CN112881979B - Initial state self-adaptive fusion positioning method based on EKF filtering - Google Patents
Initial state self-adaptive fusion positioning method based on EKF filtering Download PDFInfo
- Publication number
- CN112881979B CN112881979B CN202110069453.XA CN202110069453A CN112881979B CN 112881979 B CN112881979 B CN 112881979B CN 202110069453 A CN202110069453 A CN 202110069453A CN 112881979 B CN112881979 B CN 112881979B
- Authority
- CN
- China
- Prior art keywords
- state
- ekf
- user
- positioning
- initial
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0257—Hybrid positioning
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0294—Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention relates to the technical field of fixed point tracking, and discloses an initial state self-adaptive fusion positioning method based on EKF filtering. The method is simple and reliable, convenient to operate, easy to realize and convenient to popularize and apply.
Description
Technical Field
The invention relates to the technical field of positioning tracking, in particular to an initial state self-adaptive fusion positioning method based on EKF filtering.
Background
With the commercial fall-off of the new generation cellular network technology, indoor positioning technology has attracted widespread attention again, and the growing life and commercial demand for location services has stimulated rapid development of indoor positioning systems and related technologies. Typical indoor positioning technologies comprise Wi-Fi, bluetooth, RFID, ultra wide band and the like, wherein the Wi-Fi position fingerprint positioning has wide network coverage and can meet most indoor positioning requirements, but signals of the Wi-Fi position fingerprint positioning are easily interfered by the environment, so that a positioning result generates large fluctuation; a Pedestrian Dead Reckoning (PDR) positioning method utilizes an Inertial sensing unit (IMU) to realize Dead Reckoning positioning, and at present, mobile terminal equipment is well supported, but larger Inertial accumulated errors exist.
In order to improve the performance of the single positioning system, research and analysis are currently conducted on a Wi-Fi and PDR integrated positioning system and technology. In most cases, the positioning performance of a single system can be improved by a fusion positioning method, so that on one hand, the influence caused by the fluctuation of Wi-Fi positioning can be reduced, and on the other hand, the accumulated error caused by PDR in the positioning process can be reduced, but the existing fusion positioning method based on Wi-Fi/PDR still has the following problems:
1) Ignoring the initial state determination process, including the originating location and heading angle, which may result in the positioning system failing to converge quickly for a limited time;
2) When the extended Kalman filtering EKF algorithm is used for fusion, the error measurement of the Wi-Fi signal intensity data usually uses a self-defined fixed value, and the influence caused by the fluctuation of the Wi-Fi signal intensity data in a real environment is not well fed back to an EKF system.
Disclosure of Invention
The invention provides an initial state self-adaptive fusion positioning method based on EKF filtering, which solves the problems that the influence of an initial state on the rapid convergence of a system is not considered in the conventional fusion algorithm, a user-defined fixed value is used for measuring the error of Wi-Fi signal strength data to influence the final positioning error of the system, and the like.
The invention can be realized by the following technical scheme:
an initial state self-adaptive fusion positioning method based on EKF filtering is characterized in that a region to be detected is subjected to gridding processing, a Wi-Fi position fingerprint database in an off-line state is established, then a Wi-Fi position fingerprint positioning method and a pedestrian dead reckoning method PDR are fused by utilizing an EKF (extended Kalman filtering) method, the initial state of a user in motion is obtained by combining a multi-point Kalman filtering method on the basis of the Wi-Fi position fingerprint positioning method, and meanwhile, in the iterative process of the EKF method, a dynamic observation noise covariance matrix is formed by the Euclidean distance of signal intensity data RSSI (received signal strength indicator) of two adjacent states in the motion process of the user, so that the position prediction of the next state in motion of the user is completed.
Further, the initial state includes an initial position L 0 And an initial heading angle θ 0 On the basis of the Wi-Fi position fingerprint database, calculating by using a Wi-Fi position fingerprint positioning method to obtain coordinates of each position point predicted by a user after moving for m steps from rest
Is marked as
Acquiring M groups of Wi-Fi signal intensity data at each position point i again, resolving by using a Wi-Fi position fingerprint positioning method to obtain M positioning results corresponding to each position point i, and recording as L' i(j) (j =1,2, \8230;, M), and then, carrying out iterative solution on all positioning results by using a multi-point kalman filtering method to obtain the i coordinate (x) of each position point after filtering i ,y i ) (i =0,1,2, \8230;, m), noted
Then the initial position
Initial course angle
Further, in the iterative process of the extended Kalman filtering method EKF, an observation noise covariance matrix R is calculated by using the following equation k
Wherein, the first and the second end of the pipe are connected with each other,
when k =0, the initial value δ 0 Setting the average error as a constant value of the Wi-Fi fingerprint positioning method;
when k is more than or equal to 1,
represents the Euclidean distance of RSSI signal strength data corresponding to the k state and the k-1 state when the user moves,
a group of T Wi-Fi signal strength data [ D ] collected from a position point corresponding to the kth state when a user moves min ,D max ]In a stable environment D k Value range of [ S ] min ,S max * Represents the positioning precision range of the initial state self-adaptive fusion positioning method based on EKF filtering, D min 、D max 、S min 、S max Are all set to empirical values, δ d Representing step error, delta, in the motion of the user θ The heading angle errors, which represent the movement of the user, are all set to be constant.
Further, the method for fusing the Wi-Fi position fingerprint positioning method and the pedestrian dead reckoning method PDR by using the EKF (extended Kalman Filter) method to complete the position prediction of the next state when the user moves comprises the following steps:
step one, in a prediction stage of an extended Kalman filtering method EKF, a system state matrix X corresponding to a kth state of a user during movement is calculated by using the following formula k ' sum noise covariance matrix P k ′，
Wherein the content of the first and second substances,
represents the state transition matrix, x k-1 、y k-1 、θ k-1 Respectively representing the position coordinate and the course angle x 'corresponding to the k-1 state of the user in motion obtained by adopting an extended Kalman filtering method EKF' k 、y′ k 、θ′ k Respectively representing the position coordinate and the course angle corresponding to the kth state of the user during movement after the prediction by a pedestrian dead reckoning method (PDR), d k Representing the step length corresponding to the kth state when the user moves, W representing the Gaussian white noise vector of the system state, and Delta theta representing the estimated increment of the course angle, wherein the Delta theta is set as a constant;
P′ k ＝AP k-1 A T +Q；
wherein, P k-1 The noise covariance matrix of the k-1 th state of the user during movement is obtained by adopting an extended Kalman filtering method EKF, and the initial value is
Represents the noise brought by the prediction model itself, and is composed of the average error of each element of the PDR method, delta x 、δ y Indicating the error of position, δ θ An average error representing a heading angle;
step two, in the updating stage of the extended Kalman filtering method EKF, the method is utilized to calculate and obtain the observation noise covariance matrix R corresponding to the kth state when the user moves k And then using the following equation to calculate and obtain the Kalman gain K k Thereby obtaining an estimated state matrix of the kth state when the user moves
The positioning result of the kth state when the user moves is (x) k ,y k )，
K k ＝P′ k H T (HP′ k H T +R k ) -1 Wherein the observation matrix
X k ＝X′ k +K k (Z k -HX′ k ) Wherein, in the step (A),
expressing an observation equation, V expressing a Gaussian white noise vector of the observation equation, W and V being independent of each other,
respectively representing the position coordinates of the kth state of the user in motion obtained by adopting a Wi-Fi fingerprint positioning method,
then, using equation P k ＝(1-K k H)P′ k Calculating an updated noise covariance matrix of the kth state of the user during movement;
and step three, executing the steps one to two until all the position points in the user movement process are detected.
Further, step length d is determined through a Weinberg step length estimation model k ，
Wherein c represents a step-size scaling factor, which is a fixed constant, a max And a min Respectively representing the maximum acceleration and the minimum acceleration detected during a state of movement of the user.
Further, the method for establishing the Wi-Fi position fingerprint database in the offline state comprises the following steps:
1) Dividing a positioning area to be measured into a plurality of grids in a uniform equal division mode, and using the vertex of each grid as a reference point RP;
2) Acquiring a plurality of access points of each reference point RP in a single channel through signal acquisition equipmentThe N Wi-Fi signal strength data are filtered, so that a group of signal fingerprint data is formed
Where L denotes the L-th reference point RP position, L =1,2, \8230;, L;
3) Simultaneously acquiring coordinate information and MAC address of the position by each reference point RP, using the coordinate information and MAC address as a group of position fingerprint data, and establishing a position fingerprint database RP in an off-line state x
The beneficial technical effects of the invention are as follows:
on the basis of a Wi-Fi/PDR algorithm, the EKF is adopted to fuse the positioning information of Wi-Fi and PDR, and a method for adaptively solving an initial state is provided to avoid the influence caused by the deviation of an initial value, so that the result can be quickly converged, and meanwhile, a method for dynamically adjusting an EKF system is used. In addition, the method is simple and reliable, convenient to operate, easy to realize and convenient to popularize and apply.
Drawings
FIG. 1 is a flow chart of a fusion positioning method of the present invention;
FIG. 2 is a diagram of an experimental test scenario of the present invention;
FIG. 3 is a graph illustrating the effect of different initial positions on the positioning result according to the present invention;
FIG. 4 is a graph illustrating the effect of different initial course angles on the positioning result according to the present invention;
FIG. 5 is a performance comparison graph corresponding to different values of m in the fusion positioning method of the present invention;
FIG. 6 is a comparison graph of complexity corresponding to different values of m in the fusion positioning method of the present invention;
FIG. 7 is a schematic diagram of an initial state solution of the fusion localization method of the present invention;
FIG. 8 is a comparison of the location trace of the proposed method of the present invention with Wi-Fi location, PDR location, and conventional EKF-based fusion algorithm.
Detailed Description
The following detailed description of the preferred embodiments will be made with reference to the accompanying drawings.
The Wi-Fi positioning has larger volatility, PDR positioning has accumulated errors, as shown in figure 1, the invention provides an initial state self-adaptive fusion positioning method based on EKF filtering, a region to be detected is subjected to gridding treatment, a Wi-Fi position fingerprint database in an off-line state is established, then an extended Kalman filtering method EKF is utilized to fuse a Wi-Fi position fingerprint positioning method and a pedestrian dead reckoning method PDR, the initial state of a user in motion is obtained by combining a multipoint Kalman filtering method on the basis of the Wi-Fi position fingerprint positioning method, meanwhile, in the iteration process of the extended Kalman filtering method EKF, a dynamic observation noise covariance matrix is formed by Euclidean distance of signal strength data RSSI of two adjacent states in the motion process of the user, so as to complete the position prediction of the next state in motion of the user, thereby reducing the influence that the system cannot be rapidly converged due to improper setting of RSSI in the adjacent states in the motion process of the user, and feeding back an effective measurement result of the Euclidean RSSI in the iteration state in the motion of the extended EKF, thereby reducing the influence of the system on the stable EKF positioning, and reducing the influence of the system constraint of the system. The method specifically comprises the following steps:
step one, establishing a Wi-Fi position fingerprint database in an offline state
Firstly, dividing a positioning area to be measured into a plurality of grids in a uniform equal division mode, and using the vertex of each grid as a reference point RP;
then, N Wi-Fi signal intensity data of a plurality of access points of each reference point RP on a single channel are obtained by using signal acquisition equipment, gaussian filtering processing is carried out on the data of different reference points RP, wi-Fi signal intensity data of a large probability interval is selected as an effective value of sampling data, and then a mean value method is used for carrying out smoothing processing, so that a group of signal fingerprint data is formed
Wherein L =1,2, \8230;, L, L denotes the L-th reference point RP position;
finally, for each reference point RP, coordinate information and MAC address of the position are acquired at the same time, and the coordinate information and MAC address are used as a group of position fingerprint data to establish a position fingerprint database RP in an off-line state x
Step two, because the initial deviation of the Kalman filter may cause the filtering results of the first steps of filtering calculation to generate larger deviation, even after the algorithm enters a convergence state, the initial value may cause the converged filtering result to be difficult to converge to the true value of the state because the initial value is improperly set, therefore, the position point solved by the Wi-Fi position fingerprint positioning method cannot be directly used as the initial position value of the extended Kalman EKF system, and the subsequent steps need to be fused with a pedestrian dead reckoning method PDR, and the initial course angle of the EKF system is difficult to obtain, so that a relatively accurate initial state is needed as the initial value of the system, the fusion system can be ensured to be quickly converged, and the final fusion positioning precision is improved 0 Angle theta with the initial heading 0 。
1) On the basis of the Wi-Fi position fingerprint database, resolving and obtaining coordinates of each position point predicted by the user after moving for m steps from rest by utilizing a Wi-Fi position fingerprint positioning method such as a WKNN-based online matching algorithm
Is marked as
2) Acquiring M groups of Wi-Fi signal intensity data again at each position point i, calculating by using a Wi-Fi position fingerprint positioning method to obtain M positioning results corresponding to each position point i, and recording as L' i(j) (j =1,2, \8230;, M), and then M times of iterative solution is carried out on all positioning results by utilizing a multi-point Kalman filtering method to obtain the i coordinates (x) of each position point after filtering i ,y i ) (i =0,1,2, \ 8230;, m), noted
Then the initial position
Initial course angle
Step three, fusing a Wi-Fi position fingerprint positioning method and a pedestrian dead reckoning method PDR by using an extended Kalman filtering method EKF to complete the position prediction of the next state of the user during movement, which comprises the following steps:
step I, in the prediction stage of the extended Kalman filtering method EKF, a system state matrix X corresponding to the kth state of the user during movement is calculated by using the following formula k ' sum noise covariance matrix P k ′，
Wherein, the first and the second end of the pipe are connected with each other,
representing the state transition matrix, x k-1 、y k-1 、θ k-1 Respectively representing the position coordinate and the course angle x 'corresponding to the k-1 state of the user in motion obtained by adopting an extended Kalman filtering method EKF' k 、y′ k 、θ′ k Respectively representing the position coordinate and the course angle corresponding to the kth state of the user during movement after being predicted by a pedestrian dead reckoning method PDR, W representing a Gaussian white noise vector of a system state, delta theta representing an estimated increment of the course angle, wherein the estimated increment is set as a constant, d k The step length corresponding to the kth state representing the user's movement can be determined by a Weinberg step length estimation model, and specifically, the step length corresponding to the Kth state can be determined by the following document Harvey Weinberg].Application Notes American Devices,USA:Analog Devices Inc,2002，
Where c represents a step-size scaling factor which is a fixed constant, a max And a min Respectively representing the maximum acceleration and the minimum acceleration detected in the process of moving a state by the user;
P′ k ＝AP k-1 A T +Q；
wherein, P k-1 The noise covariance matrix of the k-1 th state of the user during movement is obtained by adopting an extended Kalman filtering method EKF, and the initial value is
Represents the noise caused by the prediction model itself, and is composed of the average error of each element of the pedestrian dead reckoning method PDR, delta x 、δ y Indicating the error of position, δ θ Mean error representing heading angle;
step II, in the updating stage of the extended Kalman filtering method EKF, the observation noise covariance matrix R corresponding to the kth state of the user during movement is obtained by calculation according to the following equation k And Kalman gain K k Thereby obtaining an estimated state matrix of the k-th state when the user moves
The positioning result of the kth state when the user moves is (x) k ,y k )。
In the existing fusion system, the error measurement of Wi-Fi signal strength data usually uses a self-defined fixed value, but in a complex positioning environment, due to the influence of factors such as multipath effect, the noise of the Wi-Fi position fingerprint positioning system does not strictly meet Gaussian distribution, which can greatly influence the final positioning result. Therefore, the invention can dynamically adjust according to the electromagnetic environment and the feedback information thereof on the basis of the EKF-based indoor Wi-Fi/PDR fusion positioning algorithm, namely, a noise feedback mechanism based on the RSSI Euclidean distance in an adjacent state is adopted, thereby improving the final positioning accuracy.
In the moving and walking process of a user, the Euclidean distance is solved for RSSI signal strength data of two adjacent states, and in each system, the RSSI signal strength data can be kept in a stable value range and is marked as [ D ] min ,D max ]Then linearly scaled to a threshold range of positioning accuracy, i.e. [ S ] min ,S max ]Finally, the dynamic state of the EKF is taken as a parameter of an observation covariance matrix of the EKF, so that the noise of the Wi-Fi signal intensity data is dynamically measured, and the method specifically comprises the following steps:
in the iterative process of the extended Kalman filtering method EKF, an observation noise covariance matrix R is calculated by using the following equation k
Wherein, the first and the second end of the pipe are connected with each other,
when k =0, the initial value δ 0 Setting the average error of the Wi-Fi fingerprint positioning method as a constant;
when k is more than or equal to 1,
represents the Euclidean distance of RSSI signal strength data corresponding to the k state and the k-1 state when the user moves,
a group of T Wi-Fi signal strength data are collected from a position point corresponding to the kth state when the user moves,]D min ,D max ]in a stable environment D k Value range of [ S ] min ,S max ]Represents the positioning accuracy range of an EKF-filter-based initial state adaptive fusion positioning method as defined in claim 1, D min 、D max 、S min 、S max Are all set to empirical values, δ d Representing step error, delta, in the motion of the user θ The heading angle errors, which represent the movement of the user, are all set to be constant.
K k ＝P′ k H T (HP′ k H T +R k ) -1 Wherein the observation matrix
X k ＝X′ k +K k (Z k -HX′ k ) Wherein, in the step (A),
expressing an observation equation, V expressing a Gaussian white noise vector of the observation equation, W and V being independent of each other,
respectively represents the position coordinates of the kth state of the user during movement obtained by adopting a Wi-Fi fingerprint positioning method,
then, using equation P k ＝(1-K k H)P′ k Calculating an updated noise covariance matrix of the kth state of the user during movement;
and step III, executing the steps I to II until all the position points in the user movement process are detected.
In order to verify the feasibility of the method, a simulation experiment is carried out, as shown in fig. 2, a simulation environment is a 7-th building of an institute of electrical and electronics engineering of a certain school, 1 mx 1m squares are calibrated on the ground, the vertexes of the squares are taken as experimental sample collection points, namely reference points RP, 8 routers with the TP-LINK model number TL-WR886N are selected as positioning beacons in the experiment, hua-Cheng-Yang 8 is taken as a PDR and Wi-Fi data collection tool, and Wi-Fi positioning, PDR positioning, a traditional EKF-based fusion algorithm and an improved EKF-based Wi-Fi/PDR fusion positioning algorithm are respectively adopted to carry out the positioning experiment. The walking track of the experiment is an L-shaped route, the initial coordinates are (2, 1), the end coordinates are (14, 13), the total walking steps are 30 steps, namely, the total number of 31 position states, the experiment adopts a walking mode with fixed step length, and the distance of each step, namely, the step length is 0.8m.
As shown in fig. 3, for the initial position, in the case that the initial heading angle is known, i.e. set to 0 °, the initial position deviation value is set to 1-10 m at 1m intervals, and the deviation case for the 1m, 5m, 10m tracks therein is shown in fig. 4. It can be found from the outputted trace map that although there is a large deviation at the beginning, it can finally converge to the vicinity of the normal value. The larger the distance of the position deviation is, the larger the number of iterations of convergence is, but the more the deviation exceeds a certain limit, the more stable the number of convergence is.
As shown in fig. 4, for the initial heading angle, in the case where the initial position is known, that is, set to (2, 1), the initial angle deviation value is set to 10 to 60 ° at intervals of 5 °, for the case where the trajectories are shifted by 10 °, 30 °, 60 °. Through the output locus diagram, the deviation of the initial heading angle does not affect the convergence speed of the output result, but the result generates continuous deviation, and the deviation result approaches to the vicinity of a normal value at a very slow speed.
As shown in fig. 5 and fig. 6, the deviation condition of the initial state when m has different values in equation (12) is analyzed experimentally, and as m increases, the heading angle initial state is better, and although the complexity also increases, the time consumed by the algorithm when m =5 is within 20ms, and the final user experience is not affected.
As shown in fig. 7, by the initial state solution method provided by the present invention, the positioning result can be converged quickly, and the convergence state can be achieved within four steps.
As shown in fig. 8, the result shows that although the Wi-Fi position fingerprint positioning method can output an absolute positioning value, the data fluctuation is large, the PDR positioning can maintain good accuracy in a short time, but the accumulated error becomes larger and larger as the number of iterations increases. The improved EKF algorithm provided by the invention can well integrate the advantages of Wi-Fi positioning and PDR positioning methods, and compared with other EKF algorithms, the precision of the improved EKF algorithm is obviously improved, which shows that the method provided by the invention can effectively inhibit errors and improve the positioning precision.
Although specific embodiments of the present invention have been described above, it will be appreciated by those skilled in the art that these embodiments are merely illustrative and that many variations or modifications may be made thereto without departing from the principles and spirit of the invention, the scope of which is therefore defined by the appended claims.
Claims (4)
1. An initial state self-adaptive fusion positioning method based on EKF filtering is characterized in that: gridding an area to be measured, establishing a Wi-Fi position fingerprint database in an off-line state, fusing a Wi-Fi position fingerprint positioning method and a pedestrian dead reckoning method PDR by using an extended Kalman filtering method EKF (extended Kalman Filter), wherein the initial state of a user in motion is obtained by combining a multipoint Kalman filtering method on the basis of the Wi-Fi position fingerprint positioning method, and meanwhile, in the iterative process of the extended Kalman filtering method EKF, a dynamic observation noise covariance matrix is formed by the Euclidean distance of signal strength data RSSI (received signal strength indicator) of two adjacent states in the motion process of the user so as to finish the position prediction of the next state of the user in motion;
the initial state comprises an initial position L 0 And an initial heading angle θ 0 On the basis of the Wi-Fi position fingerprint database, the user is obtained by resolving through a Wi-Fi position fingerprint positioning methodCoordinates of each position point predicted after m steps of starting movement from rest
Is marked as
Acquiring M groups of Wi-Fi signal intensity data at each position point i again, resolving by using a Wi-Fi position fingerprint positioning method to obtain M positioning results corresponding to each position point i, and recording as L i ′ (j) (j =1,2, \8230;, M), and then, carrying out iterative solution on all positioning results by using a multi-point Kalman filtering method to obtain the coordinates (x) of each position point i after filtering i ,y i ) (i =0,1,2, \8230;, m), noted
Then the initial position
Initial course angle
In the iterative process of the extended Kalman filtering method EKF, an observation noise covariance matrix R is calculated by using the following equation k
Wherein the content of the first and second substances,
when k =0, the initial value δ 0 Setting the average error of the Wi-Fi fingerprint positioning method as a constant;
when k is more than or equal to 1,
represents the Euclidean distance of RSSI signal strength data corresponding to the k state and the k-1 state when the user moves,
a group of T Wi-Fi signal strength data [ D ] collected from a position point corresponding to the kth state when a user moves min ,D max ]In a stable environment D k Value range of [ S ] min ,S max ]Indicating the positioning accuracy range, D, using the EKF-filter-based initial state adaptive fusion positioning method min 、D max 、S min 、S max Are all set to empirical values, δ d Representing step error, delta, in user motion θ The average error, which represents the heading angle, is set to a constant.
2. The EKF filtering-based initial state adaptive fusion positioning method as claimed in claim 1, wherein an extended Kalman filtering method EKF is used to fuse a Wi-Fi position fingerprint positioning method and a pedestrian dead reckoning method PDR to complete the position prediction of the next state when the user moves, specifically comprising the following steps:
step one, in the prediction stage of the EKF (extended Kalman Filter) method, a system state matrix X corresponding to the kth state of a user during movement is calculated and obtained by using the following formula k ' sum noise covariance matrix P k ′，
Wherein, the first and the second end of the pipe are connected with each other,
representing the state transition matrix, x k-1 、y k-1 、θ k-1 Respectively representing the corresponding k-1 state of the user during movement obtained by adopting an extended Kalman filtering method EKFPosition coordinates and heading angle, x' k 、y′ k 、θ′ k Respectively representing the position coordinate and the course angle corresponding to the k state of the user during movement after being predicted by a pedestrian dead reckoning method PDR, d k Representing the step length corresponding to the kth state when the user moves, W representing the Gaussian white noise vector of the system state, and Delta theta representing the estimated increment of the course angle, wherein the Delta theta is set as a constant;
P k ′＝AP k-1 A T +Q；
wherein, P k-1 The noise covariance matrix of the k-1 state of the user during movement is obtained by using an Extended Kalman Filter (EKF) and has an initial value of
Represents the noise caused by the prediction model itself, and is composed of the average error of each element of the pedestrian dead reckoning method PDR, delta x 、δ y Indicating the error of position, δ θ An average error representing a heading angle;
step two, in the updating stage of the extended Kalman filtering method EKF, the method of claim 1 is utilized to calculate and obtain the observation noise covariance matrix R corresponding to the kth state when the user moves k And then using the following equation to calculate and obtain the Kalman gain K k Thereby obtaining an estimated state matrix of the k-th state when the user moves
The positioning result of the kth state when the user moves is (x) k ,y k )，
K k ＝P k ′H T (HP k ′H T +R k ) -1 Wherein the observation matrix
X k ＝X′ k +K k (Z k -HX′ k ) Wherein, in the step (A),
expressing an observation equation, V expressing a Gaussian white noise vector of the observation equation, W and V being independent of each other,
respectively representing the position coordinates of the kth state of the user in motion obtained by adopting a Wi-Fi fingerprint positioning method,
then, using equation P k ＝(1-K k H)P k ', calculating the updated noise covariance matrix of the kth state when the user moves;
and step three, executing the steps from one step to two until all the position point detection in the user movement process is completed.
3. The EKF filter-based initial state adaptive fusion positioning method as claimed in claim 2, wherein: step length d is determined through Weinberg step length estimation model k ，
Wherein c represents a step-size scaling factor, which is a fixed constant, a max And a min Respectively representing the maximum acceleration and the minimum acceleration detected during a state of movement of the user.
4. An initial state adaptive fusion positioning method based on EKF filtering as defined in claim 1, wherein the method for establishing Wi-Fi position fingerprint database in off-line state comprises the following steps:
1) Dividing the positioning area to be measured into a plurality of grids in a uniform equal division mode, and using the vertex of each grid as a reference point RP;
2) Acquiring N Wi-Fi signal intensity data of a plurality of access points of each reference point RP on a single channel through signal acquisition equipment, and performing filtering processing to obtain the N Wi-Fi signal intensity data of the plurality of access points on the single channelTo form a set of signal fingerprint data
Where L denotes the L-th reference point RP position, L =1,2, \8230;, L;
3) Simultaneously acquiring coordinate information and MAC address of the position by each reference point RP, using the coordinate information and MAC address as a group of position fingerprint data, and establishing a position fingerprint database RP in an off-line state x
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110069453.XA CN112881979B (en) | 2021-01-19 | 2021-01-19 | Initial state self-adaptive fusion positioning method based on EKF filtering |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110069453.XA CN112881979B (en) | 2021-01-19 | 2021-01-19 | Initial state self-adaptive fusion positioning method based on EKF filtering |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112881979A CN112881979A (en) | 2021-06-01 |
| CN112881979B true CN112881979B (en) | 2022-10-21 |
Family
ID=76049740
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110069453.XA Active CN112881979B (en) | 2021-01-19 | 2021-01-19 | Initial state self-adaptive fusion positioning method based on EKF filtering |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112881979B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113566820B (en) * | 2021-06-17 | 2023-05-16 | 电子科技大学 | Fused pedestrian positioning method based on position fingerprint and PDR algorithm |
| CN114245460B (en) * | 2022-02-25 | 2022-05-27 | 深圳市海豚科技创新有限公司 | Positioning method, positioning system, electronic system, and computer-readable storage medium |
| CN114710744B (en) * | 2022-04-12 | 2023-03-21 | 电子科技大学 | Indoor positioning method integrating WiFi ranging and PDR calculation in depth |
| CN115103437B (en) * | 2022-04-27 | 2024-03-15 | 电子科技大学 | Tightly-coupled indoor positioning method of Bluetooth and inertial measurement unit |
| CN116303786B (en) * | 2023-03-18 | 2023-10-27 | 上海圈讯科技股份有限公司 | Block chain financial big data management system based on multidimensional data fusion algorithm |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105115487A (en) * | 2015-05-18 | 2015-12-02 | 陈希 | Positioning and navigation method in supermarket based on information fusion |
| CN107271951A (en) * | 2017-06-06 | 2017-10-20 | 上海工程技术大学 | UAV Navigation System and its air navigation aid based on WiFi fingerprint locations |
| CN107941211A (en) * | 2017-11-13 | 2018-04-20 | 北京邮电大学 | Multielement fusion and positioning method, device and electronic equipment based on Two-orders |
| CN108871325A (en) * | 2018-06-26 | 2018-11-23 | 北京航空航天大学 | A kind of WiFi/MEMS combination indoor orientation method based on two layers of Extended Kalman filter |
| CN109298389A (en) * | 2018-08-29 | 2019-02-01 | 东南大学 | Indoor pedestrian based on multiparticle group optimization combines position and orientation estimation method |
| CN110602647A (en) * | 2019-09-11 | 2019-12-20 | 江南大学 | Indoor fusion positioning method based on extended Kalman filtering and particle filtering |
| CN111722180A (en) * | 2020-07-02 | 2020-09-29 | 广东工业大学 | Kalman filtering-based indoor pedestrian positioning method, device and system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102253368B (en) * | 2011-04-18 | 2013-03-20 | 杭州华韵天略电子科技有限公司 | Target positioning and tracking system based on ultrasonic waves |
| CN105424030B (en) * | 2015-11-24 | 2018-11-09 | 东南大学 | Fusion navigation device and method based on wireless fingerprint and MEMS sensor |
| CN109323695A (en) * | 2018-10-29 | 2019-02-12 | 东南大学—无锡集成电路技术研究所 | A kind of indoor orientation method based on adaptive Unscented kalman filtering |
-
2021
- 2021-01-19 CN CN202110069453.XA patent/CN112881979B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105115487A (en) * | 2015-05-18 | 2015-12-02 | 陈希 | Positioning and navigation method in supermarket based on information fusion |
| CN107271951A (en) * | 2017-06-06 | 2017-10-20 | 上海工程技术大学 | UAV Navigation System and its air navigation aid based on WiFi fingerprint locations |
| CN107941211A (en) * | 2017-11-13 | 2018-04-20 | 北京邮电大学 | Multielement fusion and positioning method, device and electronic equipment based on Two-orders |
| CN108871325A (en) * | 2018-06-26 | 2018-11-23 | 北京航空航天大学 | A kind of WiFi/MEMS combination indoor orientation method based on two layers of Extended Kalman filter |
| CN109298389A (en) * | 2018-08-29 | 2019-02-01 | 东南大学 | Indoor pedestrian based on multiparticle group optimization combines position and orientation estimation method |
| CN110602647A (en) * | 2019-09-11 | 2019-12-20 | 江南大学 | Indoor fusion positioning method based on extended Kalman filtering and particle filtering |
| CN111722180A (en) * | 2020-07-02 | 2020-09-29 | 广东工业大学 | Kalman filtering-based indoor pedestrian positioning method, device and system |
Non-Patent Citations (4)
| Title |
|---|
| Research on Indoor Location Technology based on the Fusion of WiFi and PDR;Jianmei Sun et al.;《2020 13th International Conference on Intelligent Computation Technology and Automation (ICICTA)》;20201025;第416-419页 * |
| Robust Adaptive Extended Kalman Filtering for Smart Phone-based Pedestrian Dead Reckoning Systems;Dongjin Wu et al.;《2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS)》;20180323;第1-8页 * |
| 利用渐消自适应EKF算法进行PDR-WiFi融合定位;胡安冬 等;《武汉大学学报(信息科学版)》;20161130;第41卷(第11期);第1556-1562页 * |
| 扩展卡尔曼滤波的WiFi/PDR融合室内定位系统设计;蔡金洋 等;《单片机与嵌入式系统应用》;20200831;第20卷(第8期);第56-60页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112881979A (en) | 2021-06-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112881979B (en) | Initial state self-adaptive fusion positioning method based on EKF filtering | |
| CN108534779B (en) | Indoor positioning map construction method based on track correction and fingerprint improvement | |
| CN107703480B (en) | Mixed kernel function indoor positioning method based on machine learning | |
| CN105704652B (en) | Fingerprint base acquisition and optimization method in a kind of positioning of WLAN/ bluetooth | |
| CN105120479B (en) | The signal intensity difference modification method of terminal room Wi-Fi signal | |
| CN112533149B (en) | Moving target positioning algorithm based on UWB mobile node | |
| CN112584306A (en) | Indoor robot positioning algorithm based on Kalman filtering | |
| CN109490826B (en) | Ranging and position positioning method based on radio wave field intensity RSSI | |
| CN112367614B (en) | LSTM-based Wi-Fi and geomagnetic field fusion indoor positioning algorithm | |
| CN106199580B (en) | A kind of Singer model refinement algorithms based on fuzzy inference system | |
| CN114449452A (en) | Indoor positioning algorithm for heterogeneous equipment | |
| Fonseka et al. | Indoor localization for IoT applications using fingerprinting | |
| CN105208651A (en) | Wi-Fi position fingerprint non-monitoring training method based on map structure | |
| CN108834047B (en) | AP selection indoor positioning method of path loss model | |
| Abdou et al. | An efficient indoor localization system based on Affinity Propagation and Support Vector Regression | |
| CN111726757A (en) | Indoor parking lot positioning and navigation method based on Bluetooth | |
| Xingli et al. | Indoor positioning technology based on deep neural networks | |
| Xu et al. | A hybrid approach using multistage collaborative calibration for wireless sensor network localization in 3D environments | |
| CN111523571B (en) | Semi-supervised learning positioning method for distributed large-scale multi-antenna system | |
| CN108337639B (en) | A kind of Monte Carlo mobile node location algorithm based on Newton interpolation | |
| Tariq et al. | WSN Localization Method Based on Hybrid PSO-GRNN Approach. | |
| CN108462940A (en) | A kind of indoor orientation method based on Wi-Fi hotspot | |
| CN111257827B (en) | High-precision non-line-of-sight tracking and positioning method | |
| Ma et al. | A TDOA localization method for complex environment localization | |
| Zhang et al. | Indoor localization algorithm based on combination of Kalman filter and clustering |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |