阅读说明：本技术 一种无人机辅助定位方法、系统、存储介质及终端设备 (Unmanned aerial vehicle auxiliary positioning method and system, storage medium and terminal equipment ) 是由 周发升 邹旭 范立生 李东云 于 2021-07-16 设计创作，主要内容包括：本申请涉及一种无人机辅助定位方法,所述方法包括：通过信号强度模型得到具有噪声的接收信号；将具有噪声的所述接收信号输入滤波器进行滤波处理,以消除所述发接收信号中的阴影噪声；通过代价模型对消除阴影噪声的所述接收信号进行迭代处理,以得到路径衰落因子及发射端与接收端的距离；根据所述路径衰落因子及发射端与接收端的距离对用户进行定位。本发明能够考虑阴影效应对信号强度的影响,并提高路径衰落因子的获取精度,从而用户的定位精度,满足了实际应用需求。(The application relates to an unmanned aerial vehicle auxiliary positioning method, which comprises the following steps: obtaining a receiving signal with noise through a signal strength model; inputting the receiving signal with noise into a filter for filtering processing so as to eliminate shadow noise in the transmitting and receiving signal; performing iterative processing on the received signal without the shadow noise through a cost model to obtain a path fading factor and a distance between a transmitting end and a receiving end; and positioning the user according to the path fading factor and the distance between the transmitting end and the receiving end. The invention can consider the influence of the shadow effect on the signal strength and improve the acquisition precision of the path fading factor, thereby the positioning precision of the user can meet the requirement of practical application.)

1. An unmanned aerial vehicle auxiliary positioning method is characterized by comprising the following steps:

obtaining a receiving signal with noise through a signal strength model;

inputting the receiving signal with noise into a filter for filtering processing so as to eliminate shadow noise in the receiving signal;

performing iterative processing on the received signal without the shadow noise through a cost model to obtain a path fading factor and a distance between a transmitting end and a receiving end;

and positioning the user according to the path fading factor and the distance between the transmitting end and the receiving end.

2. An unmanned aerial vehicle assisted positioning method as defined in claim 1, wherein the signal strength model is:

wherein the content of the first and second substances,the unit of a transmitting signal transmitted from the ith anchor point and received by a user is dBm; p_t ⁽ⁱ⁾Is the strength of the transmitted signal; p₀ ⁽ⁱ⁾Is a constant, is the reference power at one meter of the transmitted signal; kappa_iA path fading factor between the user and the ith anchor point; d is the distance between the user and the ith anchor point;is a mean of 0 and a variance of σ²The shadow noise of (2) is in accordance with a normal distribution.

3. An unmanned aerial vehicle assisted positioning method according to claim 2, wherein the filter acquisition method comprises:

acquiring an initialization parameter; the initialization parameters comprise a training sequence, a receiving signal with noise of a specific length, a filter tap, the number of the filter taps and a learning step length;

taking the number of filter taps with a specific degree from a certain element of the training sequence as a subsequence;

filtering the received signal with noise in the subsequence by the filter tap to obtain an output value;

and updating an error value according to the output value, and updating the filter according to the error value and the learning step length.

4. An unmanned aerial vehicle assisted positioning method according to claim 2, wherein the model of the filter is:

ω＝ω_j+2μe(j)x^T(j)

wherein, ω is_jFor the filter taps, μ is the learning step size, e (j) is the error value, x (j) is the subsequence, j is the element in the training sequence, and T is the matrix transpose.

5. The method of claim 1, wherein the iterative processing of the received signal with shadow noise removed by a cost model comprises:

initializing a path fading factor and a distance between a transmitting end and a receiving end;

performing iterative training on the initialized path fading factor and the distance between the transmitting end and the receiving end through the cost model;

updating the cost model when a first search accuracy of the path fading factor and a second search accuracy of the distance between the transmitting end and the receiving end tend to converge.

6. An unmanned aerial vehicle assisted positioning method according to claim 5, wherein the cost model is:

wherein, P_tTo transmit signals, P_rFor receiving signals, k is the path fading factor, d is the distance between the transmitting end and the receiving end,for shadow noise, ω is the filter, K is the number of iterations, ε_κFor the first search precision, epsilon_dIs the second search precision.

7. An unmanned aerial vehicle assistance-localization real-time system, the system comprising:

the noise acquisition module is used for obtaining a receiving signal with noise through a signal intensity model;

the filtering processing module is used for inputting the receiving signal with the noise into a filter for filtering processing so as to eliminate shadow noise in the transmitting and receiving signal;

the iterative training module is used for carrying out iterative processing on the received signal without the shadow noise through the cost model so as to obtain a path fading factor and the distance between the transmitting end and the receiving end;

and the user positioning module is used for positioning the user according to the path fading factor and the distance between the transmitting end and the receiving end.

8. An unmanned aerial vehicle assisted positioning system as defined in claim 1, wherein the signal strength model is:

wherein the content of the first and second substances,the unit of a transmitting signal transmitted from the ith anchor point and received by a user is dBm; p_t ⁽ⁱ⁾Is the strength of the transmitted signal; p₀ ⁽ⁱ⁾Is a constant, is the reference power at one meter of the transmitted signal; kappa_iA path fading factor between the user and the ith anchor point; d is the distance between the user and the ith anchor point;is a mean of 0 and a variance of σ²The shadow noise of (2) is in accordance with a normal distribution.

9. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored computer program; wherein the computer program, when executed, controls an apparatus on which the computer-readable storage medium resides to perform the drone assisted positioning method of any one of claims 1 to 6.

10. A terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor when executing the computer program implementing the drone-assisted positioning method of any one of claims 1 to 6.

Technical Field

The application relates to the technical field of unmanned aerial vehicle navigation and positioning, in particular to an unmanned aerial vehicle auxiliary positioning method, system, storage medium and terminal equipment.

Background

Along with the continuous development of information technology and communication technology, unmanned aerial vehicle is by wide application in industry and scientific community, if carry out weather monitoring, communication, detection etc. through unmanned aerial vehicle. Due to their high mobility, low cost, and ease of deployment, drones are also being more used for positioning.

In recent years, RSS positioning has come to be applied to improve positioning accuracy for a moving user in a non-line-of-sight situation. RSS positioning is a positioning method that calculates the distance between a transmitting end and a receiving end from the strength of a signal received by the receiving end.

However, in practical situations, due to the existence of obstacles such as high buildings, trees, etc., between the transmitting end and the receiving end, the signal is affected by path fading during propagation, so that the strength of the signal at the receiving end is reduced. Since the influence of path fading on the signal strength needs to be taken into account, the value of the path fading factor k needs to be determined. Since the accuracy of the estimated value of κ has a direct influence on the accuracy of positioning, obtaining an estimated value of κ with high accuracy is extremely important to improve the accuracy of positioning.

Disclosure of Invention

Therefore, it is necessary to provide an unmanned aerial vehicle assisted positioning method, system, storage medium and terminal device capable of improving the positioning accuracy of the user in order to solve the above technical problems.

The embodiment of the invention provides an auxiliary positioning method for an unmanned aerial vehicle, which comprises the following steps:

obtaining a receiving signal with noise through a signal strength model;

inputting the receiving signal with noise into a filter for filtering processing so as to eliminate shadow noise in the receiving signal;

performing iterative processing on the received signal without the shadow noise through a cost model to obtain a path fading factor and a distance between a transmitting end and a receiving end;

and positioning the user according to the path fading factor and the distance between the transmitting end and the receiving end.

Further, the signal strength model is:

wherein the content of the first and second substances,the unit of a transmitting signal transmitted from the ith anchor point and received by a user is dBm;is the strength of the transmitted signal;is a constant, is the reference power at one meter of the transmitted signal; kappa_iA path fading factor between the user and the ith anchor point; d is the distance between the user and the ith anchor point;is a mean of 0 and a variance of σ²The shadow noise of (2) is in accordance with a normal distribution.

Further, the method for obtaining the filter comprises the following steps:

acquiring an initialization parameter; the initialization parameters comprise a training sequence, a receiving signal with noise of a specific length, a filter tap, the number of the filter taps and a learning step length;

taking the number of filter taps with a specific degree from a certain element of the training sequence as a subsequence;

filtering the received signal with noise in the subsequence by the filter tap to obtain an output value;

and updating an error value according to the output value, and updating the filter according to the error value and the learning step length.

Further, the model of the filter is:

ω＝ω_j+2μe(j)x^T(j)

wherein, ω is_jFor the filter taps, μ is the learning step size, e (j) is the error value, x (j) isSubsequence, j is an element in the training sequence, and T is a matrix transpose.

Further, the method for iteratively processing the received signal with shadow noise removed by the cost model includes:

initializing a path fading factor and a distance between a transmitting end and a receiving end;

performing iterative training on the initialized path fading factor and the distance between the transmitting end and the receiving end through the cost model;

updating the cost model when a first search accuracy of the path fading factor and a second search accuracy of the distance between the transmitting end and the receiving end tend to converge.

Further, the cost model is:

wherein, P_tTo transmit signals, P_rFor receiving signals, k is the path fading factor, d is the distance between the transmitting end and the receiving end,for shadow noise, ω is the filter, K is the number of iterations, ε_κFor the first search precision, epsilon_dIs the second search precision.

Another embodiment of the invention provides an unmanned aerial vehicle auxiliary positioning system, which solves the problem that the positioning accuracy of a user is reduced because the strength of a signal at a receiving end is reduced due to the influence of a shadow effect on the signal in the transmission process because obstacles such as high buildings, trees and the like exist between the existing transmitting end and the existing receiving end.

According to the unmanned aerial vehicle assistance-localization real-time system of the embodiment of the invention, include:

the noise acquisition module is used for obtaining a receiving signal with noise through a signal intensity model;

the filtering processing module is used for inputting the receiving signal with the noise into a filter for filtering processing so as to eliminate shadow noise in the transmitting and receiving signal;

the iterative training module is used for carrying out iterative processing on the received signal without the shadow noise through the cost model so as to obtain a path fading factor and the distance between the transmitting end and the receiving end;

and the user positioning module is used for positioning the user according to the path fading factor and the distance between the transmitting end and the receiving end.

Further, the signal strength model is:

wherein the content of the first and second substances,the unit of a transmitting signal transmitted from the ith anchor point and received by a user is dBm;is the strength of the transmitted signal;is a constant, is the reference power at one meter of the transmitted signal; kappa_iA path fading factor between the user and the ith anchor point; d is the distance between the user and the ith anchor point;is a mean of 0 and a variance of σ²The shadow noise of (2) is in accordance with a normal distribution.

Another embodiment of the present invention is also directed to a computer readable storage medium including a stored computer program; wherein the computer program, when running, controls the device on which the computer-readable storage medium is located to perform the drone assisted positioning method as described above.

Another embodiment of the present invention also proposes a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the drone-assisted positioning method as described above when executing the computer program.

According to the auxiliary positioning method of the unmanned aerial vehicle, the received signal with noise is obtained through the signal intensity model; inputting the receiving signal with noise into a filter for filtering processing so as to eliminate shadow noise in the receiving signal; performing iterative processing on the received signal without the shadow noise through a cost model to obtain a path fading factor and a distance between a transmitting end and a receiving end; and positioning the user according to the path fading factor and the distance between the transmitting end and the receiving end. Compared with the prior art, the method and the device can consider the influence of the shadow effect on the signal strength and improve the acquisition precision of the path fading factor, so that the positioning precision of a user can meet the actual application requirement.

Drawings

Fig. 1 is a schematic flow chart of an auxiliary positioning method for an unmanned aerial vehicle according to an embodiment of the present invention;

fig. 2 is a graph of an original signal and a signal with noise in the auxiliary positioning method for an unmanned aerial vehicle according to the embodiment of the present invention;

fig. 3 is a graph illustrating an original signal and a filtered signal in the auxiliary positioning method for an unmanned aerial vehicle according to the embodiment of the present invention;

FIG. 4 is a detailed flowchart of step S11 in FIG. 1;

fig. 5 is a diagram of a mean square error of positioning in the unmanned aerial vehicle assisted positioning method according to the embodiment of the present invention;

FIG. 6 is a schematic diagram of model fusion in step S13 in FIG. 1;

FIG. 7 is the data flow of FIG. 2;

fig. 8 is a block diagram of an auxiliary positioning system for an unmanned aerial vehicle according to an embodiment of the present invention;

fig. 9 is a structural diagram of a terminal device according to an embodiment 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 any inventive step, are within the scope of the present invention.

It should be noted that, the step numbers in the text are only for convenience of explanation of the specific embodiments, and do not serve to limit the execution sequence of the steps. The method provided by the embodiment can be executed by the relevant server, and the server is taken as an example for explanation below.

As shown in fig. 1 to 7, an unmanned aerial vehicle assisted positioning method provided in an embodiment of the present invention includes steps S11 to S14:

in step S11, a noisy received signal is obtained by the signal strength model.

Specifically, suppose there is a mobile user on the ground, N drones are deployed in the air, and the drone can locate the mobile user on the ground through the signal strength. The unmanned aerial vehicle is as the transmitting terminal of signal promptly, and the mobile subscriber is as the receiving terminal of signal, and the signal strength accessible signal strength model that the receiving terminal received calculates and obtains. Please refer to fig. 2 for a graph of the original signal and the noisy signal.

Further, the signal strength model is:

wherein the content of the first and second substances,the unit of a transmitting signal transmitted from the ith anchor point and received by a user is dBm;is the strength of the transmitted signal;is a constant, is the reference power at one meter of the transmitted signal; kappa_iA path fading factor between the user and the ith anchor point; d is the distance between the user and the ith anchor point;is a mean of 0 and a variance of σ²The shadow noise of (2) is in accordance with a normal distribution.

It can be understood that the strength of the received signal and the distance from the transmitting end to the receiving end can be obtained by the signal strength model. Wherein the shadow noise and the estimated value of k and the shadow noiseThe accuracy of the positioning is affected.

Step S12, inputting the received signal with noise into a filter for filtering processing to eliminate shadow noise in the received signal.

In particular, becauseIs an additive random process whose distribution is independent of κ and d, and is therefore independent of κ and dThe elimination of (c) can be seen as a separate problem to solve. To this end, we design a filter based on the LMS algorithm to eliminate n, the taps of this filter being trained with a training sequence. That is, additive white gaussian noise is eliminated by a weighting filter, the shadow noise is transformed into a gaussian random variable which is subject to uniform distribution, the mean value is zero, and the variance is a certain number. Please refer to fig. 3 for a graph of the original signal and the filtered signal.

Referring to fig. 4, the method for obtaining the filter includes:

in step S121, initialization parameters are acquired.

Wherein the initialization parameters comprise a subsequence x of training sequences χ, of a specific length MNoisy received signalFilter tap omega_jFilter tap number L and learning step size μ.

And step S122, taking the filter tap number of a specific degree from a certain element of the training sequence as a subsequence.

Specifically, starting from the jth element of the training sequence χ, a subsequence x (j) of length L is taken, i.e., x (j) ═ x (j-L)]x(j-L+1)…x(j-1)]^T]。

And S123, filtering the received signal with noise in the subsequence through the filter tap to obtain an output value.

In particular, by ω_jFiltering x (j) to obtainAn output value of, i.e.

Step S124, updating an error value according to the output value, and updating the filter according to the error value and the learning step size.

Specifically, the error value isThe model of the filter is:

ω＝ω_j+2μe(j)x^T(j)

wherein, ω is_jFor the filter taps, μ is the learning step size, e (j) is the error value, x (j) is the subsequence, j is the element in the training sequence, and T is the matrix transpose.

And step S13, performing iterative processing on the received signal without the shadow noise through a cost model to obtain a path fading factor and a distance between the transmitting end and the receiving end.

Specifically, in wireless communications, the path attenuation factor k is a variable value. In fact, the value of κ varies with the environment, and the value of κ affects the accuracy of positioning. The present application therefore makes estimates of κ and d through a cost model. The idea of the algorithm is to fix d, iterate k until k reaches a certain precision, and update the value of k; then fixing kappa, iterating d until d reaches a certain precision, and updating the value of d. After a certain number of iterations, κ and d with higher precision are obtained. Please refer to fig. 5 for a plot of mean square error of positioning.

Referring to fig. 6 to 7, the method for iteratively processing the received signal with shadow noise removed by the cost model includes:

step S131, initializing path fading factors and the distance between the transmitting end and the receiving end.

And step S132, performing iterative training on the initialized path fading factor and the distance between the transmitting end and the receiving end through the cost model.

Step S133, when the first search accuracy of the path fading factor and the second search accuracy of the distance between the transmitting end and the receiving end tend to converge, updating the cost model.

Specifically, the path fading factor and the distance between the transmitting end and the receiving end are initialized, and the trained filter omega is used for eliminating the shadow noisePerforming iterative training on the initialized path fading factors through the cost model until the initialized path fading factors are subjected to iterative trainingThe k value in the cost function is then updated, i.e.Iteratively training the distance between the initialized transmitting terminal and the receiving terminal through the cost model until the distance is up toThe value of d in the cost function is then updated, i.e.

Wherein the cost model is:

wherein, P_tTo transmit signals, P_rFor receiving signals, k is the path fading factor, d is the distance between the transmitting end and the receiving end,for shadow noise, ω is the filter, K is the number of iterations, ε_κFor the first search precision, epsilon_dIs the second search precision.

It can be understood that by separately iterating κ and d, the problem that the iteration result is not high in accuracy because there are multiple local optimal solutions due to the non-convex problem when solving d is avoided.

And step S14, positioning the user according to the path fading factor and the distance between the transmitting end and the receiving end.

As described above, the influence of the shadow effect is considered in the signal propagation process, and the user is positioned by the path fading factor with higher precision and the distance between the transmitting end and the receiving end, so that the satisfaction degree of the user positioning experience is improved.

According to the auxiliary positioning method of the unmanned aerial vehicle, the received signal with noise is obtained through the signal intensity model; inputting the receiving signal with noise into a filter for filtering processing so as to eliminate shadow noise in the receiving signal; performing iterative processing on the received signal without the shadow noise through a cost model to obtain a path fading factor and a distance between a transmitting end and a receiving end; and positioning the user according to the path fading factor and the distance between the transmitting end and the receiving end. Compared with the prior art, the method and the device can consider the influence of the shadow effect on the signal strength and improve the acquisition precision of the path fading factor, so that the positioning precision of a user can meet the actual application requirement.

It should be understood that, although the steps in the above-described flowcharts are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in the above-described flowcharts may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or the stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least a portion of the sub-steps or stages of other steps.

As shown in fig. 8, the structural block diagram of the auxiliary positioning system for unmanned aerial vehicle provided in the present invention is shown, the system includes:

and a noise obtaining module 21, configured to obtain a received signal with noise through a signal strength model.

Wherein the signal strength model is:

wherein the content of the first and second substances,the unit of a transmitting signal transmitted from the ith anchor point and received by a user is dBm;is the strength of the transmitted signal;is a constant, is the reference power at one meter of the transmitted signal; kappa_iA path fading factor between the user and the ith anchor point; d is the distance between the user and the ith anchor point;is a mean of 0 and a variance of σ²The shadow noise of (2) is in accordance with a normal distribution.

And a filtering processing module 22, configured to input the noisy received signal into a filter for filtering processing, so as to eliminate shadow noise in the received signal.

Specifically, the filter is obtained by obtaining, for example,

acquiring an initialization parameter; the initialization parameters comprise a training sequence, a receiving signal with noise of a specific length, a filter tap, the number of the filter taps and a learning step length;

taking the number of filter taps with a specific degree from a certain element of the training sequence as a subsequence;

filtering the received signal with noise in the subsequence by the filter tap to obtain an output value;

and updating an error value according to the output value, and updating the filter according to the error value and the learning step length.

Further, the model of the filter is:

ω＝ω_j+2μe(j)x^T(j)

wherein, ω is_jFor the filter taps, μ is the learning step size, e (j) is the error value, x (j) is the subsequence, j is the element in the training sequence, and T is the matrix transpose.

And the iterative training module 23 is configured to perform iterative processing on the received signal without the shadow noise through a cost model to obtain a path fading factor and a distance between the transmitting end and the receiving end.

The iterative training module 23 is, in particular for,

initializing a path fading factor and a distance between a transmitting end and a receiving end;

performing iterative training on the initialized path fading factor and the distance between the transmitting end and the receiving end through the cost model;

updating the cost model when a first search accuracy of the path fading factor and a second search accuracy of the distance between the transmitting end and the receiving end tend to converge.

The cost model is as follows:

wherein, P_tTo transmit signals, P_rFor receiving signals, k is the path fading factor, d is the distance between the transmitting end and the receiving end,for shadow noise, ω is the filter, K is the number of iterations, ε_κFor the first search precision, epsilon_dIs the second search precision.

And the user positioning module 24 is configured to position the user according to the path fading factor and the distance between the transmitting end and the receiving end.

According to the unmanned aerial vehicle auxiliary positioning system provided by the embodiment of the invention, a receiving signal with noise is obtained through a signal intensity model; inputting the receiving signal with noise into a filter for filtering processing so as to eliminate shadow noise in the receiving signal; performing iterative processing on the received signal without the shadow noise through a cost model to obtain a path fading factor and a distance between a transmitting end and a receiving end; and positioning the user according to the path fading factor and the distance between the transmitting end and the receiving end. Compared with the prior art, the method and the device can consider the influence of the shadow effect on the signal strength and improve the acquisition precision of the path fading factor, so that the positioning precision of a user can meet the actual application requirement.

An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program; wherein the computer program, when running, controls the device on which the computer-readable storage medium is located to perform the drone assisted positioning method as described above.

An embodiment of the present invention further provides a terminal device, as shown in fig. 9, which is a block diagram of a preferred embodiment of the terminal device provided in the present invention, the terminal device includes a processor 10, a memory 20, and a computer program stored in the memory 20 and configured to be executed by the processor 10, and the processor 10, when executing the computer program, implements the unmanned aerial vehicle assisted positioning method described above.

Preferably, the computer program can be divided into one or more modules/units (e.g. computer program 1, computer program 2,) which are stored in the memory 20 and executed by the processor 10 to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the terminal device.

The Processor 10 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, etc., the general purpose Processor may be a microprocessor, or the Processor 10 may be any conventional Processor, the Processor 10 is a control center of the terminal device, and various interfaces and lines are used to connect various parts of the terminal device.

The memory 20 mainly includes a program storage area that may store an operating system, an application program required for at least one function, and the like, and a data storage area that may store related data and the like. In addition, the memory 20 may be a high speed random access memory, may also be a non-volatile memory, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), and the like, or the memory 20 may also be other volatile solid state memory devices.

It should be noted that the terminal device may include, but is not limited to, a processor and a memory, and those skilled in the art will understand that the structural block diagram of fig. 9 is only an example of the terminal device, and does not constitute a limitation to the terminal device, and may include more or less components than those shown, or combine some components, or different components.

In summary, the unmanned aerial vehicle auxiliary positioning method, system, storage medium and terminal device provided by the embodiment of the present invention obtain a noisy received signal through a signal strength model; inputting the receiving signal with noise into a filter for filtering processing so as to eliminate shadow noise in the receiving signal; performing iterative processing on the received signal without the shadow noise through a cost model to obtain a path fading factor and a distance between a transmitting end and a receiving end; and positioning the user according to the path fading factor and the distance between the transmitting end and the receiving end. Compared with the prior art, the method and the device can consider the influence of the shadow effect on the signal strength and improve the acquisition precision of the path fading factor, so that the positioning precision of a user can meet the actual application requirement.

The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.