CN108668249B - Indoor positioning method and device for mobile terminal - Google Patents
Indoor positioning method and device for mobile terminal Download PDFInfo
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- 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
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Abstract
The embodiment of the invention provides an indoor positioning method and device for a mobile terminal, wherein the method comprises the following steps: acquiring signal intensity information corresponding to a plurality of preset sampling points in a space position in an off-line manner to serve as fingerprint point signal information; constructing a position fingerprint database according to the fingerprint point signal information; acquiring initial state information of displacement motion of the mobile terminal within current preset unit time; calculating a reference position of a displacement ending position by utilizing the displacement motion starting state information of the mobile terminal and utilizing an inertial navigation principle; and acquiring signal intensity information respectively corresponding to preset unit time points on a displacement motion trail of the mobile terminal, constructing a sparse fingerprint positioning model based on space position constraint, correcting the reference position of the displacement termination position, and acquiring the corrected position of the displacement termination position. The embodiment of the invention realizes higher positioning precision with lower cost.
Description
Technical Field
The invention relates to the technical field of mobile internet, in particular to an indoor positioning method and device for a mobile terminal.
Background
With the rapid development of computer software and hardware technologies, the rapid popularization of wireless networks and the wide application of mobile intelligent terminal equipment, the mobile internet is applied more and more widely, the application requirements based on location-based services show a rapid and greatly-increased trend, and the mobile internet can be gradually applied to various fields of social production and life because the mobile internet can provide accurate positioning information for target positioning, emergency rescue, traffic management and the like, and has a good technical development prospect and a huge application market space. Reliable and efficient positioning technology is a prerequisite and key to the implementation of location-based services.
The tool software and the mobile service based on the mobile platform emerge like spring after rain, and provide rich service for various industrial users and mass users. The application of the global satellite positioning system constructs a bridge between human activities and geographic positions; the sensor network is realized based on the mobile internet, so that the dream of efficient communication between people and objects is realized; the popularization of the Wireless Local Area Network (WLAN) solves the problem of regional interaction of mass information, and realizes the terminal closed loop of the global mobile internet. The mobile chemical tools and services can promote the improvement of the productivity of human society, subvert the traditional life style of people and promote the wave of mobile informatization in a brand new mode. However, before the mobile internet is drawn to change the beautiful blueprint of the world, some technical bottlenecks restricting the further development of the mobile internet application service must be broken through, and one of the high-precision indoor positioning technologies is. In the mobile internet, the location-based service is one of the most frequently used and widely used services, and many other mobile applications use the location-based service directly or indirectly.
In the open outdoor environment, with the help of global satellite positioning systems (e.g., GPS, russian GLONASS, european Galileo, chinese beidou, etc.), location-based services have been able to provide users with high-precision, high-stability location services, the applications of which have penetrated various industrial fields and mass markets. However, in an indoor place with more human activities, due to the influences of factors such as building shielding, signal attenuation, complex wireless propagation environment, and inability to transmit line-of-sight between a satellite and a receiver, the received satellite signal is often distorted, and the application of the outdoor positioning method in the indoor environment is greatly restricted, so that the accurate position of the target in the indoor place cannot be accurately measured by the satellite positioning system. Therefore, the global positioning system is difficult to realize high-precision positioning in a complex indoor environment, a special method must be researched according to indoor application requirements, and development of an indoor positioning method with low economic cost, high positioning precision and good real-time performance becomes one of current research hotspots.
Currently, the mainstream indoor positioning technologies and methods include the following:
(1) the method based on the inertial navigation technology comprises the following steps: the method has the advantages of good concealment, strong anti-interference performance, high output frequency and high short-term precision, but the influence of system accumulated errors on the positioning precision is large.
(2) The method based on the ultrasonic technology comprises the following steps: the method has the advantages of high positioning precision, relatively simple structure, extremely high possibility of being influenced by temperature change, limited action range, requirement of a large number of bottom hardware foundations and high development cost.
(3) The method based on optical technology comprises the following steps: the method has high positioning accuracy and simple structure, is only suitable for sight distance transmission, is easily interfered by fluorescence, sunlight and the like, and has higher requirement on application environment.
(4) The method based on the radio frequency/frequency modulation technology comprises the following steps: the label receiving signal by the method has small volume, low cost and convenient carrying, but basic equipment such as a reader and the like needs to be installed in a coverage area.
(5) The method based on the ultra-wideband technology comprises the following steps: the technology has the advantages of insensitivity to channel fading, high positioning accuracy, non-line-of-sight propagation, strong anti-interference capability, strong penetration capability and the like, but the system is expensive in cost and is not easy to popularize and apply.
(6) The method based on the Bluetooth technology comprises the following steps: because the bluetooth module is widely embedded into various terminal devices, the hardware deployment cost is low, but the positioning accuracy is not high, the positioning delay is large, and the transmission range is limited.
(7) The method based on the wireless local area network technology comprises the following steps: the method realizes positioning by utilizing the received signal strength information without adding additional equipment, has low deployment cost, but has limited position identification capability of signal strength and large same frequency and adjacent frequency interference.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art: for the practical application requirement based on location-based service, in view of the limitations of the prior art and uncertainty of indoor environment, some challenges still face to real-time and high-precision indoor positioning, which is a technical problem to be solved urgently by technical personnel in the field and still needs further intensive research.
Disclosure of Invention
The embodiment of the invention provides an indoor positioning method and device of a mobile terminal, which can realize higher positioning precision with lower cost.
In one aspect, an embodiment of the present invention provides an indoor positioning method for a mobile terminal, where the method includes:
acquiring signal intensity information corresponding to a plurality of preset sampling points in a space position in an off-line manner to serve as fingerprint point signal information;
constructing a position fingerprint database according to the fingerprint point signal information;
acquiring initial state information of displacement motion of the mobile terminal within current preset unit time;
calculating a reference position of a displacement ending position by utilizing the displacement motion starting state information of the mobile terminal and utilizing an inertial navigation principle;
and acquiring signal intensity information respectively corresponding to preset unit time points on a displacement motion trail of the mobile terminal, constructing a sparse fingerprint positioning model based on space position constraint, correcting the reference position of the displacement termination position, and acquiring the corrected position of the displacement termination position.
In another aspect, an embodiment of the present invention provides an indoor positioning device for a mobile terminal, where the device includes:
the fingerprint point signal information acquisition unit is used for acquiring signal intensity information corresponding to a plurality of preset sampling points in a space position in an off-line mode to serve as fingerprint point signal information;
the position fingerprint database construction unit is used for constructing a position fingerprint database according to the fingerprint point signal information;
a displacement motion starting state information obtaining unit, configured to obtain displacement motion starting state information of the mobile terminal within a current preset unit time;
the inertial navigation principle calculating unit is used for calculating the reference position of the displacement ending position by utilizing the inertial navigation principle and utilizing the displacement motion starting state information of the mobile terminal;
and the model construction unit is used for acquiring signal intensity information respectively corresponding to preset unit time points on a displacement motion trail of the mobile terminal, constructing a sparse fingerprint positioning model based on space position constraint, correcting the reference position of the displacement termination position and acquiring the corrected position of the displacement termination position.
The technical scheme has the following beneficial effects: the signal intensity of the wireless local area network and the information of displacement, direction and the like of the moving target are effectively fused; in the aspect of positioning technology, a combined positioning mode combining a wireless local area network technology and an inertial navigation technology is adopted. The wireless local area network technology can eliminate the accumulated error of the inertial navigation system to a certain extent by analyzing and calculating the global signal intensity for positioning; the inertial navigation technology based on the local motion state of the inertial navigation system can reflect the motion state of an object in unit time, and can effectively restrict the influence of the changeability and fluctuation of wireless signals caused by factors such as global environment change on positioning. Therefore, the effective combination of the two technologies can fully exert respective advantages and cooperatively complete high-efficiency, reliable and high-precision positioning tasks.
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 of 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 flowchart of an indoor positioning method for a mobile terminal according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an indoor positioning device of a mobile terminal according to an embodiment of the present invention;
FIG. 3 is a general block diagram of an embodiment of an indoor positioning method according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating an overall indoor positioning method according to an embodiment of the present invention;
FIG. 5 is a flowchart of the location fingerprint database construction of an embodiment of the present invention;
FIG. 6 is a flowchart of sparse fingerprint location in accordance with an embodiment of the present invention;
FIG. 7 is a flow chart of the construction of a spatial location constraint model according to an embodiment of the present invention;
FIG. 8 is a flowchart of a sparse fingerprint location model solution based on spatial location constraints for an application example of the present invention;
FIG. 9 is a schematic diagram of an experimental path of an example of an application of the present invention;
FIG. 10 is a diagram showing experimental results of an application example of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a flowchart of a method for locating a mobile terminal indoors according to an embodiment of the present invention is shown, where the method includes:
101. acquiring signal intensity information corresponding to a plurality of preset sampling points in a space position in an off-line manner to serve as fingerprint point signal information;
102. constructing a position fingerprint database according to the fingerprint point signal information;
103. acquiring initial state information of displacement motion of the mobile terminal within current preset unit time;
104. calculating a reference position of a displacement ending position by utilizing the displacement motion starting state information of the mobile terminal and utilizing an inertial navigation principle;
105. and acquiring signal intensity information respectively corresponding to preset unit time points on a displacement motion trail of the mobile terminal, constructing a sparse fingerprint positioning model based on space position constraint, correcting the reference position of the displacement termination position, and acquiring the corrected position of the displacement termination position.
Preferably, a method of sampling and averaging multiple times at the same sampling point is adopted, and signal intensity information corresponding to multiple preset sampling points in a space position is acquired offline and used as fingerprint point signal information.
Preferably, the displacement motion start state information includes: start position, start velocity, acceleration, and angular velocity.
Preferably, after a sparse fingerprint positioning model based on space position constraint is constructed, an Alternative Direction Multiplier Method (ADMM) is adopted for solving, the reference position of the displacement termination position is corrected, and the corrected position of the displacement termination position is obtained.
Preferably, after constructing a sparse fingerprint positioning model based on spatial position constraint, solving by using an alternative direction multiplier method ADMM, specifically comprising:
after a sparse fingerprint positioning model based on space position constraint is constructed, the sparse fingerprint positioning model is transformed into an augmented Lagrange form according to signal reconstruction equality constraint in a Lagrange multiplier relaxation model, and model parameters are differentiated respectively to solve corresponding model parameters.
Corresponding to the above method embodiment, as shown in fig. 2, a schematic structural diagram of an indoor positioning apparatus of a mobile terminal according to an embodiment of the present invention is shown, where the apparatus includes:
a fingerprint point signal information collecting unit 21, configured to collect, offline, signal intensity information corresponding to each of a plurality of preset sampling points in a spatial position, as fingerprint point signal information;
a location fingerprint database construction unit 22, configured to construct a location fingerprint database according to the fingerprint point signal information;
a displacement motion starting state information obtaining unit 23, configured to obtain displacement motion starting state information of the mobile terminal in a current preset unit time;
an inertial navigation principle calculating unit 24, configured to calculate a reference position of a displacement ending position by using the inertial navigation principle and using the displacement motion starting state information of the mobile terminal;
and the model construction unit 25 is configured to acquire signal intensity information respectively corresponding to preset unit time points on a displacement motion trajectory of the mobile terminal, construct a sparse fingerprint positioning model based on spatial position constraint, correct the reference position of the displacement ending position, and acquire a corrected position of the displacement ending position.
Preferably, the fingerprint point signal information collecting unit 21 is specifically configured to collect, offline, signal intensity information corresponding to a plurality of preset sampling points in a space position as fingerprint point signal information by using a method of sampling and averaging the same sampling point for multiple times.
Preferably, the displacement motion start state information includes: start position, start velocity, acceleration, and angular velocity.
Preferably, the model constructing unit 25 is specifically configured to, after constructing a sparse fingerprint positioning model based on spatial position constraint, solve by using an alternative direction multiplier method ADMM, correct the reference position of the displacement ending position, and obtain a corrected position of the displacement ending position.
Preferably, the model constructing unit 25 is further specifically configured to construct a sparse fingerprint positioning model based on spatial position constraint, transform the sparse fingerprint positioning model into an augmented lagrangian form according to signal reconstruction equation constraint in a lagrangian multiplier relaxation model, respectively derive model parameters to solve corresponding model parameters, and correct the reference position of the displacement termination position to obtain a corrected position of the displacement termination position.
The technical scheme has the following beneficial effects: the signal intensity of the wireless local area network and the information of displacement, direction and the like of the moving target are effectively fused; in the aspect of positioning technology, a combined positioning mode combining a wireless local area network technology and an inertial navigation technology is adopted. The wireless local area network technology can eliminate the accumulated error of the inertial navigation system to a certain extent by analyzing and calculating the global signal intensity for positioning; the inertial navigation technology based on the local motion state of the inertial navigation system can reflect the motion state of an object in unit time, and can effectively restrict the influence of the changeability and fluctuation of wireless signals caused by factors such as global environment change on positioning. Therefore, the effective combination of the two technologies can fully exert respective advantages and cooperatively complete high-efficiency, reliable and high-precision positioning tasks.
The above technical solution of the embodiment of the present invention is explained in detail by the following application examples: in view of the limitations of the prior art and the uncertainty of the indoor environment, and the like, aiming at the real-time and high-precision indoor positioning requirement, as shown in fig. 3, a schematic diagram of a sparse fingerprint positioning overall framework based on space position constraint is provided as an application example of the present invention, as shown in fig. 4, a flowchart of an indoor positioning method is provided as an application example of the present invention, and the method comprises the following steps:
step 1: acquiring signal intensity information of each fingerprint point (sampling point) at a spatial position in an off-line manner;
step 2: processing fingerprint signal information, and constructing a position fingerprint database in an off-line manner;
and 3, step 3: setting/updating the initial position of the current displacement motion;
and 4, step 4: undergoing a displacement movement in a unit time;
and 5, step 5: acquiring displacement motion initial state information: start position, start velocity, acceleration, and angular velocity information;
and 6, step 6: preliminarily estimating the reference position of the displacement termination position according to the inertial navigation principle (Newton kinematics principle);
and 7, step 7: acquiring signal intensity information of a displacement motion termination position;
and 8, step 8: constructing a sparse fingerprint positioning model based on space position constraint;
step 9: solving a sparse fingerprint positioning model based on space position constraint, correcting the reference position of the displacement termination position, and acquiring the corrected position of the displacement termination position (as the initial position of the next displacement motion);
step 10: and continuing to test and turning to the step 3, otherwise, ending the process.
The following details:
1. location fingerprint library construction
In view of different distances, the wireless signal strength received at different positions has difference, the signal strength information of a specific position can be extracted, and a unique position fingerprint database is established by utilizing the correlation of the wireless signals and the position, so that the positioning can be carried out by utilizing the reference data of the position fingerprint database. The mobile terminal with wireless signal receiving software can be fully utilized to collect the wireless receiving signal intensity at each sampling point, and a position fingerprint construction database is constructed according to the wireless receiving signal intensity. As shown in fig. 5, a flow chart is constructed for the application example location fingerprint database of the present invention, and the specific construction process is as follows:
step 1: collecting signal strength information at each sampling point
At time t, at the ith sampling point position (x)i,yi) Collected signal strength information from various wireless Access Points (APs)
Can be expressed as
Wherein the content of the first and second substances,
and signal strength information from the jth AP collected at the ith sampling point position at the moment t.
Step 2: signal strength resampling process
In order to weaken the influence of external interference such as unstable wireless signals, indoor noise, multipath effect and the like, a method of sampling and averaging for multiple times can be adopted to offset the external interference to a certain extent, namely sampling is carried out at a sampling point i for k times, and the average value of the k times of sampling is taken as the position fingerprint S of the sampling pointiI.e. by
According to the multi-sampling strategy, the position fingerprint information of each sampling point can be collected and stored according to a certain rule, and then a position fingerprint database can be constructed.
And 3, step 3: location fingerprint data organization
Assuming that n APs and m sampling points (location fingerprint points) are arranged in the test environment, the constructed location fingerprint database Ψ can be represented as
2. Position fingerprint positioning method based on sparse signal representation
As shown in fig. 6, it is a flowchart of sparse fingerprint positioning in the application example of the present invention:
(1) sparse signal representation adaptability analysis
The sparse signal representation is an efficient high-dimensional signal acquisition, representation and compression method, and the theory has a great pushing effect on the traditional signal processing and the application thereof. If the high-dimensional signal essentially has a representation form of a natural sparse basis, the sparse representation form of the high-dimensional signal can be accurately calculated by convex optimization or greedy algorithm and the like. According to the organization form of the sparse basis, the sparse representation model can be divided into two categories of orthogonal basis sparse representation and redundant dictionary sparse representation.
The orthogonal basis sparse representation method fully utilizes the characteristic that non-sparse natural signals in a time domain can be converted into sparse signals through a certain domain transformation algorithm, and maps the natural signals to orthogonal transformation basis functions so as to obtain a sparse or approximately sparse projection transformation model. When the orthogonal basis functions can not carry out efficient sparse representation on the original signals, proper redundant functions can be selected to replace the orthogonal basis functions. The overcomplete redundant functions are also commonly referred to as redundant dictionaries (the elements of which are commonly referred to as dictionary atoms), which must conform to the characteristics and structure of the reconstructed signal. The sparse representation process of the original signal on the redundant dictionary is to search the redundant dictionary for the atomic term with the best match to the original signal.
The number of the fingerprint points of the position fingerprint database constructed in the above way is usually far greater than that of the APs, so that the fingerprint matrix has certain redundancy on column vectors; the number of the fingerprint points is usually far greater than that of the test points, so that the position fingerprint database is redundant to the test points; furthermore, the atomic signal and the test signal in the location fingerprint database are both derived from the same device, so that both have the same characteristics and structure. Therefore, the position fingerprint database can be used as a redundant dictionary of a sparse signal representation model to carry out sparse representation on the test signal.
(2) Sparse signal representation-based location fingerprint positioning model
Step 1: monitoring observed signals of a current location
In the test stage, an observation signal monitored by the mobile terminal at the time t is assumed to be StI.e. by
Wherein s isAPi,tIndicating the signal strength information received from the ith AP at time t.
Step 2: constructing a sparse signal representation model
Under the sparse representation model framework, for the observed signal StCan be converted to solve an optimization problem by
Wherein the content of the first and second substances,
for the optimal estimate of θ, Ψ is the training matrix (i.e., the location fingerprint database described by equation (4)), θ is the m-dimensional observed signal StM represents the number of location fingerprint points. According to the sparse representation theory, an observation signal S is assumedtSparse with respect to the training matrix Ψ, S can be represented by fewer non-0 coefficientst(i.e., only a few non-zero elements in θ, with all other elements being zero); further, the less non-zero elements in θ, StThe greater the sparsity with respect to Ψ (i.e., Ψ vs. S)tThe stronger the sparse representation capability). Considering that the actual signal strength can also be expressed as a linear combination of its neighboring signal strengths, it is introducedThe non-negativity constraint and the linear combination constraint of the sparse coefficient are included.
And 3, step 3: sparse model optimization
Said l in view of formula (6)0The norm model is difficult to directly solve, and according to the related research result of the sparse representation theory, the optimal solution is sufficiently sparse as shown in the formula I0The norm optimization problem can be approximately equivalent to l1Norm optimization problem, i.e.
By solving the above equation, an observed signal S can be obtainedtSparse representation coefficients on fingerprint redundancy dictionary Ψ
And 4, step 4: estimating a current observation position
On the basis, the position information corresponding to the fingerprint signal in the redundant dictionary can be fully utilized to estimate the observation signal StPosition of
Namely, it is
Wherein (x)i,yi) And tau is the space position of the fingerprint point i in the redundant dictionary, and tau is the sparse vector component threshold. The current observed signal is only correlated with fingerprint signals corresponding to sparse representation coefficients greater than τ. The position of the observation signal can be estimated through the formula, and further the position positioning is realized.
3. Sparse fingerprint positioning method based on space position constraint
FIG. 7 is a flow chart of the construction of the spatial position constraint model for the application example of the present invention.
(1) Spatial position constraint model
Positioning methods based on wireless signals have better performance and lower cost, but as wireless access points and access devices increase, the wireless transmission environment becomes more complex. Mutual interference between radio waves is generated, reliability of a dynamic environment is poor, wireless signals show high variability and complexity due to factors such as instantaneous jump and distortion, and positioning accuracy is affected. Aiming at the problem that the wireless signal is easy to be interfered to generate sudden change, if the observation signal can be restricted at a local space position, the interference of the external dynamic environment to the wireless signal can be restricted or counteracted to a certain extent.
The spatial position constraint mainly restricts the distribution state of the sparse vector theta in the sparse model described in the formula (7). In the framework of sparse signal representation, an observed signal is generally considered to be correlated only with its neighboring fingerprint signals, and not with its non-neighboring signals. Therefore, the spatial continuity of the sparse coefficient of the observation signal in the model can be restricted, namely, the fingerprint signal corresponding to the nonzero sparse coefficient is within the neighborhood range of the position of the observation signal. Thus, a spatial constraint vector v may be defined that reflects the spatial continuity described above, i.e.
ν＝[ν1,ν2,…,νm]T
Wherein (x)i,yi) Is the ith fingerprint point spatial position, O(x',y')Is the neighborhood of the position (x ', y ') to be estimated (which can be calculated from inertial sensor monitoring data according to newton's law of kinematics, the solution method is described in (2)).
A spatial position constraint model construction step:
the method comprises the following steps: having acquired initial velocity information (velocity of last displacement motion) of the current position
Step 1: undergoes a transient displacement motion;
step 2: monitoring acceleration and angular velocity information of a displacement motion initial state by means of an acceleration of an inertial navigation system and a gyroscope sensor;
and 3, step 3: calculating and updating the speed information of the current instantaneous movement (as the initial speed of the next displacement movement);
and 4, step 4: calculating the displacement of the current displacement motion;
and 5, step 5: transforming the inertial navigation coordinate system to a physical space coordinate system;
and 6, step 6: calculating the horizontal direction angle of the instant motion under a physical space coordinate system;
and 7, step 7: preliminarily estimating a termination position according to the instantaneous motion displacement and the horizontal direction angle;
and 8, step 8: setting a neighborhood range of the current observation position according to the estimated termination position;
step 9: constructing a spatial continuity constraint vector of the current observation position according to the correlation of the wireless signals;
step 10: and continuing to test and turning to the step 1, otherwise, ending the process.
(2) Sparse fingerprint positioning model based on space position constraint
After adding the spatial position constraint condition, the sparse representation model described in formula (7) can be modified into
Wherein | · | purple sweetFDenotes the Frobenius norm, λ1Is to balance the sparse term | | theta | | non-woven phosphor1And spatial position constraint term
The parameter (c) of (c). By solving the spatial position constraint sparse model (solving method (3)), the optimal estimation of the sparse coefficient can be obtained
And then, according to the formula (8), the current observation position can be calculated, namely, the spatial position estimation of one displacement motion is completed.
(3) Neighborhood O of position to be estimated(x',y')Solving method (formula (9))
Neighborhood O of position to be estimated(x',y')The method can be obtained by calculating the monitoring data of the inertial sensor according to Newton's law of kinematics, and the calculation method is as follows.
Assuming that the moving object moves from the initial position (x, y) through the inertial displacement of the unit time interval Δ t, the position to be estimated is (x ', y'). Within a short delta t time interval, the motion state of the object can be approximately considered to be uniform variable-speed linear motion, and according to the Newton's law of kinematics, the motion state can be obtained
v(t+Δt)＝v(t)+a(t)·Δt (11)
s(t+Δt)＝v(t+Δt)·Δt (12)
Wherein a (t) is the instantaneous acceleration at the moment t, and can be obtained by monitoring the acceleration sensor of the inertial navigation system in real time; v (t) is the instantaneous speed at the moment t, and is 0 during initial movement; v (t + Δ t) is the instantaneous speed at the time of t + Δ t, and can be updated step by step subsequently according to the formula (10); s (t + Δ t) is the instantaneous displacement of the moving object in the Δ t time interval, and since Δ t can be set to a short time interval, the motion state of the object in the Δ t time can be approximated to a uniform linear motion.
According to the displacement s (t + delta t) of unit time delta t and the instantaneous motion direction at the moment t, the space position (x ', y') of the moving object at the moment t + delta t can be preliminarily estimated, namely
Wherein alpha is the instantaneous horizontal motion direction angle at the time t under the physical coordinate system. The instantaneous motion direction of the object can be obtained by monitoring the gyroscope sensor of the inertial navigation system in real time, and then the instantaneous motion direction of the object can be converted into a physical coordinate system through coordinate system transformation, so that alpha can be calculated.
Through the formula (13), the position (x ', y') to be estimated, which is pre-estimated by the inertial navigation system, can be calculated, and further the neighborhood O of the position (x ', y') to be estimated, which is required by the formula (9), can be approximately determined(x',y')。
(4) Sparse fingerprint positioning model solving method (formula (10)) based on space position constraint
The optimization model described in equation (10) can be solved by using an Alternating Direction Multiplier Method (ADMM).
FIG. 8 is a flowchart illustrating the sparse fingerprint location model solving method based on spatial location constraint according to the embodiment of the present invention.
Step 1: according to Lagrange multiplier method, the signal reconstruction equation in the relaxation model constrains psi theta to StAnd adjusting the reconstruction error constraints into the optimization model objective function, i.e.
Wherein λ2Is a balance parameter of the signal reconstruction error term.
Step 2: further transforming the above form to an augmented Lagrange form
Let Z equal to theta, the above formula can be changed to
Where ρ (ρ > 0) is a penalty factor.
And 3, step 3: respectively deriving Z and theta of the above formula to obtain model parameters Z and theta
Step 1: derivative Z and solve Z
Step 2: derivative theta and solve for theta
According to the definition and the property of Frobenius norm and matrix trace, the above formula objective function can be transformed into
The above formula can be further transformed into a typical quadratic form, and then solved by using a quadratic model correlation method, namely
The application quantity of the invention aims at the actual application requirement of indoor position positioning, and deeply discusses the advantages and disadvantages of the inertial navigation technology and the wireless local area network technology in the aspect of positioning through earlier investigation and comparative research. In the aspect of positioning information, the method effectively fuses the signal intensity of the wireless local area network and the information of displacement, direction and the like of a moving target; in the aspect of positioning technology, a combined positioning mode combining a wireless local area network technology and an inertial navigation technology is adopted. The wireless local area network technology can eliminate the accumulated error of the inertial navigation system to a certain extent by analyzing and calculating the global signal intensity for positioning; the inertial navigation technology based on the local motion state of the inertial navigation system can reflect the motion state of an object in unit time, and can effectively restrict the influence of the changeability and fluctuation of wireless signals caused by factors such as global environment change on positioning. Therefore, the effective combination of the two technologies can fully exert respective advantages and cooperatively complete efficient and reliable positioning tasks.
The following analyses were performed by simulation of relevant materials:
1. FIG. 9 is a schematic diagram of an experimental path of an example of the present invention.
2. Results of the experiment
FIG. 10 is a schematic diagram showing the experimental results of an example of the present invention. The experiment compares the experimental results of an inertial navigation positioning model, a position fingerprint positioning model based on sparse signal representation (hereinafter referred to as a sparse fingerprint model) and a sparse fingerprint positioning model based on space position constraint (hereinafter referred to as a space constraint model).
3. Analysis of experiments
(1) In the aspect of positioning precision
For the 4 test paths, the inertial navigation positioning method obtains an average positioning error of about 1.9, the average positioning error of the position fingerprint positioning method based on sparse signal representation is about 1.2, the sparse fingerprint positioning method based on space position constraint obtains the best positioning effect, the average error is about 0.8, the positioning precision is greatly improved compared with other two methods, and then the feasibility and the effectiveness of the sparse fingerprint positioning model based on space position constraint are verified. Meanwhile, the spatial position constraint provided by inertial navigation is proved to play a great role in improving the performance of the position fingerprint positioning model based on sparse signal representation; on the other hand, the position fingerprint positioning method based on sparse signal representation also weakens the influence of accumulated errors on the inertial navigation system to a certain extent. Therefore, the integration application of the two methods improves the overall performance of the model algorithm and achieves better effect.
(2) In terms of experimental pathways
The inertial navigation positioning method, the sparse signal representation-based position fingerprint positioning method and the space position constraint-based sparse fingerprint positioning method all obtain the best positioning effect on a straight line path, the positioning error is increased on a rectangular and triangular 8-shaped path with 3 inflection points, and the positioning error is further increased on a rectangular 8-shaped path with 7 inflection points.
The above results indicate that the inflection points have certain influence on the positioning method, and the positioning errors of the three methods are gradually increased along with the increase of the number of the inflection points, which also provides new challenges for future research work. In addition, the path length also has a certain influence on the positioning method. Inertial navigation methods have increasingly larger positioning errors (from 1.6 to 2.1) with increasing path length, which also follows inertial navigation principles and mechanisms (errors accumulate gradually with run time). The other two methods are not greatly influenced by the path length, the positioning accuracy is influenced to a certain extent, but the error is not obviously increased, and the fact that the sparse fingerprint positioning model has certain robustness to the path length is also proved to a certain extent.
(3) In the aspect of the overall performance of the positioning method
The inertial navigation method has the advantages of simple positioning principle and small calculated amount, but has the worst positioning accuracy, and particularly, in view of the working principle and mechanism of the inertial navigation method, the positioning accuracy is increasingly influenced by accumulated errors along with the time. Therefore, the inertial navigation method needs to properly compensate or correct the accumulated error when applied.
The position fingerprint positioning method based on sparse signal representation is superior in performance on a straight path, although the path length and inflection points influence the algorithm precision to a certain extent, the overall error change is not obvious, and the performance of the method is relatively stable. However, the positioning error of tracking a specific position can be found out that a certain jump or distortion (especially at a position near an inflection point) is generated in a positioning result at some positions, the positioning error is increased to a certain extent, which indicates that the inflection point has a certain influence on the method, and the method can be properly compensated when being applied so as to improve the positioning accuracy.
The application example of the invention integrates the methods in a data layer by qualitatively and quantitatively analyzing the advantages and the disadvantages of the inertial navigation and the position fingerprint positioning method based on sparse signal representation, and designs a sparse fingerprint positioning model based on space position constraint. Experiment results show that the position fingerprint positioning method based on sparse signal representation can properly compensate the accumulated error of inertial navigation; the pre-estimation of the inertial navigation model on the motion rule also restricts the jumping and distortion effect of the position fingerprint positioning method based on sparse signal representation to a certain extent at a specific position. Therefore, compared with inertial navigation and sparse fingerprint positioning results, the proposed data layer fusion model (namely, the sparse fingerprint positioning method based on space position constraint) has obvious improvement effect on positioning precision and performance, the superiority of the fusion algorithm is further verified, and the fusion model is proved to be more robust to the path and more stable in performance.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. To those skilled in the art; various modifications to these embodiments will be readily apparent, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
Those of skill in the art will further appreciate that the various illustrative logical blocks, units, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, elements, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design requirements of the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present embodiments.
The various illustrative logical blocks, or elements, described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be located in a user terminal. In the alternative, the processor and the storage medium may reside in different components in a user terminal.
In one or more exemplary designs, the functions described above in connection with the embodiments of the invention may be implemented in hardware, software, firmware, or any combination of the three. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media that facilitate transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, such computer-readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store program code in the form of instructions or data structures and which can be read by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Additionally, any connection is properly termed a computer-readable medium, and, thus, is included if the software is transmitted from a website, server, or other remote source via a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wirelessly, e.g., infrared, radio, and microwave. Such discs (disk) and disks (disc) include compact disks, laser disks, optical disks, DVDs, floppy disks and blu-ray disks where disks usually reproduce data magnetically, while disks usually reproduce data optically with lasers. Combinations of the above may also be included in the computer-readable medium.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A method for locating a mobile terminal indoors, the method comprising:
acquiring signal intensity information corresponding to a plurality of preset sampling points in a space position in an off-line manner to serve as fingerprint point signal information;
constructing a position fingerprint database according to the fingerprint point signal information;
acquiring initial state information of displacement motion of the mobile terminal within current preset unit time;
calculating a reference position of a displacement ending position by utilizing the displacement motion starting state information of the mobile terminal and utilizing an inertial navigation principle;
acquiring signal intensity information respectively corresponding to preset unit time points on a displacement motion trail of the mobile terminal, constructing a sparse fingerprint positioning model based on space position constraint, correcting the reference position of the displacement termination position, and acquiring the corrected position of the displacement termination position;
after a sparse fingerprint positioning model based on space position constraint is constructed, solving by adopting an Alternative Direction Multiplier Method (ADMM), correcting the reference position of the displacement termination position, and acquiring the corrected position of the displacement termination position;
the constructing of the sparse fingerprint positioning model based on the spatial position constraint comprises the following steps:
constructing a sparse fingerprint positioning model according to a sparse algorithm;
according to monitoring data of an inertial navigation system, adding a space position constraint item in the sparse fingerprint positioning model, and constructing a sparse fingerprint positioning model based on space position constraint;
the displacement motion trajectory includes: a straight line track, or a rectangular track, or a triangular figure-8 track, or a rectangular figure-8 track.
2. The indoor positioning method of the mobile terminal according to claim 1, wherein signal intensity information corresponding to each of a plurality of predetermined sampling points in a spatial location is acquired offline as fingerprint point signal information by taking an average value of a plurality of samplings at the same sampling point.
3. The mobile terminal indoor positioning method according to claim 1, wherein the displacement motion start state information includes: start position, start velocity, acceleration, and angular velocity.
4. The indoor positioning method of the mobile terminal according to claim 1, wherein after constructing the sparse fingerprint positioning model based on the spatial position constraint, the solution is performed by using an alternative direction multiplier method ADMM, which specifically includes:
after a sparse fingerprint positioning model based on space position constraint is constructed, the sparse fingerprint positioning model is transformed into an augmented Lagrange form according to signal reconstruction equality constraint in a Lagrange multiplier relaxation model, and model parameters are differentiated respectively to solve corresponding model parameters.
5. An indoor positioning device for a mobile terminal, the device comprising:
the fingerprint point signal information acquisition unit is used for acquiring signal intensity information corresponding to a plurality of preset sampling points in a space position in an off-line mode to serve as fingerprint point signal information;
the position fingerprint database construction unit is used for constructing a position fingerprint database according to the fingerprint point signal information;
a displacement motion starting state information obtaining unit, configured to obtain displacement motion starting state information of the mobile terminal within a current preset unit time;
the inertial navigation principle calculating unit is used for calculating the reference position of the displacement ending position by utilizing the inertial navigation principle and utilizing the displacement motion starting state information of the mobile terminal;
the model construction unit is used for acquiring signal intensity information respectively corresponding to preset unit time points on a displacement motion trail of the mobile terminal, constructing a sparse fingerprint positioning model based on space position constraint, correcting the reference position of the displacement termination position and acquiring the corrected position of the displacement termination position;
the model construction unit is specifically configured to, after constructing a sparse fingerprint positioning model based on spatial position constraint, solve by using an Alternative Direction Multiplier Method (ADMM), correct the reference position of the displacement termination position, and acquire a corrected position of the displacement termination position;
the model building unit is further configured to: constructing a sparse fingerprint positioning model according to a sparse algorithm; according to monitoring data of an inertial navigation system, adding a space position constraint item in the sparse fingerprint positioning model, and constructing a sparse fingerprint positioning model based on space position constraint;
the displacement motion trajectory includes: a straight line track, or a rectangular track, or a triangular figure-8 track, or a rectangular figure-8 track.
6. The mobile terminal indoor positioning apparatus according to claim 5,
the fingerprint point signal information acquisition unit is specifically used for acquiring signal intensity information corresponding to a plurality of preset sampling points in a space position in an off-line mode as fingerprint point signal information by adopting a method of sampling the same sampling point for multiple times and taking an average value.
7. The mobile terminal indoor positioning apparatus according to claim 5, wherein the displacement motion start state information includes: start position, start velocity, acceleration, and angular velocity.
8. The mobile terminal indoor positioning apparatus according to claim 5,
the model construction unit is further specifically configured to construct a sparse fingerprint positioning model based on spatial position constraint, transform the sparse fingerprint positioning model into an augmented lagrangian form according to signal reconstruction equality constraint in a lagrangian multiplier relaxation model, respectively derive model parameters to solve corresponding model parameters, and correct the reference position of the displacement termination position to obtain a corrected position of the displacement termination position.
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| CN111065052B (en) * | 2019-12-26 | 2020-11-24 | 广东星舆科技有限公司 | Positioning track-based behavior analysis method and smart watch |
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