Method for positioning and predicting constant-strength boundary value of wireless signal based on motion vector set

Abstract

The invention discloses a method for positioning and predicting a wireless signal equal-strength boundary value based on a motion vector set. The method comprises the following steps: constructing an offline map of an indoor scene in a server; performing grid division on the scene, acquiring a signal intensity value of the Bluetooth beacon at each grid center point, performing intensity value region division on the signal intensity values, and performing numerical fitting to obtain an intensity boundary line; when a user carries wireless equipment to move in a scene, cutting an intensity boundary line to obtain a dynamically updated movement vector set; at each moment, a position estimation area is constructed according to the position estimation results of the multiple motion vectors; and fusing a plurality of position estimation results through Bayesian estimation, namely predicting the position at the current moment in combination with historical data before the current moment. According to the invention, a strategy of first mapping and then positioning is adopted, a user downloads a scene map through a server, information fusion calculation is realized through a positioning algorithm, and accurate positioning and prediction of the user are realized.

Description

Technical Field

[0001] The invention relates to the field of positioning and navigation, and in particular to a positioning and prediction method for wireless signal equal strength boundary values ​​based on a moving vector set. Background Art

[0002] With the increasing demand for indoor positioning, such as shopping guides in shopping malls, 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] At present, there are many solutions related to indoor positioning, such as using WiFi positioning technology, UWB positioning technology, laser positioning technology, etc. However, due to limitations such as complex indoor environment, positioning accuracy and efficiency, equipment cost, and scalability, the above solutions cannot completely solve the positioning problem in indoor scenes.

[0004] Therefore, an urgent problem that technicians in this field need to solve is how to use the devices carried by users to propose a low-cost, high-efficiency, and high-precision indoor positioning method and system without wearing additional devices to meet the growing demand. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a positioning and prediction method for wireless signal equal strength boundary values ​​based on a mobile vector set in view of the defects of current indoor positioning methods or systems, such as requiring users to equip additional equipment and having low positioning accuracy and efficiency.

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

[0007] The present invention provides a method for positioning and predicting a wireless signal equal strength boundary value based on a moving vector set, comprising the following steps:

[0008] Offline stage:

[0009] Construct an offline map of the indoor scene in the server, including: a bottom-level vector two-dimensional map, a middle-level scene feature layer, and a high-level retrieval information layer;

[0010] Set up multiple Bluetooth beacons in the scene, divide the scene into grids according to the offline map, collect the signal strength values ​​of the Bluetooth beacons in the scene at the center point of each grid, divide the signal strength values ​​into strength value areas, and obtain the strength boundary lines after numerical fitting;

[0011] Online stage:

[0012] The user carries a wireless device and moves in the scene, cutting the intensity boundary line to obtain a dynamically updated movement vector set;

[0013] The distance constraint is set according to the position of the wireless device carried by the user and the human body particle in the scene, and the feasible solution of the distance constraint is obtained by combining the signal attenuation model and the minimum error optimization model for calculation;

[0014] Based on the motion vector set, a regionalization method is introduced. At each moment, a position estimation region is constructed according to the position estimation results of multiple motion vectors. The position estimation region represents the possible range of position estimation. Combined with the feasible solution of the spacing constraint, multiple position estimation results are fused through Bayesian estimation, that is, the position at the current moment is predicted by combining the historical data before the current moment.

[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, and is obtained by traversing and mapping the scene with a laser surveying instrument;

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

[0018] The high-level retrieval information layer is used to associate the bottom-level information with the middle-level information, and to call the bottom-level information and the middle-level information when performing positioning calculations.

[0019] Furthermore, the method for obtaining the intensity boundary line of the present invention comprises:

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

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

[0022] Take the minimum and maximum values ​​of the intensity value, divide it into k equal parts, and construct the equal intensity area of ​​each Bluetooth beacon;

[0023] Select the grid whose intensity value is equal to the boundary of the equal-intensity region, connect the center points of the grid, and obtain k+1 equal-intensity region boundary lines; use the 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 of the present invention is specifically:

[0025]

[0026] Among them, b0 to b N Represents the coefficient of the curve function; vp and v q is the intensity value corresponding to the equal intensity boundary, N is the order of the polynomial, after obtaining the polynomial expressions of k+1 boundary lines, the equal intensity line corresponding to any intensity is calculated by interpolation method, let x p ,y p The intensity value is v p Points on the isointensity line, x q ,y q The intensity value is v q Points on the isointensity line, x i ,y i The intensity value is v i The points on the isointensity line satisfy the following relationship:

[0027]

[0028] The polynomial expression of the intensity boundary line of equal intensity corresponding to any intensity can be calculated.

[0029] Furthermore, the method of obtaining a motion vector set of the present invention includes:

[0030] The user moves with the wireless device in the scene, cutting the intensity boundary line. When there are n Bluetooth beacons in the scene, there are n movement vectors; where v n,k-1 is the signal strength of the nth Bluetooth beacon received in the scene at time k-1, similar to v n,k is the signal strength of the nth Bluetooth beacon received at the kth moment and scene, is 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:

[0031]

[0032] The set of moving vectors generated by cutting boundary lines of different strengths is recorded as A, which is a dynamically updated collection:

[0033]

[0034] Furthermore, the method of obtaining a feasible solution to the spacing constraint of the present invention includes:

[0035] For the i-th Bluetooth beacon, the user’s mobile device receives a total of three signal strength values, which are the signal v received by the mobile device’s built-in Bluetooth. pi , the signal v received by the left and right Bluetooth headsets li and v ri ; Calculate v li and v ri The mean value v mi, as the intensity value received by the human body particle:

[0036] v mi =(v li +v ri ) / 2

[0037] The final signal strength value in the scene where the mobile device is located is:

[0038] v i =(v pi +v mi ) / 2

[0039] The signal strength difference between the mobile device and the human body is used as the positioning constraint for calculation. Suppose the coordinates of the mobile device are (x s ,y s ), the coordinates of the human body point are (x b ,y b ), which contains the following three constraints:

[0040] The mobile device has a built-in Bluetooth signal strength constraint for the Bluetooth beacon it receives. The mobile device is located in the scene where the strength value of the i-th Bluetooth beacon is v i On the isointensity lines:

[0041] y s -f(x s ,v i )=0

[0042] The Bluetooth beacon signal strength constraint received by the human body particle, then its position is on the isointensity line of the corresponding intensity:

[0043] y b -f(x b ,v mi )=0

[0044] The distance between the human body mass point and the mobile device is constrained to be one arm length, and the signal attenuation model is introduced for calculation:

[0045] |θ(v i -v mi )|=ξd

[0046] Using the minimum error optimization model, a feasible solution under three constraints is obtained:

[0047]

[0048] Among them, θ is the signal attenuation and distance corresponding coefficient, the unit is db / m, ξ is the empirical coefficient, d is the distance between the human body 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 point.

[0049] Furthermore, the regionalization method of the present invention specifically comprises:

[0050] At each moment, a position estimation region R is constructed based on the estimation results of multiple motion vectors, which represents the possible range of position estimation:

[0051] R={(x1,y1), (x2,y2), (x3,y3)…(x m ,y m )}

[0052] x m ,y m Represents the coordinates of the mth point in the R region

[0053] Find the target point x i ,y i Minimum distance to the curve:

[0054]

[0055] For x choose a starting point x init Calculate D((x i ,y i ), f(x,v i )), at the current point x init The gradient finds the direction of the fastest rise or fall at this point and sets a learning rate β to update the value of x:

[0056]

[0057] Repeat the gradient calculation and update steps to finally get x new D((x i ,y i ), f(x,v i )) reaches the point where it reaches its approximate minimum value, D((x i ,y i ), f(x new ,v i )) is (x i ,y i ) to the shortest distance of the curve;

[0058] The coordinates of the RP point in the coordinate point set of the traversal region R are calculated as the coordinates of the grid closest to the nearest intensity boundary line:

[0059]

[0060] Furthermore, the method of predicting the position at the current moment by combining historical data before the current moment of the present invention includes:

[0061] The current coordinate estimate is P k =(x k ,y k ), assuming that the sampling time Δt is the time interval, and the continuous sampling time interval Δt is a linear motion with constant acceleration, then:

[0062]

[0063] The observation equation is:

[0064]

[0065] Among them, P obsk (x,y) is a feasible solution p with spacing constraints obs The value of (x,y) at time k;

[0066] Modeling equations:

[0067]

[0068] Wherein, w and u are both noises, respectively obeying P(w)~N(0,Q) and P(u)~N(0,G); Q represents the covariance matrix of noise w; G represents the covariance matrix of noise u;

[0069]

[0070] A priori estimates:

[0071]

[0072] A priori covariance matrix of the errors:

[0073]

[0074] There are n Bluetooth beacons in the scene. The Kalman filter calculates n estimated values, and the n estimates are fused through Bayesian estimation to get the final estimated value, that is, the historical data at moments k-1 and k-2 are used to predict the position at moment k.

[0075] The present invention provides a positioning and prediction system for wireless signal equal strength boundary values ​​based on a moving vector set, which is used to implement a positioning and prediction method for wireless signal equal strength boundary values ​​based on a moving vector set, comprising:

[0076] The offline map construction unit is used to construct an offline map of the indoor scene in the server, including: a bottom-level vector two-dimensional map, a middle-level scene feature layer, and a high-level retrieval information layer; the scene is gridded, the signal strength value of the Bluetooth beacon in the scene is collected at the center point of each grid, and the signal strength value is divided into strength value areas, and the strength boundary line is obtained after numerical fitting;

[0077] The online prediction unit is used to cut the intensity boundary line when the user's wireless device moves in the scene to obtain a dynamically updated movement vector set; the spacing constraint is imposed according to the position of the user's wireless device and the human body particle in the scene, and the signal attenuation model and the minimum error optimization model are combined for calculation to obtain a feasible solution to the spacing constraint; based on the movement vector set, a regionalization method is introduced to construct a position estimation area at each moment according to the position estimation results of multiple movement vectors, and the position estimation area represents the possible range of position estimation; the feasible solution to the spacing constraint is to fuse multiple position estimation results through Bayesian estimation, that is, to predict the position at the current moment in combination with the historical data before the current moment.

[0078] The present invention provides a computer-readable storage medium storing a computer program for implementing the above-mentioned method for positioning and predicting the boundary value of wireless signal isostrength based on a mobile vector set when executed by a processor.

[0079] The beneficial effects produced by the present invention are:

[0080] 1. In the method of the present invention, a strategy of first building a map and then positioning is adopted. First, a scene map is built offline and stored in a server. Then the user downloads the scene map through the server and runs the positioning software. The positioning software integrates the positioning algorithm of the present invention, collects the current Bluetooth signal, and integrates it with the information stored in the scene map to obtain the user's location information, and can output the location information for the user to use.

[0081] 2. In the method of the present invention, the grid center point is used to replace the entire grid, and the intensity value information of the grid edge cannot be obtained. Therefore, when constructing an equal-intensity area, it is also necessary to optimize the grid corners so that the edge lines of the equal-intensity area are smooth arcs. By selecting a grid whose intensity value is equal to the boundary of the equal-intensity area and connecting the grid center points, k+1 equal-intensity area boundary lines can be obtained. Using a numerical fitting tool, a polynomial fitting is performed on the boundary line to obtain a boundary line curve function. In this way, an accurate and smooth boundary line curve can be obtained.

[0082] 3. In the method of the present invention, the user terminal receives a total of three signal strength values, namely the signal received by the built-in Bluetooth of the mobile device, the signal received by the left ear Bluetooth headset and the signal received by the right ear Bluetooth headset. When calculating the position, three constraints are considered: 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 particle, and the distance constraint between the human body particle and the smart phone. In this way, the positions of the mobile device and the human body particle in the scene are constrained together, and the minimum error optimization model is used to obtain a feasible solution under the three constraints.

[0083] 4. In the method of the present invention, based on the obtained motion vector set A, the prediction method based on historical data optimizes the estimation of the position at the next moment. This method makes full use of multiple AP-based motion information. Due to the existence of motion vectors, there is a certain uncertainty in the position estimation. In order to represent this uncertainty, the present invention introduces a regionalization method. At each moment, based on the estimation results of multiple motion vectors, a position estimation region R is constructed, which represents the possible range of position estimation. This method achieves accurate prediction of the position. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0085] Figure 1 is a schematic diagram of positioning according to an embodiment of the present invention;

[0086] Figure 2 is a signal equal strength boundary map constructed in an embodiment of the present invention;

[0087] Figure 3 4 is a flow chart of a positioning and prediction system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0088] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0089] Example 1

[0090] like Figure 1-Figure 3 As shown, the system and method for positioning and predicting the boundary value of equal strength of wireless signals based on a mobile vector set according to an embodiment of the present invention includes the following two stages:

[0091] 1. Offline stage:

[0092] 1. Build an offline map of the indoor scene in the server, including: the bottom layer of vector 2D map, the middle layer of scene feature layer and the high layer of retrieval information layer;

[0093] In a preferred embodiment of the present invention, the offline map specifically includes:

[0094] Offline mapping is used to construct a scene map, which consists of a bottom-level scene vector two-dimensional map, a middle-level scene feature layer, and a high-level retrieval information layer.

[0095] The underlying scene vector two-dimensional map is used to store the basic structure and scale information of the scene, and is obtained by traversing and mapping the scene with a laser surveying instrument.

[0096] The middle scene feature layer includes the fingerprint features of wireless signals (Bluetooth, WiFi, etc.) and geomagnetic signal fingerprint features of the sampling points in the scene.

[0097] The high-level retrieval information layer is used to associate the bottom-level information with the middle-level information, making it convenient to call the bottom-level and middle-level information when performing positioning calculations.

[0098] The construction of the middle scene feature layer is described in detail below.

[0099] 2. Set up multiple Bluetooth beacons in the scene, divide the scene into grids according to the offline map, collect the signal strength values ​​of the Bluetooth beacons in the scene at the center point of each grid, divide the signal strength values ​​into intensity value areas, and obtain the intensity boundary lines after numerical fitting;

[0100] In a preferred embodiment of the present invention, the method for obtaining the intensity boundary line after numerical fitting specifically includes:

[0101] Step 1: Divide the scene into grids and determine the grid size and grid center point coordinates.

[0102] Step 2: Collect the signal value of the Bluetooth beacon in the scene at each grid center point, and collect multiple times. After removing the value with intensity less than -85db, use the median filter to perform preliminary filtering on the data and store the data. The storage mode is {[x i ,y i ]; [RSSI1, RSSI2, …, RSSIn]}, where [x i ,y i ] is the two-dimensional coordinate of the center point of the i-th grid in the entire scene, [RSSI1, RSSI2, …, RSSIn] is the RSSI value received from the 1st to the nth AP;

[0103] Step 3: After preliminary fingerprint data collection, we can get a preliminary understanding of the RSSI distribution of each AP point received by each grid point, take the minimum and maximum values, divide them into k equal parts, and construct the equal intensity area of ​​each AP. Let the maximum value v max , minimum value v min , in order to reasonably construct equal intensity areas, the following strategies are set:

[0104]

[0105] In a preferred embodiment of the present invention, after each AP in the scene is divided using the above strategy, each AP has k equal strength areas in the scene. The Bluetooth signal strength value collected from each grid center point is divided accordingly according to the strength value area of ​​the equal strength area, and k Bluetooth beacon equal strength areas composed of grids can be obtained.

[0106] Since the grid data collection in steps 1 and 2 is a discrete collection method, on the one hand, the density of the collection points is limited by the size of the grid; on the other hand, the intensity value information of the grid edge cannot be obtained by replacing the grid as a whole with the center point of the grid. Therefore, when constructing the equal-intensity area, the grid corners need to be optimized so that the edge of the equal-intensity area is a smooth arc.

[0107] Therefore, by selecting the grid whose intensity value is equal to the boundary of the equal intensity region and connecting the center points of the grid, k+1 equal intensity region boundary lines can be obtained. By using the numerical fitting tool and performing polynomial fitting on the boundary line, the boundary line curve function can be obtained:

[0108]

[0109] Among them, v p and v q is the intensity value corresponding to the equal intensity boundary, N is the order of the polynomial, and in general, N=3, i.e., cubic polynomial fitting. After obtaining the polynomial expressions of k+1 boundary lines, the equal intensity line corresponding to any intensity can be calculated by interpolation. Let x p ,y p The intensity value is v p Points on the isointensity line, x q ,y q The intensity value is v q Points on the isointensity line, x i ,y i The intensity value is v i For points on the isointensity lines, the following relationship is satisfied.

[0110]

[0111] The above formula can be used to calculate the polynomial expression of isointensity lines corresponding to any intensity.

[0112] 2. Online stage:

[0113] 1. The user moves with the wireless device in the scene, cuts the intensity boundary line, and obtains a dynamically updated motion vector set;

[0114] When the user moves in the scene, the fitted equal-intensity boundary lines in the scene will be cut. If there are n APs in the scene, there are n movement vectors; where v n,k-1 is the signal strength received from the nth AP in the scene at time k-1, similar to v n,k is the signal strength received by the nth AP at the kth time and scene, 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;

[0115]

[0116] The set of moving vectors generated by cutting different equal intensity lines is recorded as A, which is a dynamically updated collection:

[0117]

[0118] 2. According to the position of the wireless device carried by the user and the human body point in the scene, the distance constraint is set, and the signal attenuation model and the minimum error optimization model are combined for calculation to obtain a feasible solution for the distance constraint;

[0119] 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 calculated as the positioning constraint. Suppose the coordinates of the smartphone are (x s ,y s ), the coordinates of the human body point are (x b ,y b ) contains the following constraints:

[0120] Constraint 1: The AP signal strength constraint received by the smartphone’s built-in Bluetooth. The smartphone’s position in the scene is at the strength value of the i-th AP v i On the isointensity lines:

[0121] y s -f(x s ,v i )=0

[0122] Constraint 2: The AP signal strength constraint received by the human body particle, then its position is on the isointensity line of the corresponding intensity:

[0123] y b -f(x b ,v mi )=0

[0124] Constraints, human body mass point and smartphone distance constraints, the distance is about an arm's length, the signal attenuation model is introduced for calculation:

[0125] |θ(v i -v mi )|=ξd

[0126] Among them, θ is the signal attenuation and distance corresponding coefficient, its unit is db / m, ξ is the empirical coefficient, and d is the distance between the human body point and the smartphone.

[0127] The above three constraints jointly constrain the positions of smartphones and human body particles in the scene. In reality, it is difficult to obtain a single solution that fully satisfies the above three constraints. The minimum error optimization model is used to obtain a feasible solution under the three constraints.

[0128]

[0129] 3. Based on the moving vector set, a regionalization method is introduced. At each moment, a position estimation region is constructed according to the position estimation results of multiple moving vectors. The position estimation region represents the possible range of position estimation. Combined with the feasible solution of the spacing constraint, multiple position estimation results are fused through Bayesian estimation, that is, the position at the current moment is predicted by combining the historical data before the current moment.

[0130] Based on the obtained motion vector set A, the prediction method based on historical data optimizes the position of the next moment. This method makes full use of the motion information based on multiple AP points. Due to the existence of motion vectors, there is a certain uncertainty in the position estimation. In order to represent this uncertainty, we introduce a regionalization method. At each moment, based on the estimation results of multiple motion vectors, a position estimation region R is constructed, which represents the possible range of position estimation.

[0131] R={(x1,y1), (x2,y2), (x3,y3)…(x m ,y m )}

[0132] Find the target point x i ,y i Minimum distance to the curve:

[0133]

[0134] For x choose a starting point x init Calculate D((x i ,y i ), f(x,v i ))At the current point x init The gradient finds the direction of the fastest rise or fall at this point and sets a learning rate β to update the value of x:

[0135]

[0136] Repeat the gradient calculation and update steps to finally get x new D((x i ,y i ), f(x,v i )) reaches the point where it reaches its approximate minimum value, D((x i ,y i), f(x new ,v i )) is (x i ,y i ) to the shortest distance from the curve.

[0137] Traverse the RP points in R and calculate the coordinates of the grid closest to the nearest contour line as the target point coordinates:

[0138]

[0139] Each moving vector cuts through the isointensity lines of different APs in the scene. This property can be used to predict the user's next position through a Kalman filter.

[0140] The current coordinate estimate is P k =(x k ,y k ), assuming that the sampling time Δt is the time interval, and assuming that the model is a linear motion with constant acceleration within the continuous sampling time interval Δt:

[0141]

[0142] Observation equation:

[0143]

[0144] Among them, P obsk (x,y) is p obs The value of (x,y) at time k;

[0145] Modeling equation:

[0146]

[0147] Where w and u are both noises, respectively obeying P(w)~N(0,Q) and P(u)~N(0,G)

[0148]

[0149] A priori estimates:

[0150]

[0151] A priori covariance matrix of the errors:

[0152]

[0153] There are n APs in the scene. According to the Kalman filter, n estimated values ​​can be calculated. The final estimated value is obtained by fusing the n estimates through Bayesian estimation, that is, the historical data at moments k-1 and k-2 are used to predict the position at moment k.

[0154] Example 2

[0155] The embodiment of the present invention provides a system for positioning and predicting a wireless signal equal strength boundary value based on a moving vector set, which is used to implement a method for positioning and predicting a wireless signal equal strength boundary value based on a moving vector set, including:

[0156] The offline map construction unit is used to construct an offline map of the indoor scene in the server, including: a bottom-level vector two-dimensional map, a middle-level scene feature layer, and a high-level retrieval information layer; the scene is gridded, the signal strength value of the Bluetooth beacon in the scene is collected at the center point of each grid, and the signal strength value is divided into strength value areas, and the strength boundary line is obtained after numerical fitting;

[0157] The online prediction unit is used to cut the intensity boundary line when the user's wireless device moves in the scene to obtain a dynamically updated movement vector set; the spacing constraint is imposed according to the position of the user's wireless device and the human body particle in the scene, and the signal attenuation model and the minimum error optimization model are combined for calculation to obtain a feasible solution to the spacing constraint; based on the movement vector set, a regionalization method is introduced to construct a position estimation area at each moment according to the position estimation results of multiple movement vectors, and the position estimation area represents the possible range of position estimation; the feasible solution to the spacing constraint is to fuse multiple position estimation results through Bayesian estimation, that is, to predict the position at the current moment in combination with the historical data before the current moment.

[0158] Example 3

[0159] An embodiment of the present invention provides a positioning and prediction system for wireless signal equal strength boundary values ​​based on a mobile vector set. The system is composed of a user device, a Bluetooth signal base station and a server. After the user downloads the scene map stored in the server, the user's current location information can be calculated by sensing the surrounding Bluetooth signals.

[0160] The user device can receive Bluetooth signals in the scene, and the server stores scene map information, which includes vector map information and Bluetooth boundary information of the scene. In the embodiment of the method, a strategy of first building a map and then positioning is adopted. First, the scene map is constructed offline and stored in the server. Then the user downloads the scene map through the server and runs the positioning software. The positioning software integrates the positioning algorithm of the present invention, collects the current Bluetooth signal, and fuses it with the information stored in the scene map to obtain the user's location information, and can output the location information for the user to use.

[0161] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean 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 the present application.

[0162] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all these improvements and changes should fall within the scope of protection of the appended claims of the present invention.

Claims

1. A method for locating and predicting wireless signal equal strength boundary values ​​based on a moving vector set, characterized in that: The following steps are involved: Offline stage: Construct an offline map of the indoor scene in the server, including: a bottom-level vector two-dimensional map, a middle-level scene feature layer, and a high-level retrieval information layer; Set up multiple Bluetooth beacons in the scene, divide the scene into grids according to the offline map, collect the signal strength values ​​of the Bluetooth beacons in the scene at the center point of each grid, divide the signal strength values ​​into strength value areas, and obtain the strength boundary lines after numerical fitting; Online stage: The user carries a wireless device and moves in the scene, cutting the intensity boundary line to obtain a dynamically updated movement vector set; The distance constraint is set according to the position of the wireless device carried by the user and the human body particle in the scene, and the feasible solution of the distance constraint is obtained by combining the signal attenuation model and the minimum error optimization model for calculation; Based on the motion vector set, a regionalization method is introduced. At each moment, a position estimation region is constructed according to the position estimation results of multiple motion vectors. The position estimation region represents the possible range of position estimation. Combined with the feasible solution of the spacing constraint, multiple position estimation results are fused through Bayesian estimation, that is, the position at the current moment is predicted by combining the historical data before the current moment.

2. The method for locating and predicting wireless signal equal strength boundary values ​​based on a mobile vector set according to claim 1, characterized in that: Offline maps include: The underlying scene vector 2D map is used to store the basic structure and scale information of the scene, and is obtained by traversing and mapping the scene with a laser surveying instrument; The middle scene feature layer includes the wireless signal fingerprint features and geomagnetic signal fingerprint features of the sampling points in the scene; The high-level retrieval information layer is used to associate the bottom-level information with the middle-level information, and to call the bottom-level information and the middle-level information when performing positioning calculations.

3. The method for locating and predicting wireless signal equal strength boundary values ​​based on a mobile vector set according to claim 1, characterized in that: Methods for obtaining intensity boundary lines include: Divide the scene into grids, determine the grid size and grid center point coordinates; The intensity value of the Bluetooth beacon signal in the scene is collected at each grid center point, and the collection is performed multiple times. After removing the values ​​whose intensity value is less than a certain threshold, the intensity value data is preliminarily filtered by a median filter and then stored; Take the minimum and maximum values ​​of the intensity value, divide it into k equal parts, and construct the equal intensity area of ​​each Bluetooth beacon; Select the grid whose intensity value is equal to the boundary of the equal-intensity region, and connect the center points of the grid to obtain k+1 equal-intensity region boundary lines; use the numerical fitting tool to perform polynomial fitting on the boundary lines to obtain the curve function of the intensity boundary lines.

4. The method for locating and predicting wireless signal equal strength boundary values ​​based on a mobile vector set according to claim 3, characterized in that: The curve function for obtaining the intensity boundary line is specifically: Among them, b0 to b N Represents the coefficient of the curve function; v p and v q is the intensity value corresponding to the equal intensity boundary, N is the order of the polynomial, after obtaining the polynomial expressions of k+1 boundary lines, the equal intensity line corresponding to any intensity is calculated by interpolation method, let x p ,y p The intensity value is v p Points on the isointensity line, x q ,y q The intensity value is v q Points on the isointensity line, x i ,y i The intensity value is v i The points on the isointensity line satisfy the following relationship: The polynomial expression of the intensity boundary line of equal intensity corresponding to any intensity can be calculated.

5. The method for locating and predicting wireless signal equal strength boundary values ​​based on a mobile vector set according to claim 1, characterized in that: Methods for obtaining a moving vector set include: The user moves with the wireless device in the scene, cutting the intensity boundary line. When there are n Bluetooth beacons in the scene, there are n movement vectors; where v n,k-1 is the signal strength of the nth Bluetooth beacon received in the scene at time k-1, similar to v n,k is the signal strength of the nth Bluetooth beacon received at the kth moment and scene, is 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: The set of moving vectors generated by cutting boundary lines of different strengths is recorded as A, which is a dynamically updated collection:

6. The method for locating and predicting wireless signal equal strength boundary values ​​based on a mobile vector set according to claim 4, characterized in that: Methods for obtaining feasible solutions to spacing constraints include: For the i-th Bluetooth beacon, the user’s mobile device receives a total of three signal strength values, which are the signal v received by the mobile device’s built-in Bluetooth. pi , the signal v received by the left and right Bluetooth headsets li and v ri ; Calculate v li and v ri The mean value v mi , as the intensity value received by the human body particle: v mi =(v li +v ri ) / 2 The final signal strength value in the scene where the mobile device is located is: v i =(v pi +v mi ) / 2 The signal strength difference between the mobile device and the human body is used as the positioning constraint for calculation. Let the coordinates of the mobile device be (x s ,y s ), the coordinates of the human body point are (x b ,y b ), which contains the following three constraints: The mobile device has a built-in Bluetooth signal strength constraint for the Bluetooth beacon it receives. The mobile device is located in the scene where the strength value of the i-th Bluetooth beacon is v i On the isointensity lines: y s -f(x s ,v i )=0 The Bluetooth beacon signal strength constraint received by the human body particle, then its position is on the isointensity line of the corresponding intensity: y b -f(x b ,v mi )=0 The distance between the human body mass point and the mobile device is constrained to be one arm length, and the signal attenuation model is introduced for calculation: |θ(v i -v mi )|=ξd Using the minimum error optimization model, a feasible solution under three constraints is obtained: Among them, θ is the signal attenuation and distance corresponding coefficient, the unit is db / m, ξ is the empirical coefficient, d is the distance between the human body 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 point.

7. The method for locating and predicting wireless signal equal strength boundary values ​​based on a mobile vector set according to claim 1, characterized in that: The regionalization approach specifically includes: At each moment, a position estimation region R is constructed based on the estimation results of multiple motion vectors, which represents the possible range of position estimation: R={(x1,y1),(x2,y2),(x3,y3)…(x m ,y m )} x m ,y m Represents the coordinates of the mth point in the R region; Find the target point x i ,y i Minimum distance to the curve: For x choose a starting point x init Calculate D((x i ,y i ), f(x,v i )), at the current point x init The gradient finds the direction of the fastest rise or fall at this point and sets a learning rate β to update the value of x: Repeat the gradient calculation and update steps to finally get x new D((x i ,y i ), f(x,v i )) reaches the point where it reaches its approximate minimum value, D((x i ,y i ), f(x new ,v i )) is (x i ,y i ) to the shortest distance of the curve; The coordinates of the RP point in the coordinate point set of the traversal region R are calculated as the coordinates of the grid closest to the nearest intensity boundary line:

8. The method for locating and predicting wireless signal equal strength boundary values ​​based on a mobile vector set according to claim 7, characterized in that: Methods for predicting the current position by combining historical data before the current time include: The current coordinate estimate is P k =(x k ,y k ), assuming that the sampling time Δt is the time interval, and the continuous sampling time interval Δt is a linear motion with constant acceleration, then: The observation equation is: Among them, P obsk (x,y) is a feasible solution p with spacing constraints obs The value of (x,y) at time k; Modeling equations: Wherein, w and u are both noises, respectively obeying P(w)~N(0,Q) and P(u)~N(0,G); Q represents the covariance matrix of noise w; G represents the covariance matrix of noise u; A priori estimates: A priori covariance matrix of the errors: There are n Bluetooth beacons in the scene. The Kalman filter calculates n estimated values, and the n estimates are fused through Bayesian estimation to get the final estimated value, that is, the historical data at moments k-1 and k-2 are used to predict the position at moment k.

9. A positioning and prediction system for wireless signal equal strength boundary values ​​based on a moving vector set, used to implement the positioning and prediction method for wireless signal equal strength boundary values ​​based on a moving vector set as claimed in any one of claims 1 to 8, characterized in that: include: The offline map construction unit is used to construct an offline map of the indoor scene in the server, including: a bottom-level vector two-dimensional map, a middle-level scene feature layer, and a high-level retrieval information layer; the scene is gridded, the signal strength value of the Bluetooth beacon in the scene is collected at the center point of each grid, and the signal strength value is divided into strength value areas, and the strength boundary line is obtained after numerical fitting; The online prediction unit is used to cut the intensity boundary line when the user's wireless device moves in the scene to obtain a dynamically updated movement vector set; the spacing constraint is imposed according to the position of the user's wireless device and the human body particle in the scene, and the signal attenuation model and the minimum error optimization model are combined for calculation to obtain a feasible solution to the spacing constraint; based on the movement vector set, a regionalization method is introduced to construct a position estimation area at each moment according to the position estimation results of multiple movement vectors, and the position estimation area represents the possible range of position estimation; the feasible solution to the spacing constraint is to fuse multiple position estimation results through Bayesian estimation, that is, to predict the position at the current moment in combination with the historical data before the current moment.

10. A computer-readable storage medium, characterized in that: A computer program is stored, which is used to implement the method for positioning and predicting the boundary value of wireless signal isostrength based on a mobile vector set as described in any one of claims 1 to 8 when executed by a processor.

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