CN108668249B - Indoor positioning method and device for mobile terminal - Google Patents

Abstract

Embodiments of the present invention provide an indoor positioning method and device for a mobile terminal. The method includes: offline collection of signal strength information corresponding to a plurality of preset sampling points in a spatial position, as fingerprint point signal information; signal information to build a location fingerprint database; obtain the information on the starting state of the displacement movement within the current preset unit time of the mobile terminal; use the information on the starting state of the displacement movement of the mobile terminal to calculate the reference for the end position of the displacement using the principle of inertial navigation Obtain the signal strength information corresponding to the preset unit time points of the mobile terminal on the displacement motion trajectory, construct a sparse fingerprint positioning model based on spatial position constraints, correct the reference position of the displacement termination position, and obtain the displacement termination position. The corrected position of the position. The embodiments of the present invention achieve higher positioning accuracy at lower cost.

Description

A kind of indoor positioning method and device of mobile terminal

technical field

The present invention relates to the field of mobile Internet technologies, and in particular, to a method and device for indoor positioning of a mobile terminal.

Background technique

With the rapid development of computer software and hardware technology, the rapid popularization of wireless networks, and the wide application of mobile intelligent terminal equipment, the mobile Internet has been widely used, and the application demand for location-based services has shown a rapid and substantial growth trend. Because it can provide accurate positioning information for target positioning, emergency rescue, traffic management, etc., it is gradually applied to various fields of social production and life, and shows good technical development prospects and huge application market space. Reliable and efficient positioning technology is the premise and key to the realization of location-based services.

Tool software and mobile services based on mobile platforms have emerged like spring after rain, providing a wealth of services for users in various industries and the general public. The application of global satellite positioning system has built a bridge between human activities and geographical location; the sensor network based on mobile Internet has realized the dream of efficient communication between people and things; the popularization of wireless local area network (WLAN) has solved the problem of The regionalized interaction problem of massive information realizes the terminal closed loop of the globalized mobile Internet. The above-mentioned mobile tools and services are all in a brand-new mode, promoting the improvement of human social productivity, subverting people's traditional way of life, and promoting the wave of mobile informatization. However, before sketching out a beautiful blueprint for the mobile Internet to change the world, some technical bottlenecks that restrict the further development of mobile Internet application services must be broken through, and high-precision indoor positioning technology is one of them. In the mobile Internet, location-based services are one of the most frequently used and widely used services, and many other mobile applications use location-based services directly or indirectly.

In an open outdoor environment, with the help of global satellite positioning systems (such as GPS in the United States, GLONASS in Russia, Galileo in Europe, Beidou in China, etc.), location-based services have been able to provide users with high-precision and high-stability location services. Applications have penetrated into various industries and the mass market. However, in indoor places with more human activities, the received satellite signals are often distorted due to factors such as building blockage, signal attenuation, complex wireless propagation environment, and lack of line-of-sight transmission between satellites and receivers. The application of the positioning method in the indoor environment is greatly restricted, and the exact position of the target in the indoor place cannot be accurately measured by the satellite positioning system. Therefore, it is difficult for global satellite positioning systems to achieve high-precision positioning in complex indoor environments. Special methods must be studied for indoor application requirements. The development of indoor positioning methods with low economic cost, high positioning accuracy and good real-time performance has become one of the current research hotspots .

The current mainstream indoor positioning technologies and methods are as follows:

(1) Method based on inertial navigation technology: This method has good concealment, strong anti-interference, high output frequency, and high short-term accuracy, but the cumulative error of the system has a great influence on the positioning accuracy.

(2) Method based on ultrasonic technology: This method has high positioning accuracy and relatively simple structure, but is easily affected by temperature changes, has a limited scope of action, and requires a large amount of underlying hardware foundation, resulting in high development costs.

(3) Method based on light technology: This method has high positioning accuracy and simple structure, but it is only suitable for line-of-sight propagation, and is easily interfered by fluorescence, sunlight, etc., and has high requirements on the application environment.

(4) Method based on radio frequency/frequency modulation technology: In this method, the tag receiving the signal is small in size, low in cost, and convenient to carry, but basic equipment such as a reader needs to be installed in the coverage area.

(5) Method based on ultra-wideband technology: This technology has the advantages of insensitivity to channel fading, high positioning accuracy, non-line-of-sight propagation, strong anti-interference ability, and strong penetration ability, but the system is expensive and difficult to popularize and apply.

(6) Method based on Bluetooth technology: Since the Bluetooth module has been widely embedded in various terminal devices, its hardware deployment cost is low, but the positioning accuracy is not high, the positioning delay is large, and the transmission range is limited.

(7) Method based on wireless local area network technology: This method uses the received signal strength information to achieve positioning without adding additional equipment, and the deployment cost is low.

In the process of realizing the present invention, the inventor found that there are at least the following problems in the prior art: for the practical application requirements of location-based services, in view of the limitations of the prior art and the uncertainty of the indoor environment and other factors, real-time, high-precision indoor positioning There are still some challenges, which is a technical problem to be solved urgently by those skilled in the art, and further in-depth research is required.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide an indoor positioning method and device for a mobile terminal, which can achieve higher positioning accuracy at a lower cost.

On the one hand, an embodiment of the present invention provides an indoor positioning method for a mobile terminal, the method includes:

Offline collection of signal strength information corresponding to a plurality of preset sampling points in a spatial position, respectively, as fingerprint point signal information;

constructing a location fingerprint database according to the signal information of the fingerprint points;

Acquiring the initial state information of the displacement movement within the current preset unit time of the mobile terminal;

Using the displacement motion starting state information of the mobile terminal, the inertial navigation principle is used to calculate the reference position of the displacement termination position;

Obtain the signal strength information corresponding to the preset unit time points of the mobile terminal on the displacement motion trajectory, construct a sparse fingerprint positioning model based on spatial position constraints, correct the reference position of the displacement termination position, and obtain the displacement termination position. Correct position.

On the other hand, an embodiment of the present invention provides an indoor positioning device for a mobile terminal, and the device includes:

a fingerprint point signal information collection unit, used for offline collection of signal strength information corresponding to a plurality of preset sampling points in a spatial position, as fingerprint point signal information;

a location fingerprint database construction unit, configured to construct a location fingerprint database according to the signal information of the fingerprint points;

a displacement motion initial state information acquisition unit, configured to acquire the displacement motion initial state information within the current preset unit time of the mobile terminal;

an inertial navigation principle calculation unit, used for calculating the reference position of the displacement termination position by using the inertial navigation principle by using the displacement motion starting state information of the mobile terminal;

A model building unit, configured to obtain the signal strength information corresponding to the preset unit time points of the mobile terminal on the displacement motion trajectory, construct a sparse fingerprint positioning model based on spatial position constraints, and correct the reference position of the displacement termination position , to obtain the corrected position of the displacement end position.

The above technical solution has the following beneficial effects: the signal strength of the wireless local area network and the information such as the displacement and direction of the moving target are effectively integrated; in terms of positioning technology, a joint positioning method combining the wireless local area network technology and the 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 strength for positioning; and the inertial navigation technology based on its own local motion state can reflect the motion state of the object in unit time. , which can effectively restrict the influence of the changeable and fluctuating wireless signals on the positioning caused by factors such as global environmental changes. Therefore, the effective combination of the above two technologies can give full play to their respective advantages and cooperate to complete efficient and reliable high-precision positioning tasks.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

1 is a flowchart of an indoor positioning method for a mobile terminal according to an embodiment of the present invention;

2 is a schematic structural diagram of an indoor positioning device for a mobile terminal according to an embodiment of the present invention;

3 is an overall frame diagram of an embodiment of an indoor positioning method in an application example of the present invention;

4 is an overall flow chart of an indoor positioning method of an application example of the present invention;

Fig. 5 is the construction flow chart of the location fingerprint database of the application example of the present invention;

6 is a flow chart of sparse fingerprint positioning of an application example of the present invention;

Fig. 7 is the flow chart of constructing the spatial position constraint model of the application example of the present invention;

FIG. 8 is a flow chart for solving a sparse fingerprint positioning model based on spatial position constraints in an application example of the present invention;

Fig. 9 is the schematic diagram of the experimental route of the application example of the present invention;

FIG. 10 is a schematic diagram of the experimental results of an application example of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

As shown in FIG. 1, it is a flowchart of an indoor positioning method of a mobile terminal according to an embodiment of the present invention, and the method includes:

101. Collect signal strength information corresponding to a plurality of preset sampling points in a spatial position offline, as fingerprint point signal information;

102. Build a location fingerprint database according to the fingerprint point signal information;

103. Acquire the information on the starting state of the displacement movement within the current preset unit time of the mobile terminal;

104. Using the displacement motion start state information of the mobile terminal, and using the inertial navigation principle to calculate the reference position of the displacement termination position;

105. Acquire the signal strength information corresponding to the preset unit time points of the mobile terminal on the displacement motion trajectory, construct a sparse fingerprint positioning model based on spatial position constraints, correct the reference position of the displacement termination position, and obtain the displacement termination position. The corrected position of the position.

Preferably, the method of taking the average value of multiple samples at the same sampling point is used to collect offline signal strength information corresponding to a plurality of preset sampling points in a spatial position as the fingerprint point signal information.

Preferably, the initial state information of the displacement movement includes: initial position, initial velocity, acceleration and angular velocity.

Preferably, after the sparse fingerprint positioning model based on spatial position constraints is constructed, the alternating direction multiplier method ADMM is used to solve the problem, and the reference position of the displacement termination position is modified to obtain the modified position of the displacement termination position.

Preferably, after constructing a sparse fingerprint positioning model based on spatial position constraints, the alternating direction multiplier method ADMM is used to solve the problem, which specifically includes:

After constructing a sparse fingerprint localization model based on spatial position constraints, according to the signal reconstruction equation constraints in the Lagrangian multiplier relaxation model, it is transformed into an augmented Lagrangian form, and the model parameters are derived separately to solve corresponding model parameters.

Corresponding to the above method embodiment, as shown in FIG. 2 , it is a schematic structural diagram of an indoor positioning apparatus for a mobile terminal according to an embodiment of the present invention, and the apparatus includes:

The fingerprint point signal information collection unit 21 is used for offline collection of signal strength information corresponding to 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 initial state information acquisition unit 23, configured to acquire the displacement motion initial state information within the current preset unit time of the mobile terminal;

The inertial navigation principle calculation unit 24 is used to calculate the reference position of the displacement termination position by using the inertial navigation principle by using the displacement motion starting state information of the mobile terminal;

The model building unit 25 is used to obtain the signal strength information corresponding to the preset unit time points of the mobile terminal on the displacement motion trajectory, construct a sparse fingerprint positioning model based on spatial position constraints, and perform a reference position on the displacement termination position. Correction to obtain the corrected position of the displacement end position.

Preferably, the fingerprint point signal information collection unit 21 is specifically configured to collect the signal strength information corresponding to a plurality of preset sampling points in a spatial position offline by adopting the method of sampling multiple times at the same sampling point to obtain an average value, as the fingerprint point signal information.

Preferably, the initial state information of the displacement movement includes: initial position, initial velocity, acceleration and angular velocity.

Preferably, the model construction unit 25 is specifically configured to, after constructing a sparse fingerprint positioning model based on spatial position constraints, use the alternating direction multiplier method ADMM to solve the problem, correct the reference position of the displacement termination position, and obtain the displacement termination position. The corrected position of the position.

Preferably, the model building unit 25 is further configured to, after building a sparse fingerprint positioning model based on spatial position constraints, transform it into an augmented stretched model according to the Lagrange multiplier method relaxation model of the signal reconstruction equation constraints In Grangian form, the model parameters are respectively derived to solve the corresponding model parameters, and the reference position of the displacement termination position is modified to obtain the corrected position of the displacement termination position.

The above technical solution has the following beneficial effects: the signal strength of the wireless local area network and the information such as the displacement and direction of the moving target are effectively integrated; in terms of positioning technology, a joint positioning method combining the wireless local area network technology and the 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 strength for positioning; and the inertial navigation technology based on its own local motion state can reflect the motion state of the object in unit time. , which can effectively restrict the influence of the changeable and fluctuating wireless signals on the positioning caused by factors such as global environmental changes. Therefore, the effective combination of the above two technologies can give full play to their respective advantages and cooperate to complete efficient and reliable high-precision positioning tasks.

The above technical solutions of the embodiments of the present invention will be described in detail below through application examples: in view of the limitations of the prior art and the uncertainty of the indoor environment and other factors, for real-time, high-precision indoor positioning requirements, as shown in FIG. 3, the application of the present invention Example A schematic diagram of the overall framework of sparse fingerprint positioning based on spatial position constraints, as shown in Figure 4, is an overall flow chart of an indoor positioning method of an application example of the present invention, and the method steps of a sparse fingerprint positioning method based on spatial position constraints:

Step 1: Collect the signal strength information of each fingerprint point (sampling point) in the spatial position offline;

Step 2: Fingerprint signal information processing, offline construction of location fingerprint database;

Step 3: Set/update the initial position of this displacement movement;

Step 4: Experiencing a displacement movement per unit time;

Step 5: Obtain the initial state information of displacement motion: initial position, initial velocity, acceleration and angular velocity information;

Step 6: Preliminarily estimate the reference position of the displacement termination position according to the principle of inertial navigation (the principle of Newton's kinematics);

Step 7: Obtain the signal strength information of the position where the displacement motion ends.

Step 8: Build a sparse fingerprint localization model based on spatial location constraints;

Step 9: Solve the sparse fingerprint positioning model based on spatial position constraints, correct the reference position of the displacement termination position, and obtain the corrected position of the displacement termination position (as the starting position of the next displacement movement);

Step 10: Continue the test and go to Step 3, otherwise end the process.

Details are as follows:

1. Location fingerprint library construction

In view of the different distances, the received wireless signal strength at different locations is different, the signal strength information of a specific location can be extracted, and the correlation between the wireless signal and the location can be used to establish a unique location fingerprint database, so that the location can be used. The reference data of the fingerprint database is used for positioning. You can make full use of the mobile terminal installed with the wireless signal receiving software, collect the wireless receiving signal strength at each sampling point, and use this to build a location fingerprint to build a database. As shown in Figure 5, it is a flow chart for the construction of the location fingerprint database of the application example of the present invention, and the specific construction process is as follows:

Step 1: Collect signal strength information at each sampling point

可表示为At time t, the signal strength information from each wireless access point (AP) collected at the i-th sampling point position (x _i , y _i )

can be expressed as

为t时刻在第i个采样点位置采集到的来自第j个AP的信号强度信息。in,

is the signal strength information from the j-th AP collected at the i-th sampling point at time t.

Step 2: Signal Strength Resampling Processing

In order to weaken the influence of the unstable wireless signal and external interference such as indoor noise and multipath effect, the method of taking the average value of multiple sampling can be used to offset the external interference to a certain extent, that is, sampling k times at the sampling point i, and k times. The sampling mean is used as the location fingerprint S ⁱ of the sampling point, that is,

According to the above multiple sampling strategy, the location fingerprint information of each sampling point can be collected and stored according to a certain rule to build a location fingerprint database.

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 expressed as

2. Location fingerprinting method based on sparse signal representation

As shown in Figure 6, it is a flow chart of sparse fingerprint positioning for an application example of the present invention:

(1) Adaptive Analysis of Sparse Signal Representation

Sparse signal representation is an efficient high-dimensional signal acquisition, representation and compression method, which greatly promotes traditional signal processing and its applications. If the high-dimensional signal essentially has a representation of a natural sparse basis, algorithms such as convex optimization or greedy can be used to accurately calculate the sparse representation of the high-dimensional signal. According to the organization form of sparse base, sparse representation models can be divided into two categories: orthogonal base sparse representation and redundant dictionary sparse representation.

The orthogonal basis sparse representation method makes full use of the characteristics that non-sparse natural signals in the time domain can be converted into sparse signals through a certain domain transformation algorithm, and maps the natural signals to the orthogonal transform basis function, and then obtains sparse or approximately sparse projection transformation. Model. When the orthonormal basis function cannot efficiently represent the original signal sparsely, an appropriate redundant function can be selected to replace the above-mentioned orthonormal basis function. The overcomplete redundancy function is usually called a redundancy dictionary (its elements are usually called dictionary atoms), and the redundancy dictionary 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 for the atomic item that has the best match with the original signal from the redundant dictionary.

The number of fingerprint points in the location fingerprint database constructed above is usually much larger than the number of APs, so the fingerprint matrix has a certain redundancy in the column vector; the number of fingerprint points is usually much larger than the number of test points, so the location fingerprint database The test points are also redundant; moreover, the atomic signals and the test signals in the location fingerprint database originate from the same equipment, so they have the same characteristics and structure. Therefore, the above-mentioned location fingerprint database can be used as a redundant dictionary of the sparse signal representation model to sparsely represent the test signal.

(2) Position fingerprint positioning model based on sparse signal representation

Step 1: Monitor the observation signal at the current location

In the testing phase, it is assumed that the observed signal monitored by the mobile terminal at time t is S _t , that is,

Among them, s _APi,t represents the signal strength information received from the i-th AP at time t.

Step 2: Build a sparse signal representation model

Under the framework of the sparse representation model, the position estimation task for the observed signal S _t can be transformed into solving the following optimization problem:

为θ的最优估计，Ψ为训练矩阵(即公式(4)所描述的位置指纹数据库)，θ是一个m维的观测信号S_t的稀疏系数向量，m表示位置指纹点数量。根据稀疏表示理论，假设观测信号S_t相对于训练矩阵Ψ是稀疏的，则可用较少的非0系数表示S_t(即θ中仅有少量非零元素，其它元素均为零)；而且，θ中的非零元素越少，S_t相对于Ψ的稀疏程度就越高(即Ψ对S_t的稀疏表示能力就越强)。考虑到实际信号强度亦可表示为其邻域信号强度的线性组合，故引入了上述稀疏系数的非负性约束和线性组合约束。in,

is the optimal estimation of θ, Ψ is the training matrix (that is, the location fingerprint database described in formula (4)), θ is a sparse coefficient vector of an m-dimensional observation signal S _t , and m represents the number of location fingerprint points. According to the sparse representation theory, assuming that the observed signal S _t is sparse relative to the training matrix Ψ, S _t can be represented by fewer non-zero coefficients (that is, there are only a few non-zero elements in θ, and other elements are zero); and, The fewer non-zero elements in θ, the more sparse S _t is relative to Ψ (that is, the more sparsely represented by Ψ to S _t ). Considering that the actual signal strength can also be expressed as a linear combination of its neighborhood signal strengths, the above-mentioned non-negativity constraints and linear combination constraints of the sparse coefficients are introduced.

Step 3: Sparse Model Optimization

Since the _l0 -norm model described in formula (6) is difficult to solve directly, according to the relevant research results of the sparse representation theory, if the optimal solution of the above formula is sufficiently sparse, the _l0 -norm optimization problem described in it can be approximately equivalent to l ₁ -norm optimization problem, i.e.

By solving the above formula, the sparse representation coefficient of the observation signal S _t on the fingerprint redundancy dictionary Ψ can be obtained

Step 4: Estimate the current observation location

即On this basis, the position information corresponding to the fingerprint signal in the redundant dictionary can be fully utilized to estimate the position of the observed signal S _t

which is

Among them, (x _i , y _i ) is the spatial position of the fingerprint point i in the redundant dictionary, and τ is the sparse vector component threshold. The current observation signal is only correlated with fingerprint signals corresponding to sparse representation coefficients greater than τ. The position of the observed signal can be estimated by the above formula, and then the position positioning can be realized.

3. Sparse fingerprint localization method based on spatial location constraints

As shown in FIG. 7 , it is a flow chart for constructing a spatial position constraint model of an application instance of the present invention.

(1) Spatial position constraint model

The wireless signal-based positioning method has better performance and lower cost, but with the increase of wireless access points and access devices, the wireless transmission environment becomes more and more complicated. Mutual interference between radio waves is bound to occur, which makes the reliability of the dynamic environment worse, and causes the wireless signal to show high variability and complexity due to instantaneous jumps, distortions and other factors, which in turn affects the positioning accuracy. Aiming at the problem that the wireless signal is susceptible to interference and abrupt change, if the observation signal can be constrained in the local space position, the interference of the external dynamic environment to the wireless signal can be restricted or cancelled to a certain extent.

The spatial position constraint mainly restricts the distribution state of the sparse vector θ in the sparse model described in formula (7). In the framework of sparse signal representation, it is generally considered that the observed signal is only related to its adjacent fingerprint signals, and has nothing to do with its non-adjacent signals. Therefore, it can be used to constrain the spatial continuity of the sparse coefficients of the observation signal in the model, that is, the fingerprint signal corresponding to the non-zero sparse coefficient should be in the neighborhood of the position of the observation signal. Therefore, the space constraint vector ν that reflects the above-mentioned space continuity can be defined, namely

ν=[ν ₁ ,ν ₂ ,…,ν _m ] ^T

Among them, (x _i , y _i ) is the spatial position of the ith fingerprint point, and O _{(x', y')} is the neighborhood of the position to be estimated (x', y') (according to Newton's law of kinematics, the inertial The sensor monitoring data is calculated, and the solution method is shown in the following (2)).

Steps of constructing the spatial position constraint model:

Premise: The initial speed information of the current position (the speed of the last displacement movement) has been obtained

Step 1: Experiencing an instantaneous displacement movement;

Step 2: Use the acceleration and gyroscope sensors of the inertial navigation system to monitor the acceleration and angular velocity information of the initial state of the displacement motion;

Step 3: Calculate and update the speed information of this instantaneous movement (as the initial speed of the next displacement movement);

Step 4: Calculate the displacement of this displacement movement;

Step 5: Transform the inertial navigation coordinate system to the physical space coordinate system;

Step 6: Calculate the horizontal direction angle of this instantaneous movement in the physical space coordinate system;

Step 7: Preliminarily estimate the termination position according to the instantaneous movement displacement and horizontal direction angle;

Step 8: According to the estimated termination position, set the neighborhood range of the current observation position;

Step 9: According to the wireless signal correlation, construct the spatial continuity constraint vector of the current observation position;

Step 10: Continue the test and go to Step 1, otherwise end the process.

(2) Sparse fingerprint localization model based on spatial location constraints

After adding spatial location constraints, the sparse representation model described in formula (7) can be modified as

的参数。通过求解上述空间位置约束稀疏模型(求解方法见后(3)),可得出稀疏系数的最优估计

进而根据公式(8)可计算得出当前观测位置，即完成一次位移运动的空间位置估计。where ||·|| _F represents the Frobenius norm, λ ₁ is the balance sparse term || θ|| ₁ and the spatial position constraint term

parameter. By solving the above-mentioned spatial position-constrained sparse model (see (3) for the solution method), the optimal estimate of the sparse coefficient can be obtained

Then, the current observation position can be calculated according to formula (8), that is, the spatial position estimation of a displacement motion is completed.

(3) The solution method of the location neighborhood to be estimated O _{(x', y')} (formula (9))

The position neighborhood O _{(x', y')} to be estimated can be calculated from the monitoring data of the inertial sensor according to Newton's laws of kinematics. The calculation method is as follows.

Assuming that the moving object moves from the initial position (x, y) and undergoes inertial displacement movement per unit time interval Δt, the position to be estimated is (x', y'). In the short time interval of Δt, the motion state of the object can be approximated as a uniformly variable linear motion. According to Newton's law of kinematics, it can be obtained that

v(t+Δt)=v(t)+a(t)·Δt (11)

s(t+Δt)=v(t+Δt)·Δt (12)

Among them, a(t) is the instantaneous acceleration at time t, which can be obtained by real-time monitoring of the inertial navigation system acceleration sensor; v(t) is the instantaneous speed at time t, which is 0 during initial motion; v(t+Δt) is t+Δt The instantaneous speed at the moment can be updated gradually according to formula (10); s(t+Δt) is the instantaneous displacement of the moving object within the Δt time interval. Since Δt can be set as a short time interval, the movement of the object within the Δt time interval The state can be approximated as a uniform linear motion.

According to the displacement s(t+Δt) per unit time Δt and the instantaneous motion direction at time t, the spatial position (x', y') of the moving object at time t+Δt can be preliminarily estimated, that is,

Among them, α is the direction angle of the instantaneous horizontal movement at time t in the physical coordinate system. The instantaneous movement direction of the object can be obtained by real-time monitoring of the gyroscope sensor of the inertial navigation system, and then it can be converted into the physical coordinate system through the coordinate system transformation, and then α can be calculated.

By formula (13), the position to be estimated (x', y') pre-estimated by the inertial navigation system can be calculated, and then the neighbors of the position to be estimated (x', y') required by formula (9) can be approximately determined The field O _(x',y') .

(4) Solving method of sparse fingerprint localization model based on spatial location constraints (formula (10))

The optimization model described in formula (10) can be solved by the alternating direction multiplier method (ADMM).

As shown in FIG. 8 , it is a flow chart for solving the sparse fingerprint positioning model based on spatial position constraints in an application example of the present invention.

Step 1: According to the Lagrange multiplier method, the signal reconstruction equation in the relaxation model is constrained Ψθ=S _t , and the reconstruction error constraint is adjusted to the objective function of the optimization model, namely

where _λ2 is the balance parameter of the signal reconstruction error term.

Step 2: Further transform the above formula into augmented Lagrangian form

Let Z=θ, then the above formula can be transformed into

where ρ(ρ>0) is the penalty factor.

Step 3: Differentiate Z and θ of the above formula respectively, and then the model parameters Z and θ can be solved

Step 1: Derive Z and solve for Z

Step 2: Derive θ and solve θ

According to the definition and properties of Frobenius norm and matrix trace, the objective function above can be transformed into

The above formula can be further transformed into a typical quadratic form, and then the quadratic model correlation method can be used to solve it, namely

The above application number of the present invention is aimed at the practical application requirements of indoor location positioning. Through preliminary investigation and comparative research, the advantages and disadvantages of inertial navigation technology and wireless local area network technology in positioning are deeply discussed. And wireless local area network technology multi-source information fusion positioning method, this method has carried out in-depth research in positioning information analysis, information collection, information fusion, model construction and solution. In the aspect of positioning information, the method in this paper effectively integrates the signal strength of the wireless local area network and the displacement and direction of the moving target. 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 strength for positioning; and the inertial navigation technology based on its own local motion state can reflect the motion state of the object in unit time. , which can effectively restrict the influence of the changeable and fluctuating wireless signals on the positioning caused by factors such as global environmental changes. Therefore, the effective combination of the above two technologies can give full play to their respective advantages and cooperate to complete efficient and reliable positioning tasks.

The following materials are analyzed through simulation experiments:

1. As shown in FIG. 9, it is a schematic diagram of the experimental route of the application example of the present invention.

2. Experimental results

As shown in FIG. 10, it is a schematic diagram of the experimental result of the application example of the present invention. The experiment compares the experimental results of the inertial navigation positioning model, the position fingerprint positioning model based on sparse signal representation (hereinafter referred to as the sparse fingerprint model) and the sparse fingerprint positioning model based on spatial position constraints (hereinafter referred to as the spatial constraint model).

3. Experimental analysis

(1) In terms of positioning accuracy

For the above four test paths, the inertial navigation positioning method obtained an average positioning error of about 1.9, the average positioning error of the position fingerprint positioning method based on sparse signal representation was about 1.2, and the sparse fingerprint positioning method based on spatial position constraints achieved the best results. The average error is about 0.8, and the positioning accuracy is greatly improved compared with the other two methods, which further verifies the feasibility and effectiveness of the sparse fingerprint positioning model based on spatial position constraints. At the same time, it is also proved that the spatial location constraints provided by inertial navigation greatly improve the performance of the location fingerprint location model based on sparse signal representation; on the other hand, the location fingerprint location method based on sparse signal representation also weakens the accumulation to a certain extent The effect of errors on inertial navigation systems. Therefore, the fusion application of the two improves the overall performance of the model algorithm and achieves good results.

(2) In terms of experimental paths

The length of the straight path is 20, with no inflection points; the length of the rectangular path is 40, including 3 inflection points; the length of the triangular 8-character path is about 48, including 3 inflection points; the length of the rectangular 8-character path is 60, including 7 inflection points.

The inertial navigation positioning method, the position fingerprint positioning method based on sparse signal representation, and the sparse fingerprint positioning method based on spatial position constraints all achieved the best positioning effect on the straight path, and the rectangular and triangular 8-character paths with three inflection points. The positioning error increases, and the positioning error further increases on the rectangular figure-of-8 path with 7 inflection points.

The above results show that the inflection point has a certain influence on the positioning method, and the positioning errors of the three methods gradually increase with the increase of the number of inflection points, which also poses new challenges for future research work. In addition, the path length also has a certain influence on the positioning method. In the inertial navigation method, as the path length increases, the positioning error gradually increases (from 1.6 to 2.1), which is also in line with the inertial navigation principle and mechanism (the error gradually accumulates with the running time). The other two methods are not greatly affected by the path length, and although the positioning accuracy is affected to a certain extent, the error increase is not obvious, which also proves that the sparse fingerprint positioning model has a certain robustness to the path length.

(3) In terms of the overall performance of the positioning method

The positioning principle of the inertial navigation method is simple and the amount of calculation is small, but the positioning accuracy is the worst. Especially in view of its working principle and mechanism, the cumulative error over time has an increasingly strong influence on its positioning accuracy. Therefore, the inertial navigation method must be properly compensated or corrected for its accumulated error when it is applied.

The location fingerprint positioning method based on sparse signal representation is superior on straight paths. Although the path length and inflection point affect the accuracy of the algorithm to a certain extent, the overall error does not change significantly, indicating that the performance of the method is relatively stable. However, by tracking the positioning error of a specific position, it can be found that the positioning results at some positions will produce a certain jump or distortion (especially near the inflection point), and the positioning error will increase to a certain extent, indicating that the inflection point has a certain influence on the method. Appropriate compensation to improve positioning accuracy.

The application example of the present invention integrates the above methods at the data layer by qualitatively and quantitatively analyzing the advantages and disadvantages of inertial navigation and location fingerprints based on sparse signal representation, and designs a sparse fingerprint location model based on spatial location constraints. The experimental results show that the position fingerprint positioning method based on the sparse signal representation can properly compensate the accumulated error of the inertial navigation; the pre-estimation of the motion law by the inertial navigation model also restricts the position fingerprint positioning method based on the sparse signal representation to a certain extent. Jump and distortion effects at specific locations. Therefore, comparing the results of inertial navigation and sparse fingerprint positioning, the proposed data layer fusion model (that is, the sparse fingerprint positioning method based on spatial location constraints) has a significant improvement in positioning accuracy and performance, which further verifies the superiority of the fusion algorithm. It is proved that the fusion model is more robust to paths and has more stable performance.

It is understood that the specific order or hierarchy of steps in the disclosed processes is an example of a sample approach. 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 meant 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 simplifying the disclosure. This method of disclosure should not be interpreted as reflecting an intention that embodiments of the claimed subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, present 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 this invention.

The disclosed embodiments are described above to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit and scope of this disclosure. Thus, the present disclosure is not intended to be limited to the embodiments set forth herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The above description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods in order to describe the above embodiments, but one of ordinary skill in the art will recognize that further combinations and permutations of the various embodiments are possible. Accordingly, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of the appended claims. Furthermore, with respect to the term "comprising," as used in the specification or claims, the word is encompassed in a manner similar to the term "comprising," as if "comprising," were construed as a conjunction in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or."

Those skilled in the art may also understand that various illustrative logical blocks (illustrative logical blocks), units, and steps listed in the embodiments of the present invention may be implemented by electronic hardware, computer software, or a combination of the two. To clearly demonstrate the interchangeability of hardware and software, the various illustrative components, units and steps described above have generally described their functions. Whether such functionality is implemented in hardware or software depends on the specific application and overall system design requirements. Those skilled in the art may use various methods to implement the described functions for each specific application, but such implementation should not be construed as exceeding the protection scope of the embodiments of the present invention.

The various illustrative logic blocks, or units described in the embodiments of the present invention can be implemented by general-purpose processors, digital signal processors, application specific integrated circuits (ASICs), field programmable gate arrays or other programmable logic devices, discrete Gate or transistor logic, discrete hardware components, or any combination of the above are designed to implement or operate the functions described. A general-purpose processor may be a microprocessor, or alternatively, the general-purpose processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, multiple microprocessors, one or more microprocessors in combination with a digital signal processor core, or any other similar configuration. accomplish.

The steps of the method or algorithm described in the embodiments of the present invention may be directly embedded in hardware, a software module executed by a processor, or a combination of the two. Software modules may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. Illustratively, a storage medium may be coupled to the processor such that the processor may read information from, and store information in, the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium may be provided in the ASIC, and the ASIC may be provided in the user terminal. Alternatively, the processor and the storage medium may also be provided in different components in the user terminal.

In one or more exemplary designs, the above functions described in the embodiments of the present 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, a computer-readable medium in the form of one or more instructions or code. Computer-readable media includes computer storage media and communication media that facilitate the transfer of a computer program from one place to another. Storage media can be any available media that a general-purpose or special-purpose computer can access. For example, such computer-readable media may include, but are not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other device that can be used to carry or store instructions or data structures and Other media in the form of program code that can be read by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Furthermore, any connection is properly defined as a computer-readable medium, for example, if software is transmitted from a web site, server or other remote source over a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) Or transmitted by wireless means such as infrared, wireless, and microwave are also included in the definition of computer-readable media. The disks and disks include compact disks, laser disks, optical disks, DVDs, floppy disks and blu-ray disks. Disks usually reproduce data magnetically, while discs generally reproduce data optically with lasers. Combinations of the above can also be included in computer readable media.

The specific embodiments described above further describe the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above descriptions are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims (8)

1. An indoor positioning method for a mobile terminal, wherein the method comprises: Offline collection of signal strength information corresponding to a plurality of preset sampling points in a spatial position, respectively, as fingerprint point signal information; constructing a location fingerprint database according to the signal information of the fingerprint points; Acquiring the initial state information of the displacement movement within the current preset unit time of the mobile terminal; Using the displacement motion starting state information of the mobile terminal, the inertial navigation principle is used to calculate the reference position of the displacement termination position; Obtain the signal strength information corresponding to the preset unit time points of the mobile terminal on the displacement motion trajectory, construct a sparse fingerprint positioning model based on spatial position constraints, correct the reference position of the displacement termination position, and obtain the displacement termination position. correct position; Wherein, after constructing a sparse fingerprint positioning model based on spatial position constraints, the alternating direction multiplier method ADMM is used to solve the problem, the reference position of the displacement termination position is modified, and the modified position of the displacement termination position is obtained; The construction of a sparse fingerprint positioning model based on spatial location constraints includes: Build a sparse fingerprint localization model according to the sparse algorithm; According to the monitoring data of the inertial navigation system, a spatial position constraint item is added to the sparse fingerprint positioning model, and a sparse fingerprint positioning model based on the spatial position constraint is constructed; The displacement motion trajectory includes: a linear trajectory, a rectangular trajectory, a triangle-shaped 8-shaped trajectory, or a rectangular 8-shaped trajectory. 2. The indoor positioning method of a mobile terminal as claimed in claim 1, characterized in that, adopting the method of taking the mean value by sampling multiple times at the same sampling point, offline collection of signal strength information corresponding to a plurality of preset sampling points in a spatial position, respectively, as fingerprint point signal information. 3 . The indoor positioning method for a mobile terminal according to claim 1 , wherein the information on the starting state of the displacement movement comprises: starting position, starting velocity, acceleration and angular velocity. 4 . 4. mobile terminal indoor positioning method as claimed in claim 1, is characterized in that, after constructing the sparse fingerprint positioning model based on spatial position constraint, adopts alternating direction multiplier method ADMM to solve, specifically comprises: After constructing a sparse fingerprint localization model based on spatial position constraints, according to the signal reconstruction equation constraints in the Lagrangian multiplier relaxation model, it is transformed into an augmented Lagrangian form, and the model parameters are derived separately to solve corresponding model parameters. 5. An indoor positioning device for a mobile terminal, wherein the device comprises: a fingerprint point signal information collection unit, used for offline collection of signal strength information corresponding to a plurality of preset sampling points in a spatial position, as fingerprint point signal information; a location fingerprint database construction unit, configured to construct a location fingerprint database according to the signal information of the fingerprint points; a displacement motion initial state information acquisition unit, configured to acquire the displacement motion initial state information within the current preset unit time of the mobile terminal; an inertial navigation principle calculation unit, used for calculating the reference position of the displacement termination position by using the inertial navigation principle by using the displacement motion starting state information of the mobile terminal; A model building unit, configured to obtain the signal strength information corresponding to the preset unit time points of the mobile terminal on the displacement motion trajectory, construct a sparse fingerprint positioning model based on spatial position constraints, and correct the reference position of the displacement termination position , obtain the corrected position of the displacement termination position; Wherein, the model building unit is specifically used to build a sparse fingerprint positioning model based on spatial position constraints, and then use the alternating direction multiplier method ADMM to solve the problem, correct the reference position of the displacement termination position, and obtain the displacement termination position. correct position; The model construction unit is further used for: constructing a sparse fingerprint positioning model according to a sparse algorithm; according to the monitoring data of the inertial navigation system, adding a spatial position constraint item to the sparse fingerprint positioning model, and constructing a sparse fingerprint positioning model based on the spatial position constraint ; The displacement motion trajectory includes: a linear trajectory, a rectangular trajectory, a triangle-shaped 8-shaped trajectory, or a rectangular 8-shaped trajectory. 6. The indoor positioning device for a mobile terminal according to claim 5, wherein, The fingerprint point signal information collection unit is specifically configured to collect the signal strength information corresponding to a plurality of preset sampling points in a spatial position offline by adopting the method of multiple sampling at the same sampling point to obtain an average value, as the fingerprint point signal information. 7 . The indoor positioning device for a mobile terminal according to claim 5 , wherein the information on the starting state of the displacement movement comprises: starting position, starting velocity, acceleration and angular velocity. 8 . 8. The indoor positioning device for a mobile terminal according to claim 5, wherein, The model building unit is further specifically configured to transform into an augmented Lagrangian form according to the signal reconstruction equation constraints in the Lagrangian multiplier relaxation model after constructing a sparse fingerprint location model based on spatial position constraints. , respectively derive the model parameters to solve the corresponding model parameters, and correct the reference position of the displacement termination position to obtain the corrected position of the displacement termination position.

Images