Intelligent metasurface-assisted indoor Beidou single signal source positioning method

Abstract

The invention discloses an intelligent metasurface-assisted indoor Beidou single-signal-source positioning method, which comprises the following steps of: firstly, constructing an intelligent metasurface-assisted indoor Beidou single-signal-source positioning system channel model, constructing a virtual line-of-sight link by using an intelligent metasurface, and then, carrying out grid division on an intelligent metasurface far-field region to obtain an intelligent metasurface-assisted indoor Beidou single-signal-source positioning system channel model; a Beidou signal can be directionally reflected to different positions by changing an intelligent metasurface phase shift matrix, an unknown target position estimation value is calculated by comprehensively evaluating positioning loss and receiving end signal intensity, and finally, a dynamic positioning position estimation result is optimized in real time by introducing particle filtering. The compatibility problem of a non-line-of-sight signal and a receiving terminal is effectively solved, and low-cost reliable seamless positioning of an emergency area is realized. A numerical simulation result shows that the method can reach meter-scale to sub-meter-scale positioning precision.

Description

Technical Field

[0001] The present invention belongs to the technical field of indoor and outdoor seamless positioning, and specifically relates to an intelligent metasurface-assisted indoor Beidou single-signal source positioning method. Background Art

[0002] Because satellite signals are essentially electromagnetic waves, they are inherently fragile, characterized by weak signals, poor penetration, and susceptibility to interference. This makes it difficult to ensure the availability and reliability of positioning services in emergency situations, such as indoors, underground, and in mountainous areas where GNSS signals are limited. Traditional solutions to this problem can be categorized as satellite-based optimization and ground-based optimization.

[0003] On the satellite side, efforts are underway to improve global coverage and availability in obstructed environments by launching more satellites (e.g., large-scale deployment of low-orbit satellites). However, this approach carries high deployment costs and can easily lead to congestion in satellite orbits. On the ground side, efforts are underway to optimize receiver RF front-end design to enhance weak signal reception. Furthermore, efforts are underway to integrate multiple sensors, such as inertial navigation (INS), ultra-wideband (UWB), and pseudo-satellite technology. Among them, UWB transmits information by sending and receiving high-frequency pulses between modules. It has many advantages, such as strong multipath resistance, high resolution, strong penetration, and low energy consumption. However, due to environmental and cost constraints, its deployment cost is high. In addition, due to the influence of obstruction and time delay, UWB ranging results contain non-line of sight (NLOS) errors and systematic errors, which need to be estimated and compensated. For users, they need to change the receiving terminal to receive the signal. Although pseudo-satellites are highly compatible with terminal receivers, they require the deployment of at least four signal transmission base stations to form a network, which usually requires time synchronization. The large size and weight of the equipment make it difficult to port and maneuver. The deployment is complex and slow, and it is easily affected by multipath interference. Given this, it is urgent to explore a new paradigm for independent positioning. This is to dynamically reconstruct the satellite signal propagation path through intelligent metasurfaces. This can provide a solution for the satellite signal propagation segment without pre-existing infrastructure and achieve reliable positioning in emergency areas at a low cost.

[0004] Currently, metasurfaces are moving towards intelligence. Intelligent metasurfaces (RIS) integrate various sensor devices to achieve real-time perception and adaptive control, eliminating reliance on manually operated electromagnetic functions. RIS has great potential to improve channel capacity and signal coverage, and can also provide strong guarantees for secure signal transmission. Based on the advantages of RIS in wireless communication systems, applying RIS to wireless positioning can produce reliable and high-precision position estimates at low cost and high energy efficiency, and some research results have been achieved in recent years. However, research on using metasurfaces to assist satellite communication and positioning is still in its early stages, with only a few simulations validating the results. These methods overly idealize the positioning scenario and are only applicable when there are at least three visible satellites. If the number of satellites in the LOS link is less than three, the fixed position of the metasurface will lead to rank deficiency in the pseudorange equation, necessitating the use of multiple metasurfaces to assist positioning, increasing deployment costs. Summary of the Invention

[0005] To solve the above problems, the present invention discloses an intelligent metasurface-assisted indoor Beidou single-signal source positioning method. The corresponding channel model is established according to the scene characteristics of the positioning system, and Beidou single-signal source positioning is achieved through the optimal grid search method. At the same time, in order to improve the search efficiency, the relationship between the number of candidate grids and positioning accuracy is quantitatively studied in static positioning and applied to the dynamic positioning solution, and a particle filtering algorithm is added to improve the dynamic positioning accuracy.

[0006] To achieve the above object, the technical solution of the present invention is as follows:

[0007] An intelligent metasurface-assisted indoor Beidou single-signal source positioning method comprises the following steps:

[0008] Step 1: Establish a positioning system channel model;

[0009] Step 2: Divide the RIS far-field three-dimensional area on the receiver side into S grids;

[0010] Step 3: Enter the optimal grid search algorithm, set the RIS phase shift vector β to a random phase shift, and calculate the atomic channel F of RIS-UE and the reconstructed signal y of each grid. constructed . ;

[0011] Step 4: Select the first n candidate grids with the smallest positioning loss as the coarse positioning result;

[0012] Step 5: Adjust the RIS phase shift vector so that the BeiDou signal beam is aligned with n candidate grids in sequence, and calculate the corresponding received signal strength.

[0013] Step 6: When the received signal strength is the largest, the corresponding grid is taken as the final estimation result, and the particle filter is introduced to optimize the estimation value.

[0014] The specific steps are:

[0015] Step 1. Establish a positioning system channel model.

[0016] The system channel is considered as the cascade of the BS-RIS and RIS-UE sub-link channels, which are expressed as:

[0017]

[0018] Where H br and H ru Denote the channel matrices of BS-RIS and RIS-UE respectively, δ br and δ ru denote the gains of the BS-RIS channel and the RIS-UE channel respectively, j is an imaginary unit, λ is the frequency of the BeiDou signal source, and d br is the known Euclidean distance from BS to RIS, and R is the unknown Euclidean distance from RIS to UE. The steering vector at the RIS array is It can be expressed as:

[0019]

[0020] Where, d a and d e Represents the spacing of each unit in the horizontal and vertical directions, M and N represent the number of rows and columns of the RIS array, respectively. represents the Kronecker product of vectors or matrices, θ x Indicates the angle between the incident signal and the reflected signal and the negative direction of the z-axis, It represents the angle between the projection of the incident signal or reflected signal on the xoy plane and the positive direction of the x-axis.

[0021] Therefore, if the signal transmitted by the signal source is s, the signal received by the receiver is expressed as:

[0022]

[0023] in, represents the adjustable phase shift vector of RIS, each element of which can be expressed as n is the variance σ 2 , random Gaussian noise with mean 0. The symbol T represents the transpose of the matrix, and ⊙ represents the Hadamard product of the vector.

[0024] Step 2. Divide the RIS far-field 3D area on the receiver side into S grids.

[0025] Grid the far field area where the target is located, θ∈[0,π / 2], The sampling interval is π / 50, R∈[2D 2 / λ,R max ], the sampling interval is 0.25m, and there are S grids in total. Among them, 2D 2 / λ is the boundary between far and near fields. Since the upper limit of the far field distance tends to infinity, it is necessary to preliminarily determine R in order to improve the search efficiency. max Under far-field conditions, beamforming is performed through smart metasurface smart reflection, and the RIS-UE channel transmission distance can be expressed as:

[0026]

[0027] Among them, δ1 and δ2 satisfy When θ r =θ, When , the right side of the equation can reach the maximum value, then:

[0028]

[0029] Since the power radiation pattern Often modeled as form, so continue to scale it down to the following formula:

[0030]

[0031] Where A represents the reflection coefficient amplitude of the RIS reflection unit. After the above derivation, the search upper limit of R is preliminarily determined.

[0032] Step 3. Set the RIS phase shift vector β to a random phase shift, and calculate the atomic channel F of RIS-UE and the reconstructed signal y of each grid constructed ;

[0033] First, define the atomic channel of RIS-UE The channel from the RIS unit to each grid corresponds to each element. Define u as the selection vector. Since there is only a single receiving target in this system, there is only one non-zero element in it, and ||u||=1. Based on this, the reconstructed signal for each grid can be written as:

[0034]

[0035] Step 4. Select the first n candidate grids with the smallest positioning loss as the coarse positioning result;

[0036] In the coarse positioning stage, the two-norm difference between the UE received signal and the grid reconstructed signal is defined as the positioning loss, and the grid position with the smallest positioning loss value is selected as the coarse positioning result. The positioning problem can be written as follows:

[0037]

[0038] Since the positioning loss difference between some grids may be very small, but the positions of these grids may not be close, in order to improve the positioning accuracy, this algorithm selects n candidate grids i during coarse positioning and enters into the fine positioning algorithm.

[0039] Step 5. Adjust the RIS phase shift vector so that the BeiDou signal beam is aligned with n candidate grids in sequence, and calculate the corresponding received signal strength;

[0040] The phase shift vector β of the RIS is adjusted according to the location of the candidate grid, so that the BeiDou signal transmitted by the BS can be aligned with the candidate grid location area in sequence. According to the beamforming principle, the reflection phase of each unit of the RIS should satisfy:

[0041]

[0042] Among them, x k and y k represents the unit coordinates,

[0043]

[0044] Step 6. When the received signal strength is the largest, the corresponding grid is taken as the final estimation result, and the particle filter is introduced to optimize the estimation value.

[0045] Only when the receiving end is located at this grid will the received signal strength reach its maximum value. Therefore, the final position estimation is performed by sequentially calculating the corresponding receiving end signal strength values when the Beidou signal is reflected to different candidate grids, as shown in the following formula:

[0046]

[0047] in, is the final estimated position, and β i Represent the channel parameters and phase shift vectors corresponding to each grid. In order to reduce the positioning error caused by the insufficient number of candidate grids, a particle filter is introduced to smooth the positioning result trajectory. The number of particles is initialized to N, and the weight of each particle is set to 1 / N. The particle weights are recursively sampled according to importance, and the result is:

[0048]

[0049] in, is the weight of particle i at time k, d mea-esti is the Euclidean distance between the grid search observation and the particle filter estimate, σ mea-noise is the standard deviation of the observation noise.

[0050] A systematic resampling strategy is adopted, with the particle weight vector as input. By generating uniformly distributed sampling positions and mapping the index according to the cumulative distribution determined by the particle weight, weighted proportional sampling is achieved, and the sample set is weighted to obtain the estimated value at time k:

[0051]

[0052] in, is the normalized particle weight, is the particle state vector, Estimate the state vector for the target. At this time, let Output the final target positioning result at time k.

[0053] The beneficial effects of the present invention are:

[0054] This paper proposes an intelligent metasurface-assisted indoor Beidou single-signal source positioning method. This method addresses the problem that GNSS signals are severely limited in indoor environments, underground spaces, and emergency areas such as mountainous areas. By leveraging the advantages of metasurfaces, such as low cost, easy deployment, and effective configuration of wireless channels, it changes the channel environment of Beidou signals from the propagation path segment, effectively establishes a virtual line-of-sight link, and realizes Beidou single-signal source positioning through optimal grid search. Simulation results show that the proposed algorithm has meter-level to sub-meter-level positioning accuracy in angle search and angle + distance three-parameter search. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is an implementation flow chart of the present invention;

[0056] Figure 2 Comparison chart of positioning error between the optimal grid search algorithm and the one with particle filter estimation. DETAILED DESCRIPTION

[0057] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0058] As shown in the figure, the intelligent metasurface-assisted indoor Beidou single-signal source positioning method described in the present invention has the following specific steps:

[0059] Step 1. Establish a positioning system channel model.

[0060] The system channel can be viewed as a cascade of two sub-link channels: BS-RIS and RIS-UE, which are represented as follows:

[0061]

[0062] Where H br and H ruDenote the channel matrices of BS-RIS and RIS-UE respectively, δ br and δ ru denote the gains of the BS-RIS channel and the RIS-UE channel respectively, j is an imaginary unit, λ is the frequency of the BeiDou signal source, and d br is the known Euclidean distance from BS to RIS, and R is the unknown Euclidean distance from RIS to UE. The steering vector at the RIS array is It can be expressed as:

[0063]

[0064] Where, d a and d e Respectively represent the spacing of each unit in the horizontal direction and the vertical direction, M and N represent the number of rows and columns of the RIS array, represents the Kronecker product of vectors or matrices, θ x Indicates the angle between the incident signal and the reflected signal and the negative direction of the z-axis, It represents the angle between the projection of the incident signal or reflected signal on the xoy plane and the positive direction of the x-axis.

[0065] Therefore, if the signal transmitted by the signal source is s, the signal received by the receiver can be expressed as:

[0066]

[0067] in, represents the adjustable phase shift vector of RIS, each element of which can be expressed as n is the variance σ 2 , random Gaussian noise with mean 0. The symbol T represents the transpose of the matrix, and ⊙ represents the Hadamard product of the vector.

[0068] Step 2. Divide the RIS far-field 3D area on the receiver side into S grids.

[0069] Grid the far field area where the target is located, θ∈[0,π / 2], The sampling interval is π / 50, R∈[2D 2 / λ,R max ], the sampling interval is 0.25m, and there are S grids in total. Among them, 2D 2 / λ is the boundary between far and near fields. Since the upper limit of the far field distance tends to infinity, it is necessary to preliminarily determine R in order to improve the search efficiency. max Under far-field conditions, beamforming is performed through smart metasurface smart reflection, and the RIS-UE channel transmission distance can be expressed as:

[0070]

[0071] Among them, δ1 and δ2 satisfy When θ r =θ, When , the right side of the equation reaches its maximum value, then:

[0072]

[0073] Since the power radiation pattern Often modeled as form, so continue to scale it down to the following formula:

[0074]

[0075] Where A represents the reflection coefficient amplitude of the RIS reflection unit. After the above derivation, the search upper limit of R is preliminarily determined.

[0076] Step 3. Set the RIS phase shift vector β to a random phase shift, and calculate the atomic channel F of RIS-UE and the reconstructed signal y of each grid constructed ;

[0077] First, define the atomic channel of RIS-UE The channel from the RIS unit to each grid corresponds to each element. Define u as the selection vector. Since there is only a single receiving target in this system, there is only one non-zero element in it, and ||u||=1. Based on this, the reconstructed signal for each grid can be written as:

[0078]

[0079] Step 4. Select the first n candidate grids with the smallest positioning loss as the coarse positioning result;

[0080] In the coarse positioning stage, the two-norm difference between the UE received signal and the grid reconstructed signal is defined as the positioning loss, and the grid position with the smallest positioning loss value is selected as the coarse positioning result. The positioning problem can be written as follows:

[0081]

[0082] Since the positioning loss difference between some grids may be very small, but the positions of these grids may not be close, in order to improve the positioning accuracy, this algorithm selects n candidate grids i during coarse positioning and enters into the fine positioning algorithm.

[0083] Step 5. Adjust the RIS phase shift vector so that the BeiDou signal beam is aligned with n candidate grids in sequence, and calculate the corresponding received signal strength;

[0084] The phase shift vector β of the RIS is adjusted according to the location of the candidate grid, so that the BeiDou signal transmitted by the BS can be aligned with the candidate grid location area in sequence. According to the beamforming principle, the reflection phase of each unit of the RIS should satisfy:

[0085]

[0086] Among them, x k and y k represents the unit coordinates,

[0087] Step 6. When the received signal strength is the largest, the corresponding grid is taken as the final estimation result, and the particle filter is introduced to optimize the estimation value.

[0088] Only when the receiving end is located at this grid will the received signal strength reach its maximum value. Therefore, the final position estimation is performed by sequentially calculating the corresponding receiving end signal strength values when the Beidou signal is reflected to different candidate grids, as shown in the following formula:

[0089]

[0090] in, is the final estimated position, and β i Represent the channel parameters and phase shift vectors corresponding to each grid. In order to reduce the positioning error caused by the insufficient number of candidate grids, a particle filter is introduced to smooth the positioning result trajectory. The number of particles is initialized to N, and the weight of each particle is set to 1 / N. The particle weights are recursively sampled according to importance, and the result is:

[0091]

[0092] in, is the weight of particle i at time k, d mea-esti is the Euclidean distance between the grid search observation and the particle filter estimate, σ mea-noise is the standard deviation of the observation noise.

[0093] A systematic resampling strategy is adopted, with the particle weight vector as input. By generating uniformly distributed sampling positions and mapping the index according to the cumulative distribution determined by the particle weight, weighted proportional sampling is achieved, and the sample set is weighted to obtain the estimated value at time k:

[0094]

[0095] in, is the normalized particle weight, is the particle state vector, Estimate the state vector for the target. At this time, let Output the final target positioning result at time k.

[0096] It should be noted that the above content merely illustrates the technical idea of the present invention and cannot be used to limit the scope of protection of the present invention. For ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications all fall within the scope of protection of the claims of the present invention.

Claims

1. An intelligent metasurface-assisted indoor Beidou single-signal source positioning method, characterized in that: include: Step 1: Establish a positioning system channel model; Step 2: Divide the RIS far-field three-dimensional area on the receiver side into S grids; Step 3: Enter the optimal grid search algorithm: set the RIS phase shift vector β to a random phase shift, calculate the atomic channel F of RIS-UE and the reconstructed signal y of each grid constructed ; Step 4: Select the first n candidate grids with the smallest positioning loss as the coarse positioning result; Step 5: Adjust the RIS phase shift vector so that the BeiDou signal beam is aligned with n candidate grids in sequence, and calculate the corresponding received signal strength; Step 6: When the received signal strength is the largest, the corresponding grid is taken as the final estimation result, and the particle filter is introduced to optimize the estimation value.

2. The intelligent metasurface-assisted indoor Beidou single signal source positioning method according to claim 1, characterized in that: Step 1: Establish the positioning system channel model; the details are as follows: The system channel is considered as a cascade of two sub-link channels: the BeiDou signal source-intelligent metasurface and the RIS-receiver, which are expressed as: Where H br and H ru Denote the channel matrices of BS-RIS and RIS-UE respectively, δ br and δ ru denote the gains of the BS-RIS channel and the RIS-UE channel respectively, j is an imaginary unit, λ is the frequency of the BeiDou signal source, and d br is the known Euclidean distance from BS to RIS, R is the unknown Euclidean distance from RIS to UE; the steering vector at the RIS array Expressed as: Where, x∈{in,out},d a and d e Respectively represent the spacing of each unit in the horizontal direction and the vertical direction, M and N represent the number of rows and columns of the RIS array, represents the Kronecker product of vectors or matrices, θ x Indicates the angle between the incident signal and the reflected signal and the negative direction of the z-axis, It represents the angle between the projection of the incident signal or reflected signal on the xoy plane and the positive direction of the x-axis; Therefore, if the signal transmitted by the signal source is s, the signal received by the receiver is expressed as: in, represents the adjustable phase shift vector of RIS, each element of which is expressed as n is the variance σ 2 , random Gaussian noise with mean 0; the symbol T represents the transpose of the matrix, and the symbol ⊙ represents the Hadamard product of the vector.

3. The intelligent metasurface-assisted indoor Beidou single signal source positioning method according to claim 1, characterized in that: Step 2 divides the RIS far-field 3D area on the receiver side into S grids. The details are as follows: Grid the far field area where the target is located, θ∈[0,π / 2], The sampling interval is π / 50, R∈[2D 2 / λ,R max ], the sampling interval is 0.25m, and there are S grids in total; among them, 2D 2 / λ is the boundary between far and near fields. Since the upper limit of the far field distance tends to infinity, it is necessary to preliminarily determine R in order to improve the search efficiency. max ; Under far-field conditions, beamforming is performed through smart metasurface smart reflection. The RIS-UE channel transmission distance is expressed as: Among them, δ1 and δ2 satisfy When θ r =θ, When , the right side of the equation reaches its maximum value, then: Since the power radiation pattern Often modeled as form, so continue to scale it down to the following formula: Where A represents the reflection coefficient amplitude of the RIS reflection unit. After the above derivation, the search upper limit of R is preliminarily determined.

4. The intelligent metasurface-assisted indoor Beidou single signal source positioning method according to claim 1, characterized in that: Set the RIS phase shift vector β as a random phase shift in step 3, calculate the atomic channel F of RIS-UE and the reconstructed signal y of each grid constructed ; The details are as follows: First, define the atomic channel of RIS-UE The channel from the RIS unit to each grid corresponds to each element in it; Define u as the selection vector. Since there is only a single receiving target in this system, there is only one non-zero element in it, and ||u||=1. Based on this, the reconstructed signal for each grid is written as:

5. The intelligent metasurface-assisted indoor Beidou single signal source positioning method according to claim 1, characterized in that: The first n candidate grids with the smallest positioning loss are selected as the coarse positioning result in step 4; the details are as follows: In the coarse positioning stage, the two-norm difference between the UE received signal and the grid reconstructed signal is defined as the positioning loss, and the grid position with the smallest positioning loss is selected as the coarse positioning result. The positioning problem is expressed as follows: During coarse positioning, n candidate grids i are selected to enter the fine positioning algorithm.

6. The intelligent metasurface-assisted indoor Beidou single signal source positioning method according to claim 1, characterized in that: Adjust the RIS phase shift vector as described in step 5 so that the BeiDou signal beam is aligned with n candidate grids in sequence, and calculate the corresponding received signal strength; the details are as follows: The phase shift vector β of the RIS is adjusted according to the location of the candidate grid, so that the BeiDou signal transmitted by the BS can be aligned with the candidate grid location area in sequence. According to the beamforming principle, the reflection phase of each unit of the RIS should satisfy: Among them, x k and y k represents the unit coordinates, 7. The intelligent metasurface-assisted indoor Beidou single signal source positioning method according to claim 1, characterized in that: The grid corresponding to the maximum received signal strength in step 6 is used as the final estimation result, and a particle filter is introduced to optimize the estimated value, as follows: Only when the receiving end is located at this grid will the received signal strength reach its maximum value. Therefore, the final position estimation is performed by sequentially calculating the corresponding receiving end signal strength values when the Beidou signal is reflected to different candidate grids, as shown in the following formula: in, is the final estimated position, and β i Represent the channel parameters and phase shift vectors corresponding to each grid. In order to reduce the positioning error caused by the insufficient number of candidate grids, a particle filter is introduced to smooth the positioning result trajectory; the number of particles is initialized to N, and the weight of each particle is set to 1 / N. The particle weights are recursively sampled according to importance, and the result is: in, is the weight of particle i at time k, d mea-esti is the Euclidean distance between the grid search observation and the particle filter estimate; σ mea-noise is the standard deviation of the observation noise; A systematic resampling strategy is adopted, with the particle weight vector as input. By generating uniformly distributed sampling positions and mapping the index according to the cumulative distribution determined by the particle weight, weighted proportional sampling is achieved, and the sample set is weighted to obtain the estimated value at time k: in, is the normalized particle weight, is the particle state vector, Estimate the state vector for the target; at this time, let Output the final target positioning result at time k.

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