CN114353787A - Multi-source fusion positioning method - Google Patents
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Abstract
The invention discloses a multi-source fusion positioning method, which comprises the following steps: acquiring positioning information to be corrected through a micro-electro-mechanical system sensor group in a user terminal; determining a target positioning algorithm corresponding to the user terminal, and determining reference positioning information according to the target positioning algorithm, wherein the target positioning algorithm is a positioning algorithm executed by the user terminal through an action hotspot; and correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal. The invention integrates the positioning results output by two positioning sources of the action hotspot and the micro-electro-mechanical system sensor, and solves the problem that the accurate positioning result is difficult to obtain by using a single indoor positioning source in the prior art.
Description
Technical Field
The invention relates to the field of intelligent terminals, in particular to a multi-source fusion positioning method.
Background
Wi-Fi based positioning is considered an effective way to achieve ubiquitous and high-precision indoor navigation, especially with the support of Wi-Fi Fine Time Measurement (FTM) protocols towards next generation wireless access points. A Micro Electro Mechanical System (MEMS) sensor can provide an accurate short-term navigation solution, while providing a feasible solution for building a navigation library based on Wi-Fi fingerprints by collecting and mining user's daily spatiotemporal trajectory information and opportunity signals acquired along the way. Currently, a positioning method in an intelligent terminal is generally used for positioning by adopting a single indoor positioning source, such as only Wi-Fi positioning or MEMS sensor positioning. Due to the complexity and diversity of indoor scenes, it is difficult to obtain accurate positioning results using a single indoor positioning source.
Thus, there is still a need for improvement and development of the prior art.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a multi-source fusion positioning method, aiming at solving the problem that it is difficult to obtain an accurate positioning result by using a single indoor positioning source in the prior art.
The technical scheme adopted by the invention for solving the problems is as follows:
in a first aspect, an embodiment of the present invention provides a multi-source fusion positioning method, where the method includes:
acquiring positioning information to be corrected through a micro-electro-mechanical system sensor group in a user terminal;
determining a target positioning algorithm corresponding to the user terminal, and determining reference positioning information according to the target positioning algorithm, wherein the target positioning algorithm is a positioning algorithm executed by the user terminal through an action hotspot;
and correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal.
In one embodiment, the acquiring, by the mems sensor group in the user terminal, the positioning information to be corrected includes:
acquiring angular velocity data output by the gyroscope;
acquiring acceleration data output by the accelerometer;
acquiring magnetic field data output by the magnetometer;
acquiring air pressure data output by the barometer, and determining height data corresponding to the user terminal according to the air pressure data;
and determining the positioning information to be corrected according to the angular velocity data, the acceleration data, the magnetic field data and the height data.
In one embodiment, the determining the positioning information to be corrected according to the angular velocity data, the acceleration data, the magnetic field data, and the altitude data includes:
inputting the angular velocity data and the acceleration data into an inertial navigation positioning model to obtain inertial navigation positioning information;
inputting the acceleration data into a pedestrian navigation positioning model to obtain pedestrian navigation positioning information;
and inputting the magnetic field data, the height data, the inertial navigation positioning information and the pedestrian navigation positioning information into a preset filtering algorithm to obtain the positioning information to be corrected.
In an embodiment, when the target location algorithm is a mobile hotspot ranging algorithm and a mobile hotspot fingerprinting algorithm, the determining the target location algorithm corresponding to the ue determines reference location information according to the target location algorithm, including:
determining first reference positioning information corresponding to the user terminal through the mobile hotspot ranging algorithm;
determining second reference positioning information corresponding to the user terminal through the action hotspot fingerprint algorithm;
and using the first reference positioning information and the second reference positioning information as the reference positioning information.
In one embodiment, the determining, by the mobile hotspot ranging algorithm, first reference location information corresponding to the ue includes:
acquiring channel state data between a plurality of first access points and the user terminal respectively;
inputting the channel state data corresponding to the first access points into the mobile hotspot ranging algorithm to obtain distance data between the first access points and the user terminal;
and determining the first reference positioning information according to the distance data respectively corresponding to the plurality of first access points.
In one embodiment, the determining, by the mobile hotspot fingerprinting algorithm, second reference location information corresponding to the user terminal includes:
acquiring signal intensity data between a plurality of second access points and the user terminal respectively;
and inputting the signal intensity data corresponding to the second access points into the action hotspot fingerprint algorithm to obtain the second reference positioning information.
In an embodiment, the modifying the positioning information to be modified according to the reference positioning information to obtain the target positioning information corresponding to the user terminal includes:
determining first error data corresponding to the micro-electro-mechanical system sensor group according to the first reference positioning information;
correcting the positioning information to be corrected according to the first error data to obtain corrected positioning information;
determining second error data corresponding to the micro-electro-mechanical system sensor group according to the second reference positioning information;
and correcting the corrected positioning information according to the second error data to obtain the target positioning information.
In one embodiment, when the target location algorithm is an action hotspot fingerprint algorithm, the determining the target location algorithm corresponding to the user terminal and determining reference location information according to the target location algorithm includes:
and determining the reference positioning information through the action hotspot fingerprint algorithm.
In an embodiment, the modifying the positioning information to be modified according to the reference positioning information to obtain the target positioning information corresponding to the user terminal includes:
determining error data corresponding to the micro-electro-mechanical system sensor group according to the reference positioning information;
and correcting the positioning information to be corrected according to the error data to obtain the target positioning information.
In a second aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a plurality of instructions are stored, where the instructions are adapted to be loaded and executed by a processor, so as to implement any of the steps of the multi-source fusion positioning method described above.
The invention has the beneficial effects that: in the embodiment of the invention, the positioning information to be corrected is acquired through a micro-electro-mechanical system sensor group in the user terminal; determining a target positioning algorithm corresponding to the user terminal, and determining reference positioning information according to the target positioning algorithm, wherein the target positioning algorithm is a positioning algorithm executed by the user terminal through an action hotspot; and correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal. The invention integrates the positioning results output by two positioning sources of the action hotspot and the micro-electro-mechanical system sensor, and solves the problem that the accurate positioning result is difficult to obtain by using a single indoor positioning source in the prior art.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a multi-source fusion positioning method provided in an embodiment of the present invention.
Fig. 2 is a schematic diagram of a closed-loop detection algorithm provided in the embodiment of the present invention.
Fig. 3 is a schematic diagram of data fusion performed by using an unscented kalman filter algorithm according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a trust ellipse provided by an embodiment of the present invention.
FIG. 5 is a schematic diagram illustrating a comparison between the heading solutions provided by the embodiment of the invention.
Fig. 6 is a schematic diagram for comparing the coordinate calculation effects provided by the embodiment of the invention.
FIG. 7 is a comparison diagram of positioning trajectories of different combination models provided by the embodiment of the invention.
FIG. 8 is a comparison graph of positioning accuracy of different combination models provided by the embodiment of the present invention.
Fig. 9 is a comparison diagram of positioning trajectories of different combination models in a large-scale indoor scene according to an embodiment of the present invention.
Fig. 10 is a comparison diagram of positioning accuracy of the present algorithm and similar algorithms in an office scenario according to an embodiment of the present invention.
Fig. 11 is a comparison diagram of positioning accuracy of the present algorithm and similar algorithms in a corridor scene provided by the embodiment of the present invention.
Fig. 12 is a schematic diagram of an internal module of a multi-source positioning device according to an embodiment of the present invention.
Fig. 13 is a functional block diagram of a terminal according to an embodiment of the present invention.
Detailed Description
The invention discloses a multi-source fusion positioning method, which is further described in detail below by referring to the attached drawings and embodiments in order to make the purpose, technical scheme and effect of the invention clearer and clearer. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Wi-Fi based positioning is considered an effective way to achieve ubiquitous and high-precision indoor navigation, especially with the support of Wi-Fi Fine Time Measurement (FTM) protocols towards next generation wireless access points. A Micro Electro Mechanical System (MEMS) sensor can provide an accurate short-term navigation solution, while providing a feasible solution for building a navigation library based on Wi-Fi fingerprints by collecting and mining user's daily spatiotemporal trajectory information and opportunity signals acquired along the way. Currently, a positioning method in an intelligent terminal is generally used for positioning by adopting a single indoor positioning source, such as only Wi-Fi positioning or MEMS sensor positioning. Due to the complexity and diversity of indoor scenes, it is difficult to obtain accurate positioning results using a single indoor positioning source.
In view of the above-mentioned drawbacks of the prior art, the present invention provides a multi-source fusion positioning method, including: acquiring positioning information to be corrected through a micro-electro-mechanical system sensor group in a user terminal; determining a target positioning algorithm corresponding to the user terminal, and determining reference positioning information according to the target positioning algorithm, wherein the target positioning algorithm is a positioning algorithm executed by the user terminal through an action hotspot; and correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal. The invention integrates the positioning results output by two positioning sources of the action hotspot and the micro-electro-mechanical system sensor, and solves the problem that the accurate positioning result is difficult to obtain by using a single indoor positioning source in the prior art.
As shown in fig. 1, the method comprises the steps of:
and S100, acquiring the positioning information to be corrected through a micro-electro-mechanical system sensor group in the user terminal.
Specifically, the mems sensor group in this embodiment may be a sensor group used for positioning in any user terminal device. The micro-electro-mechanical system sensor group calculates the current position information of the user by acquiring the angle change information and the speed change information when the terminal equipment moves. However, because the mems sensor group has error accumulation and noise signals, there is usually a large difference between the positioning information directly output by the mems sensor group and the real position information of the user, and therefore, the embodiment does not directly adopt the positioning information output by the mems sensor group, but uses the positioning information output by the mems sensor group as the positioning information to be corrected.
In one implementation, the mems sensor group includes a magnetometer, a gyroscope, an accelerometer, and a barometer, and the step S100 specifically includes the following steps:
s101, acquiring angular velocity data output by the gyroscope;
step S102, acquiring acceleration data output by the accelerometer;
step S103, acquiring magnetic field data output by the magnetometer;
step S104, acquiring air pressure data output by the barometer, and determining height data corresponding to the user terminal according to the air pressure data;
step S105, determining the positioning information to be corrected according to the angular velocity data, the acceleration data, the magnetic field data and the height data.
Specifically, the mems sensor group in this embodiment mainly includes a magnetometer, a gyroscope, an accelerometer, and a barometer. The magnetometer can calculate the magnetic field around the user terminal and output magnetic field data; the gyroscope can calculate the angular deflection condition of the user terminal and output angular velocity data; the accelerometer can calculate the speed change condition of the user terminal and output acceleration data; the barometer can calculate the air pressure data around the user terminal, so that the height of the user terminal is calculated based on the air pressure data, and the height data is output. And finally, the user terminal can calculate the positioning information to be corrected based on the angular velocity data, the acceleration data, the magnetic field data and the height data acquired by the micro-electro-mechanical system sensor group.
In one implementation, the step S102 specifically includes the following steps:
step S1021, inputting the angular velocity data and the acceleration data into an inertial navigation positioning model to obtain inertial navigation positioning information;
step S1022, inputting the acceleration data into a pedestrian navigation positioning model to obtain pedestrian navigation positioning information;
and S1023, inputting the magnetic field data, the height data, the inertial navigation positioning information and the pedestrian navigation positioning information into a preset filtering algorithm to obtain the positioning information to be corrected.
In brief, in order to improve the positioning accuracy of the mems sensor, in this embodiment, the data collected by the mems sensor group are respectively input into the two positioning models, and then the positioning information output by the two positioning models is fused into the positioning information to be corrected. Specifically, the present embodiment inputs the angular velocity data and the acceleration data into the inertial navigation positioning model to obtain the inertial navigation positioning information. The basic working principle of inertial navigation is based on Newton's law of mechanics, and by measuring the acceleration of a carrier in an inertial reference system, integrating the acceleration with time and transforming the acceleration into a navigation coordinate system, information such as speed, yaw angle and position in the navigation coordinate system can be obtained. Secondly, the embodiment can also input the acceleration data into the pedestrian navigation positioning model to obtain the pedestrian navigation positioning information. The pedestrian navigation technology is an accurate positioning technology capable of providing navigation services such as walking planning and the like for pedestrians, and outputs positioning information of the pedestrians by measuring the step length and the course of the pedestrians. In order to fuse the positioning information respectively output by the two positioning models, the embodiment inputs the magnetic field data, the height data, the inertial navigation positioning information and the pedestrian navigation positioning information into a preset filtering algorithm, fuses the input data through the filtering algorithm, and outputs the positioning information to be corrected.
In one implementation, the inertial navigation positioning model includes a posture update module, a velocity update module, and a position update module, and the working principles of the three modules are as follows:
a posture updating module:
wherein the content of the first and second substances,
for the updated attitude quaternion information,
is the attitude quaternion information of the last moment,
is the variation of the attitude quaternion.
A speed update module:
wherein the content of the first and second substances,
for the purpose of the updated velocity vector,
for the amount of velocity compensation, gnFor acceleration of gravity, TsIs the sampling interval.
A location update module:
wherein the content of the first and second substances,
in order to be able to update the position vector,
the speed value at the previous moment.
In one implementation manner, the pedestrian navigation positioning model includes a step length calculation module, a two-dimensional coordinate updating module and a three-dimensional height updating module, wherein the working principle of the three modules is as follows:
a step length calculation module:
α＝μ·(Amax-Amin)1/4
wherein alpha is the pedestrian step length calculated after gait detection, mu is the step length coefficient, AmaxAnd AminThe peak value and the valley value of the acceleration module value.
A two-dimensional coordinate updating module:
wherein (E)t-1，Nt-1) And (E)t，Nt) The two-dimensional position coordinates of the current moment and the last moment are respectively, alpha (t) is the step value of the current moment, and theta (t) is the course value of the current moment.
A three-dimensional height update module:
wherein,. DELTA.htAltitude update value, p, calculated from barometric pressure updatetIs the barometer output value, p0Is a reference air pressure value.
In one implementation, as shown in fig. 3, the filtering algorithm is an unscented kalman filtering algorithm, and the step S1023 specifically includes the following steps:
inputting the magnetic field data, the altitude data, the inertial navigation positioning information and the pedestrian navigation positioning information into the unscented kalman filter algorithm;
and determining speed error data, position error data and magnetic field error data corresponding to the inertial navigation positioning information based on the magnetic field data, the height data, the inertial navigation positioning information and the pedestrian navigation positioning information through the unscented Kalman filtering algorithm, correcting the inertial navigation positioning information according to the speed error data, the position error data and the magnetic field error data, and outputting positioning information to be corrected after correction. In other words, in this embodiment, the inertial navigation positioning information and the pedestrian navigation positioning information are fused through the unscented kalman filter algorithm, actually, the inertial navigation positioning information is used as a state quantity through the unscented kalman filter algorithm, the pedestrian navigation positioning information is used as an observed quantity, an error value of the state quantity is determined based on the observed quantity, then, the state quantity is corrected based on the error value, and the positioning information to be corrected output by the mems sensor group is obtained after the correction.
For example, the operation principle of the unscented kalman filter algorithm is as follows:
firstly, establishing an error model for updating the position speed and the attitude of the sensor:
wherein, δ pn，δvn，φ1×3，
Respectively representing position error, velocity error, attitude error, gyroscope zero offset and accelerometer zero offset.
And then establishing a state updating model and an equation of the error vector, and predicting the fifteen-dimensional error in one step:
in the formula (I), the compound is shown in the specification,
is an acceleration vector, τbgAnd τbaIs a measurement error coefficient associated with an event, wεgAnd wεaTo drive noise.
And finally, establishing an observation equation to update the error vector, wherein the observation updating of the velocity vector is carried out:
wherein the content of the first and second substances,
is an error vector for the velocity of the object,
the speed information provided for the pedestrian navigation algorithm,
velocity information provided for inertial navigation algorithms.
Then the location update:
wherein the content of the first and second substances,
is an error vector for the position of the object,
the position information provided for the pedestrian navigation algorithm,
position information provided for inertial navigation algorithms.
And finally, updating the magnetic field vector, firstly extracting the magnetic field vector at the first moment as a reference value:
wherein the content of the first and second substances,
for the purpose of calculating the reference vector of the magnetic field,
in order to be a matrix of rotations,
is the magnetometer output value.
Next, an observation update model of the magnetic field vector is established:
wherein the content of the first and second substances,
is the error vector of the magnetic field and,
in order to rotate the matrix in real-time,
and outputting the value for the magnetometer in real time.
As shown in fig. 1, the method further comprises the steps of:
step S200, determining a target positioning algorithm corresponding to the user terminal, and determining reference positioning information according to the target positioning algorithm, wherein the target positioning algorithm is a positioning algorithm executed by the user terminal through an action hotspot.
In particular, mobile hot-spots (Wi-Fi) are a wireless networking technology that is generally applicable to a variety of user terminals. At present, an intelligent terminal comprises one or more positioning algorithms executed based on action hotspots, and because the positioning algorithms related to the action hotspots are usually affected by signal strength and channel state, compared with a micro-electro-mechanical system sensor, the range of positioning by adopting the action hotspots is smaller, but the precision is higher. In this embodiment, a positioning algorithm implemented based on a mobile hotspot included in a user terminal is defined as a target positioning algorithm, and since target algorithms included in different user terminals may have differences, for example, a terminal of a user a only has a mobile hotspot fingerprint algorithm, and a user B has not only a mobile hotspot fingerprint algorithm but also a mobile hotspot ranging algorithm. It is therefore necessary to determine what target location algorithm is included in the current ue. Because the positioning precision of the target positioning algorithm is higher than that of the micro-electro-mechanical system sensor group, the positioning information output based on the target positioning algorithm is used as reference positioning information for correcting the positioning information output by the micro-electro-mechanical system sensor group.
In an implementation manner, when the target location algorithm is a mobile hotspot ranging algorithm and a mobile hotspot fingerprint algorithm, the step S200 specifically includes the following steps:
step S201, determining first reference positioning information corresponding to the user terminal through the mobile hotspot ranging algorithm;
step S202, second reference positioning information corresponding to the user terminal is determined through the action hotspot fingerprint algorithm;
step S203, using the first reference positioning information and the second reference positioning information as the reference positioning information.
Specifically, if the current ue includes both a mobile hotspot ranging algorithm and a mobile hotspot fingerprint algorithm, a mobile hotspot ranging algorithm and a mobile hotspot fingerprint algorithm are used to output a piece of positioning information, where the positioning information output by the mobile hotspot ranging algorithm is defined as first positioning information, and the positioning information output by the mobile hotspot fingerprint algorithm is defined as second positioning information. And then, both the two kinds of positioning information are used as reference positioning information for correcting the positioning information to be corrected.
In one implementation, the step S201 specifically includes the following steps:
step S2011, channel state data between each of a plurality of first access points and the user terminal are acquired;
step S2012, inputting the channel state data corresponding to the plurality of first access points to the mobile hotspot ranging algorithm to obtain distance data between the plurality of first access points and the user equipment;
step S2013, the first reference positioning information is determined according to the distance data corresponding to the first access points respectively.
Specifically, the plurality of first access points may be a plurality of Wi-Fi access points arranged around the user terminal, and by obtaining channel state data between each first access point and the user terminal, distance data between each first access point and the user terminal may be calculated, and a position of the user terminal may be calculated based on all the obtained distance data, that is, the first reference positioning information may be obtained.
In one implementation, the plurality of first access points include a first access point and a second access point, and the step S2013 specifically includes the following steps:
acquiring position information corresponding to the first access point and the second access point respectively;
determining ranging and positioning information according to the distance data and the position information respectively corresponding to the first access point and the second access point;
constructing a vector to be checked according to the position information and the ranging positioning information respectively corresponding to the first access point and the second access point;
and judging whether the closed loop of the vector to be detected is established, and when the closed loop of the vector to be detected is established, using the ranging positioning information as the first reference positioning information.
For example, the principle of closed loop detection is as follows:
1. constructing a ranging model of a mobile hotspot ranging algorithm:
Lobserved＝LFTM+dbias+dN+drandom
wherein L isobservedFor the range-finding value, L, obtained at the receiving endFTMIs the true value, dbiasIs an initial zero offset value, dNIs a non-line-of-sight error value, drandomIs a random error value.
2. Establishing a closed-loop model, as shown in fig. 2, wherein the closed-loop model in an ideal state is:
wherein the content of the first and second substances,
and
respectively representing vectors constructed among the three points a, B and C. The closed-loop model after the error amount is added is as follows:
wherein the newly added parameter dpIndicating the uncertainty error introduced by the location update in S102.
3. The closed-loop model equation is converted from a vector form to a coordinate form:
wherein, XBCAnd YBCRespectively representing closed-loop vectors
The result of the decomposition of the coordinates of (2),
and
respectively, the coordinate difference between the position coordinates predicted based on one step and two Wi-Fi base stations.
4. Constructing a corresponding vector for the Wi-Fi precision ranging result according to the predicted position coordinates:
wherein the content of the first and second substances,
and
representing a ranging vector constructed based on the updated position,
representing the actual distance measurement result between the points A and B,
representing the euclidean distance between the predicted location a and the point B corresponding to the Wi-Fi AP. Further closed-loop vectors in the form of coordinates can be constructed:
wherein the content of the first and second substances,
whether the final closed loop result holds for the closed loop vector can be expressed by the following equation:
wherein the content of the first and second substances,
for closed-loop vector norm, DrVariance of random error for ranging, Dpμ is the scale for the variance of uncertainty error of the predicted position. By using the method, when the modulus value of the closed-loop vector is larger than the calculation result on the right side, the closed-loop vector can be considered to be interfered by a non-line-of-sight error.
In an implementation manner, the step S202 specifically includes the following steps:
step S2021, acquiring signal intensity data between a plurality of second access points and the user terminal respectively;
step S2022, inputting the signal strength data corresponding to the plurality of second access points into the action hotspot fingerprint algorithm to obtain the second reference positioning information.
In particular, the number of second access points may be Wi-Fi access points around the user terminal, wherein the second access points may have coinciding access points with the first access point. And acquiring signal intensity data between each second access point and the user terminal, wherein the signal intensity data can reflect the distance between each second access point and the user terminal to a certain extent, so that the current position information of the user terminal, namely second reference positioning information, can be acquired through the signal intensity data of each second access point and a mobile hotspot fingerprint algorithm.
In one implementation, the step S2022 specifically includes the following steps:
acquiring a preset action hotspot fingerprint database, wherein the action hotspot fingerprint database comprises a plurality of reference access points, and each reference access point is provided with a signal intensity label and an address label;
comparing the signal intensity data respectively corresponding to the second access points with the action hotspot fingerprint database to obtain a plurality of candidate reference access points;
inputting a plurality of reference access points into a nearest neighbor matching algorithm to obtain weight values corresponding to the candidate reference access points respectively;
and determining the second reference positioning information according to the weight values and the address labels respectively corresponding to the candidate reference access points.
For example, the working principle of the nearest neighbor matching algorithm is as follows:
k value self-adaptive adjustment and error evaluation in the nearest neighbor matching method:
in the formula (I), the compound is shown in the specification,
is the nearest value among the K nearest neighbor values, Dist，otherAnd when gamma is smaller than a set threshold value kappa, keeping K adjacent values meeting the condition, thereby completing the self-adaptive adjustment of the K value. The calculated weighted positions are as follows:
wherein the content of the first and second substances,
for each corresponding nearest neighbor position weight, POS(xi，yi) Are the corresponding two-dimensional position coordinates. The weighted position error estimation formula is as follows:
wherein, Prssi(t) and Prssi(t-1) position coordinates of the previous and subsequent time points obtained by fingerprint matching, PMEMS(t) and PMEMS(t-1) represents the position coordinates of the previous time and the next time obtained using the micro sensor method.
In an implementation manner, in this embodiment, the second reference location information needs to be checked to improve the data reliability of the second reference location information, and the checking method includes:
1. acquiring preset trusted position area information, and comparing the second reference positioning information with the trusted position area information;
2. when the position corresponding to the second reference positioning information is located in the region corresponding to the trust position region information, judging that the second reference positioning information is available;
3. and when the position corresponding to the second reference positioning information is located outside the area corresponding to the trust position area information, judging that the second reference positioning information is unavailable.
In another implementation manner, when the target location algorithm is an action hotspot fingerprint algorithm, the step S200 specifically includes the following steps:
and step S204, determining the reference positioning information through the action hotspot fingerprint algorithm.
Specifically, since not every terminal can be positioned by using the action hotspot ranging algorithm, if only the action hotspot fingerprint algorithm exists on the current user terminal, the action hotspot fingerprint algorithm is the target positioning algorithm, and the positioning information obtained by the action hotspot fingerprint algorithm is the reference positioning information.
As shown in fig. 1, the method further comprises the steps of:
step S300, the positioning information to be corrected is corrected according to the reference positioning information, and target positioning information corresponding to the user terminal is obtained.
Specifically, because the reference positioning information is the positioning information of the user terminal calculated based on the action hot spot, compared with the positioning information to be corrected obtained based on the micro-electro-mechanical system sensor group, the reference positioning information has less error accumulation and is more accurate in positioning. Therefore, in the embodiment, the reference positioning information is adopted to correct the positioning information to be corrected, and the positioning information obtained after correction is used as the target positioning information finally output by the micro electro mechanical system sensor group.
In an implementation manner, when the target location algorithm is a mobile hotspot ranging algorithm and a mobile hotspot fingerprint algorithm, the step S300 specifically includes the following steps:
step S301, determining first error data corresponding to the micro-electromechanical system sensor group according to the first reference positioning information;
step S302, correcting the positioning information to be corrected according to the first error data to obtain corrected positioning information;
step S303, determining second error data corresponding to the micro-electromechanical system sensor group according to the second reference positioning information;
and step S304, correcting the corrected positioning information according to the second error data to obtain the target positioning information.
In brief, when a mobile hotspot ranging algorithm exists in the user terminal, the first reference positioning information obtained based on the mobile hotspot ranging algorithm is preferentially adopted to correct the positioning information to be corrected, and then the second reference positioning information obtained based on the mobile hotspot fingerprint algorithm is adopted to correct the positioning information to be corrected. Specifically, first error data of the MEMS sensor group is determined based on first reference positioning information, and corrected positioning information is obtained after the first error data in the positioning information to be corrected is eliminated. And then determining second error data of the micro-electro-mechanical system sensor group based on the second reference positioning information, and eliminating the second error data in the corrected positioning information to obtain the final target positioning information output by the micro-electro-mechanical system sensor group.
In another implementation manner, when the target location algorithm is an action hotspot fingerprint algorithm, the step S300 specifically includes the following steps:
step S305, determining error data corresponding to the micro-electromechanical system sensor group according to the reference positioning information;
and S306, correcting the positioning information to be corrected according to the error data to obtain the target positioning information.
In brief, when only the action hotspot fingerprint algorithm exists in the user terminal, only the reference positioning information obtained based on the action hotspot fingerprint algorithm is adopted to correct the positioning information to be corrected. Specifically, error data of the MEMS sensor group is determined based on the reference positioning information, and the target positioning information output by the MEMS sensor group is finally obtained after the error data in the positioning information to be corrected is eliminated.
For example, a close coupling model based on Wi-Fi precision ranging and a micro sensor is established, and a state quantity is constructed by using a sensor error and a Wi-Fi ranging zero offset error:
where F is the state matrix, GsFor a state noise drive matrix, ∈sIs state noise. The Wi-Fi precision ranging zero offset error equation is constructed as follows:
wherein, bRTTFor the Wi-Fi precision ranging zero offset error,
for the purpose of the time-dependent coefficients,
is gaussian white noise. The final constructed augmented state equation is:
the observation updating equation based on the Wi-Fi precision ranging result is as follows:
wherein δ zdFor the difference between the actual Wi-Fi precision range value and the sensor position based range value, dFTM,mAnd dMEMS,mRespectively representing Wi-Fi precision ranging values and sensor location based ranging values.
Then, a loose coupling model based on Wi-Fi fingerprints and the micro sensors is established, the state quantity is constructed by using the error values of the sensors, and an observation equation is constructed by using the position coordinates provided by the fingerprints as the observed quantity:
in the formula (I), the compound is shown in the specification,
and
representing position and velocity observations derived by a fingerprinting method,
and
represents the observed quantity of position and velocity derived by the microsensor method.
And finally, establishing a mixed positioning model, as shown in fig. 4, firstly establishing a trust ellipse for removing the observation gross error in the final fusion process, wherein the major axis of the trust ellipse is calculated as follows:
wherein a is the calculated ellipse major axis,
and
representing the north and east position error covariance parameters in the covariance matrix in the filter,
is the northeast position error covariance parameter, s, in the covariance matrix in the filtereIs a scaling factor. The short axis of the confidence ellipse is calculated as follows:
the azimuth angle of the confidence ellipse from the true north direction is recorded as follows:
in the initial stage of multi-source fusion positioning, when a pedestrian moves from a scene supporting Wi-Fi precision measurement to a scene not supporting Wi-Fi precision measurement, the error variance of positioning initialization is large due to insufficient iteration times; therefore, the value of the parameter Se is adjusted high to avoid removing the location result to which the useful fingerprint is matched. And gradually reducing the value of Se along with the increase of the iteration times, and finally keeping the value unchanged so as to eliminate the gross error of the Wi-Fi fingerprint result.
In order to prove the technical effect of the invention, the inventor performs the following experiments:
by comparing the course calculation accuracy based on the micro-sensor and the course calculation accuracy based on the gyroscope and the magnetometer, the embodiment of the invention can be found to obtain a better course calculation result. Similarly, the position calculation accuracy based on the micro-sensor is compared with the position calculation accuracy of the traditional dead reckoning algorithm, and a better result is obtained. The heading solution and positioning result pairs are shown in fig. 5 and 6.
Fig. 7 and 8 further show the positioning trajectories and the corresponding positioning accuracies using several different combination patterns proposed by the present invention. It can be seen from fig. 8 that a large accumulated error still exists in the positioning mode using a single micro sensor, the error existing in the positioning mode using the single micro sensor can be effectively eliminated by the loose coupling model, the positioning accuracy is higher by the original tight coupling model using Wi-Fi precision ranging and micro sensor combined positioning, the accuracy after the self-calibration algorithm is used is further improved, and the highest positioning accuracy is realized by the final hybrid positioning model. Fig. 9 compares the positioning effect of the multi-source fusion algorithm and the positioning effect of a single positioning source provided by the present invention in a larger-scale indoor office scene, and it can be found that the positioning accuracy better than 1.06 m in 75% of cases can be achieved in an office scene supported by Wi-Fi precision ranging using the multi-source fusion indoor positioning frame provided by the embodiment of the present invention, and the positioning accuracy better than 1.65 m in 75% of cases can be achieved in a corridor scene not supported by Wi-Fi precision ranging. The positioning effect that compares the location source realization that uses the microsensor has had apparent promotion, can satisfy the demand that uses the indoor location of the ordinary crowd high accuracy of smart mobile phone terminal effectively. Fig. 10 and fig. 11 compare the positioning accuracy of the algorithm proposed by the present invention and the positioning accuracy of two similar algorithms in office and corridor scenes, respectively, and it can be found that the algorithm proposed by the present invention obtains higher positioning accuracy in both indoor scenes, so that the algorithm has stronger robustness and universality.
Based on the above embodiment, the present invention further provides a multi-source positioning apparatus, as shown in fig. 12, the apparatus includes:
the MEMS sensor group 01 is used for acquiring positioning information to be corrected;
the action hotspot module 02 is used for determining a target positioning algorithm corresponding to the user terminal and determining reference positioning information according to the target positioning algorithm, wherein the target positioning algorithm is a positioning algorithm executed by the user terminal through an action hotspot;
and the positioning correction module 03 is configured to correct the positioning information to be corrected according to the reference positioning information, so as to obtain target positioning information corresponding to the user terminal.
Based on the above embodiments, the present invention further provides a terminal, and a schematic block diagram thereof may be as shown in fig. 13. The terminal comprises a processor, a memory, a network interface and a display screen which are connected through a system bus. Wherein the processor of the terminal is configured to provide computing and control capabilities. The memory of the terminal comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the terminal is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement a multi-source fusion localization method. The display screen of the terminal can be a liquid crystal display screen or an electronic ink display screen.
It will be understood by those skilled in the art that the block diagram of fig. 13 is only a block diagram of a portion of the structure associated with the solution of the present invention, and does not constitute a limitation of the terminal to which the solution of the present invention is applied, and a specific terminal may include more or less components than those shown in the drawings, or may combine some components, or have a different arrangement of components.
In one implementation, one or more programs are stored in a memory of the terminal and configured to be executed by one or more processors include instructions for performing a multi-source fusion localization method.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
In summary, the present invention discloses a multi-source fusion positioning method, which includes: acquiring positioning information to be corrected through a micro-electro-mechanical system sensor group in a user terminal; determining a target positioning algorithm corresponding to the user terminal, and determining reference positioning information according to the target positioning algorithm, wherein the target positioning algorithm is a positioning algorithm executed by the user terminal through an action hotspot; and correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal. The invention integrates the positioning results output by two positioning sources of the action hotspot and the micro-electro-mechanical system sensor, and solves the problem that the accurate positioning result is difficult to obtain by using a single indoor positioning source in the prior art.
It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected thereto by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.
Claims (10)
1. A multi-source fusion positioning method is characterized by comprising the following steps:
acquiring positioning information to be corrected through a micro-electro-mechanical system sensor group in a user terminal;
determining a target positioning algorithm corresponding to the user terminal, and determining reference positioning information according to the target positioning algorithm, wherein the target positioning algorithm is a positioning algorithm executed by the user terminal through an action hotspot;
and correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal.
2. The multi-source fusion positioning method according to claim 1, wherein the mems sensor group comprises a magnetometer, a gyroscope, an accelerometer, and a barometer, and the obtaining of the positioning information to be corrected by the mems sensor group in the user terminal comprises:
acquiring angular velocity data output by the gyroscope;
acquiring acceleration data output by the accelerometer;
acquiring magnetic field data output by the magnetometer;
acquiring air pressure data output by the barometer, and determining height data corresponding to the user terminal according to the air pressure data;
and determining the positioning information to be corrected according to the angular velocity data, the acceleration data, the magnetic field data and the height data.
3. The multi-source fusion positioning method according to claim 2, wherein the determining the positioning information to be corrected according to the angular velocity data, the acceleration data, the magnetic field data, and the altitude data comprises:
inputting the angular velocity data and the acceleration data into an inertial navigation positioning model to obtain inertial navigation positioning information;
inputting the acceleration data into a pedestrian navigation positioning model to obtain pedestrian navigation positioning information;
and inputting the magnetic field data, the height data, the inertial navigation positioning information and the pedestrian navigation positioning information into a preset filtering algorithm to obtain the positioning information to be corrected.
4. The multi-source fusion positioning method according to claim 1, wherein when the target positioning algorithm is an action hotspot ranging algorithm and an action hotspot fingerprint algorithm, the determining a target positioning algorithm corresponding to the user terminal and determining reference positioning information according to the target positioning algorithm includes:
determining first reference positioning information corresponding to the user terminal through the mobile hotspot ranging algorithm;
determining second reference positioning information corresponding to the user terminal through the action hotspot fingerprint algorithm;
and using the first reference positioning information and the second reference positioning information as the reference positioning information.
5. The multi-source fusion positioning method according to claim 4, wherein the determining the first reference positioning information corresponding to the ue by the mobile hotspot ranging algorithm comprises:
acquiring channel state data between a plurality of first access points and the user terminal respectively;
inputting the channel state data corresponding to the first access points into the mobile hotspot ranging algorithm to obtain distance data between the first access points and the user terminal;
and determining the first reference positioning information according to the distance data respectively corresponding to the plurality of first access points.
6. The multi-source fusion positioning method according to claim 4, wherein the determining second reference positioning information corresponding to the user terminal by the action hotspot fingerprint algorithm includes:
acquiring signal intensity data between a plurality of second access points and the user terminal respectively;
and inputting the signal intensity data corresponding to the second access points into the action hotspot fingerprint algorithm to obtain the second reference positioning information.
7. The multi-source fusion positioning method according to claim 4, wherein the correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal includes:
determining first error data corresponding to the micro-electro-mechanical system sensor group according to the first reference positioning information;
correcting the positioning information to be corrected according to the first error data to obtain corrected positioning information;
determining second error data corresponding to the micro-electro-mechanical system sensor group according to the second reference positioning information;
and correcting the corrected positioning information according to the second error data to obtain the target positioning information.
8. The multi-source fusion positioning method according to claim 1, wherein when the target positioning algorithm is an action hotspot fingerprint algorithm, the determining a target positioning algorithm corresponding to the user terminal and determining reference positioning information according to the target positioning algorithm includes:
and determining the reference positioning information through the action hotspot fingerprint algorithm.
9. The multi-source fusion positioning method according to claim 8, wherein the correcting the positioning information to be corrected according to the reference positioning information to obtain target positioning information corresponding to the user terminal includes:
determining error data corresponding to the micro-electro-mechanical system sensor group according to the reference positioning information;
and correcting the positioning information to be corrected according to the error data to obtain the target positioning information.
10. A computer readable storage medium having stored thereon a plurality of instructions adapted to be loaded and executed by a processor to perform the steps of the multi-source fusion localization method according to any of the preceding claims 1-9.
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