises: The current coordinate estimate is , assuming a scenario where the sampling time is a time interval, and the motion is a constant acceleration straight line motion during the consecutive sampling time interval, then: The observation equation is: wherein, P obsk (x,y) is a feasible solution of the distance constraint value at time k; The modeling equation is: where w and u are both noises, respectively obeying and ; Q denotes the covariance matrix of noise ; G denotes the covariance matrix of noise ​ The prior estimation value is: The prior covariance matrix of error is: There are n Bluetooth beacons in the scene, and n estimation values are calculated according to the Kalman filter, and the final estimation value is obtained by fusing the n estimations through Bayesian estimation, that is, the position at time k is predicted by using the historical data at times k-1 and k-2.

9. A positioning and prediction system for radio signal isocline boundary values based on a set of movement vectors, for implementing the method for positioning and prediction of radio signal isocline boundary values based on a set of movement vectors as claimed in any of claims 1 - 8, characterized in that, It comprises: An offline map construction unit is configured to construct an offline map of an indoor scene in a server, including a bottom vector two-dimensional map, a middle scene feature layer and a high search information layer; the scene is divided into grids, the signal strength values of the Bluetooth beacons in the scene are collected at the center points of the grids, the signal strength values are divided into regions, and the intensity boundary lines are obtained after numerical fitting; An online prediction unit is configured to cut the intensity boundary lines when a user carries a wireless device and moves in the scene, to obtain a dynamically updated movement vector set; the positions of the wireless device and the human body particles carried by the user in the scene are subjected to distance constraint, and a feasible solution of the distance constraint is obtained by combining a signal attenuation model and a minimum error optimization model; based on the movement vector set, a regionalization method is introduced, a position estimation region is constructed at each time according to the position estimation results of the plurality of movement vectors, and the position estimation region represents a possible range of position estimation; the feasible solution of the distance constraint is fused with the plurality of position estimation results through Bayesian estimation, that is, the position at the current time is predicted based on the historical data before the current time.

10. A computer-readable storage medium, characterized in that, The computer program is stored in the storage and is configured to be executed by the processor to implement the positioning and prediction method of the wireless signal equal-intensity boundary value based on the movement vector set.

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