A pseudolite indoor positioning modeling simulation method

Abstract

The application provides a pseudo-satellite indoor positioning modeling simulation method, and belongs to the indoor positioning technology simulation field. The application generates an indoor pseudo-satellite base station deployment optimization scheme by using a pseudo-satellite optimal deployment optimization algorithm, and performs indoor signal propagation calculation according to a pre-input or modeled indoor three-dimensional environment through an indoor multipath simulation unit, generates original observation data, inputs the observation data into an observation data quality analysis unit for data quality analysis, and then inputs a positioning algorithm to complete positioning, so that the simulation modeling of indoor pseudo-satellite positioning signal characteristics and base station deployment is realized, the influence of factors such as pseudo-satellite base station layout, indoor space layout and signal power size can be analyzed, simulation basis is provided for improving the indoor positioning algorithm, and the waste of manpower and material resources caused by repeated actual measurement is avoided.

Description

Technical Field

[0001] This invention belongs to the field of indoor positioning technology simulation, and in particular relates to a pseudo-satellite indoor positioning modeling and simulation method. Background Technology

[0002] With the large-scale development of indoor and underground spaces, people are spending more and more time indoors, especially in large venues (such as large shopping malls, museums, and underground parking garages). These indoor environments are often unfamiliar or overly complex, making it impossible to determine location and routes based solely on experience. Therefore, additional information and tools are essential. Indoor positioning technology is key to providing useful and targeted market and service content to indoor users, and has become a research focus and hot topic in recent years. Indoor intelligent positioning systems will undoubtedly play a vital role in future production and daily life.

[0003] Pseudo-satellites are devices installed near the ground that can emit GNSS-like signals. Pseudo-satellite positioning technology is relatively easy to scale up, and its positioning accuracy can reach sub-meter levels. In practical applications, actual collected data is often used for performance analysis and verification of positioning algorithms. However, actual measurements involve a large workload, incomplete data, and limited flexibility. Furthermore, due to limitations in experimental sites and equipment, it is impossible to test and verify each proposed principle system during the research phase, resulting in a small number of evaluation samples for small-scale field testing. Therefore, a more systematic design approach is needed: first, establish a virtual prototype of the positioning base station deployment, conduct simulation testing and verification, and then develop a physical prototype. This provides simulation support for subsequent indoor positioning system methods. Simultaneously, based on the influence of factors such as base station deployment, indoor spatial layout, and signal power, simulation data is provided to improve indoor positioning algorithms. Summary of the Invention

[0004] To address the growing demand for indoor simulation in indoor positioning technology, this invention provides a pseudo-satellite indoor positioning modeling and simulation method. This method utilizes a pseudo-satellite optimal deployment optimization algorithm to generate an optimized indoor pseudo-satellite base station deployment scheme. An indoor multipath simulation unit then performs indoor signal propagation calculations based on a pre-input or modeled indoor 3D environment. The generated raw observations are input into an observation data quality analysis unit for data quality analysis before being input into the positioning algorithm to complete the positioning process. This achieves simulation modeling of indoor pseudo-satellite positioning signal characteristics and base station deployment. It can analyze the impact of factors such as pseudo-satellite base station deployment, indoor spatial layout, and signal power, providing simulation basis for improving indoor positioning algorithms and avoiding the waste of manpower and resources from repeated field measurements.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] A pseudo-satellite indoor positioning modeling and simulation method includes the following steps:

[0007] Step 1: Read in the indoor 3D map data. The ground pseudo-satellite base station adopts the adaptive gridding method with the minimum GDOP value criterion and the maximum field strength coverage criterion to calculate the optimal deployment scheme of the ground base station.

[0008] Step 2: Based on the optimal deployment scheme coordinates of the pseudo-satellite base station generated in Step 1 and the preset receiving path or area coordinate set, a three-dimensional ray tracing algorithm is used to simulate point-to-point multipath propagation parameters to obtain the simulated observations of the ground base station containing multipath information at the receiver, including pseudorange, carrier phase value, Doppler observation value, and carrier-to-noise ratio.

[0009] Step 3: Perform data quality analysis on the simulation observations generated in Step 2, and statistically analyze the total number of epochs, completeness rate, mean signal-to-noise ratio, mean and standard deviation of pseudorange residuals, and mean and standard deviation of carrier residuals. Substitute these values ​​into the positioning algorithm to calculate the location and simulate the indoor positioning effect.

[0010] Furthermore, the specific method of step 1 is as follows:

[0011] 1.1 Read or construct 3D map data containing configuration and material information, divide the service area into unit grids according to the actual spatial size. If it is a multi-story building, first divide the interior building into layers, set horizontal and vertical intervals on each layer, and then grid each layer of the service area according to the intervals to obtain the area grid points.

[0012] 1.2 According to the pseudo-satellite positioning algorithm, the optimal positioning accuracy is achieved when the number of positioning base stations is greater than 4, which is reflected in the minimum GDOP value. The optimal network deployment model is: min(GDOP). Solving the optimal model yields the best conditions for the base station observation azimuth and elevation angles.

[0013]

[0014]

[0015] The receiver at the observation station is located at the origin of the coordinate system, and the horizontal azimuth and elevation angles of the observed pseudo-satellite are A and A, respectively. i and E i Each base station follows an orthogonal uniform distribution. Based on the principle of minimizing the number of base stations, the grid points are traversed to generate a possible set of base station distribution coordinates.

[0016] 1.3 Based on the possible pseudo-satellite base station distribution coordinate set generated in step 1.2, the ray tracing algorithm is used to calculate the distribution of received signal field strength in the service area one by one, and the set of base station coordinates with the widest signal field strength coverage is selected as the optimal deployment scheme for pseudo-satellite base stations.

[0017] Further, in step 2, a three-dimensional ray tracing algorithm is used to simulate point-to-point multipath propagation parameters, obtaining simulated observations of the ground base station containing multipath information at the receiver, including the following steps:

[0018] Calculate the direction of the signal reaching the receiving point:

[0019]

[0020]

[0021] Where, θ i and φ i G represents the direction of arrival of the i-th signal path. θ (θ,φ) is the θ component of the receiving antenna gain. It is the relative phase of the far-field electric field θ component, G φ (θ,φ) is the φ component of the receiving antenna gain. It is the relative phase of the φ component of the far-field electric field;

[0022] Let β be the transmitted signal S T (f) frequency band and receiver S R (f) The overlapping portion of the received frequency bands is:

[0023]

[0024] Among them, f T and B T These are the center frequency and bandwidth of the transmitted waveform, respectively; S T (f) and S R (f) is calculated as follows:

[0025]

[0026] Where f0 is the center frequency and B is the bandwidth;

[0027] Calculate the average power P of the i-th path. i :

[0028]

[0029] Where λ is the wavelength, η0 is the impedance of free space, and E θ,i and E φ,i These are the electric field components θ and φ of the i-th path at the receiving point, where θ i and φ i Indicates the direction of arrival for the i-th path;

[0030] The received carrier-to-noise ratio at the receiving point R is calculated as follows:

[0031]

[0032] Where C is the received signal power, N0 is the noise power spectral density, and N is the total number of arrival paths at the receiving point;

[0033] The arrival time for each propagation path is:

[0034]

[0035] Among them, L i is the total geometric path length, and c is the speed of light in free space;

[0036] Using the carrier phase positioning method, the distance between the pseudo-satellite s and the receiver r is calculated as follows:

[0037]

[0038] Where λ is the wavelength. and These are the carrier phases at the transmitting and receiving ends, respectively.

[0039] Let the carrier phase difference at any two receiving points be... If the time difference of arrival is Δt, then the distance difference between the pseudosatellite s and the two points is:

[0040]

[0041] The carrier phase difference is:

[0042]

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] This invention utilizes the principles of minimizing GDOP value and maximizing field strength coverage to determine an optimized deployment scheme for pseudo-satellite base stations. It then uses an indoor multipath simulation unit to calculate indoor signal propagation based on a pre-input or modeled indoor 3D environment. Finally, it uses a raw observation simulation unit to output a raw observation simulation dataset. After analyzing the observation data quality, the dataset is input into a positioning algorithm to complete the positioning process. This achieves simulation modeling of indoor pseudo-satellite signal characteristics and base station deployment, providing simulation basis for improving indoor positioning algorithms and simulation support for subsequent indoor positioning system methods. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the implementation system of a pseudo-satellite indoor positioning modeling and simulation method in an embodiment of the present invention.

[0046] Figure 2 This is a flowchart illustrating the deployment simulation of pseudo-satellite base stations in an embodiment of the present invention.

[0047] Figure 3This is a flowchart of indoor multipath simulation in an embodiment of the present invention. Detailed Implementation

[0048] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings.

[0049] A pseudo-satellite indoor positioning modeling and simulation method, such as Figure 3 As shown, the process of this method is as follows:

[0050] Step 1: Input an existing map or perform environmental modeling. After the base station network optimization unit reads the indoor 3D map data, it generates a deployment plan and its signal strength spatial distribution file. Figure 2 The simulation process for base station network optimization is shown. After configuring indoor spatial geographic information data, base station information, and antenna information, observation equations are established, and network optimization design is carried out. According to the precision attenuation factor optimization model, the grid points are traversed to provide an optimal deployment scheme set. Based on the principle of the widest field strength coverage, the ray tracing algorithm is used to calculate and select the deployment scheme with the most visible base stations and the highest average signal strength of the visible base stations at the grid points. At the same time, the field strength distribution file under this scheme is generated.

[0051] The network optimization design process is as follows:

[0052] (1) Construction of observation equations

[0053] Assume the receiver of the observation station is located at the origin O of the coordinate system, and the horizontal azimuth and elevation angles of the pseudo-satellite base station S are A and B, respectively. i and E i Therefore, the direction cosine vector (l) of the line-of-sight vector OS can be calculated. i ,m i ,n i As shown below:

[0054] l i =cosE i cosA i

[0055] m i =cosE i sinA i

[0056] n i =sinE i

[0057] The station observation equation is:

[0058] V = HX - L

[0059] V is the observation residual vector, and L is the difference between the observed value and the approximate value. H is the design matrix, and its row vectors are:

[0060] h i =(l i ,m i ,n i ,1)

[0061] The covariance matrix representing the positioning accuracy is:

[0062] Q=(H T H) -1

[0063] The formula for calculating the precision factor is:

[0064] VDOP=Q 3,3

[0065]

[0066]

[0067] (2) Optimization of network layout

[0068] The design of a pseudo-satellite base station network must consider both positioning accuracy and cost-effectiveness. According to the pseudo-satellite positioning principle, the minimum number of positioning base stations should be greater than 4. Under the premise of cost-effectiveness, achieving optimal positioning accuracy is achieved by minimizing the GDOP value. Therefore, the optimal network deployment model is:

[0069] min(GDOP)

[0070] Solving the optimization model yields the optimal conditions for the base station observation azimuth and elevation angles:

[0071]

[0072]

[0073] The optimal azimuth and elevation angles for base station deployment are obtained by solving the optimization model. Based on the optimization model, the HDOP values ​​for different numbers of base stations are simulated to select a suitable scheme.

[0074] (3) Performance Analysis

[0075] Based on the actual spatial dimensions, the service area is divided into unit grids. If it is a multi-story building, the interior architecture needs to be divided into layers, with horizontal and vertical intervals set at 1m intervals on each layer. Each layer of the service area is then gridded according to these intervals to obtain regional grid points. Each grid point is identified as an observation unit, providing three-dimensional coordinates.

[0076] Based on the base station distribution and antenna parameters, grid points are traversed with a lower elevation angle limit of 15° to establish observation equations, calculate precision factor values ​​such as GDOP, HDOP, and VDOP, and store and visualize them. Using the base map as the underlying layer, new GDOP, HDOP, and VDOP layers are created and overlaid to display the distribution of precision factors. The possible base station distribution coordinate set is generated based on the minimum GDOP value.

[0077] Step 2: The indoor multipath simulation unit sets the coordinates of the pseudo-satellite transmitter based on the deployment scheme obtained in Step 1. Using the indoor environment map, it performs point-to-point and point-to-multipoint wireless propagation calculations using ray tracing to obtain parameters such as wireless propagation loss between transceivers, multipath arrival path / time / angle, etc. This yields the simulated observation power, carrier-to-noise ratio, propagation arrival time, and carrier phase difference of the pseudo-satellite base station at the receiver. This includes the following steps:

[0078] Assume θ i and φ i Characterize the direction of arrival of the i-th signal path, and calculate the direction of the signal arriving at the receiving point:

[0079]

[0080]

[0081] Among them G θ It is the θ component of the receiving antenna gain. It is the relative phase of the θ component of the far-field electric field. G φ It is the φ component of the receiving antenna gain. It is the relative phase of the φ component of the far-field electric field.

[0082] β is the transmitted signal spectrum S T (f) and receiver frequency sensitivity spectrum S R The overlapping portion of (f) can be set according to the actual situation in the simulation:

[0083]

[0084] Where f T and B T These are the center frequency and bandwidth of the transmitted waveform, respectively.

[0085] Pseudo-satellite signals are narrowband signals. Narrowband waveforms are always assumed to have a flat distribution, meaning that theoretically the spectrum S(f) is:

[0086]

[0087] Where f0 is the center frequency and B is the bandwidth.

[0088] Calculate the average power P of the i-th path.i :

[0089]

[0090] Where λ is the wavelength, η0 is the impedance of free space, and E θ,i and E φ,i These are the electric field components θ and φ of the i-th path at the receiving point, where θ i and φ i This indicates the direction of arrival for the i-th path.

[0091] The received carrier-to-noise ratio at the receiving point R is calculated as follows:

[0092]

[0093] Where C is the received signal power, N0 is the noise power spectral density, and N is the total number of arrival paths at the receiving point.

[0094] The arrival time for each propagation path is:

[0095]

[0096] Among them, L i is the total geometric path length, and c is the speed of light in free space.

[0097] For carrier phase positioning, the distance between the pseudo-satellite s and the receiver r can be described by the following formula:

[0098]

[0099] Where λ is the wavelength. and These are the carrier phases at the transmitting and receiving ends, respectively. Considering the influence of clock difference, using the carrier phase difference is more accurate. Let the carrier phase difference at any two receiving points be... If the time difference of arrival is Δt, then the distance difference between the pseudosatellite s and the two points is:

[0100]

[0101] The carrier phase difference is:

[0102]

[0103] Step 5: The measurement data quality analysis unit reads in the pseudo-satellite base station observation data and base station coordinate true values ​​generated in Step 2, performs data quality analysis and statistics such as total epoch count, completeness rate, mean signal-to-noise ratio, mean and standard deviation of pseudorange residuals, and mean and standard deviation of carrier residuals, and inputs them into the positioning algorithm for positioning calculation.

[0104] To implement this method, a construction such as Figure 1 The system shown includes a base station network optimization simulation unit, an indoor multipath simulation unit, and a measurement data quality analysis unit, which realizes simulation testing and evaluation of the entire process of indoor pseudo-satellite station deployment and positioning.

[0105] In summary, this invention utilizes a pseudo-satellite optimal deployment optimization algorithm to generate an optimized deployment scheme for indoor pseudo-satellite base stations. An indoor multipath simulation unit performs indoor signal propagation calculations based on a pre-input or modeled indoor 3D environment. The generated raw observations are input into an observation data quality analysis unit for data quality analysis, and then input into a positioning algorithm to complete the positioning process. This achieves simulation modeling of indoor pseudo-satellite positioning signal characteristics and base station deployment. It can analyze the impact of factors such as pseudo-satellite base station deployment, indoor spatial layout, and signal power, providing a simulation basis for improving indoor positioning algorithms and avoiding the waste of manpower and resources caused by repeated field measurements.

[0106] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any transformations or substitutions that can be conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A pseudolite indoor positioning modeling and simulation method, characterized in that, Includes the following steps: Step 1: Read in the indoor 3D map data. The ground pseudo-satellite base station adopts the adaptive gridding method with the minimum GDOP value criterion and the maximum field strength coverage criterion to calculate the optimal deployment scheme of the ground base station. Step 2: Based on the optimal deployment coordinates of the pseudo-satellite base station generated in Step 1 and the preset receiving path or area coordinate set, a three-dimensional ray tracing algorithm is used to simulate point-to-point multipath propagation parameters, obtaining the simulated observations of the ground base station containing multipath information at the receiver, including pseudorange, carrier phase value, Doppler observation value, and carrier-to-noise ratio; including the following steps: Calculate the direction of the signal reaching the receiving point: ； ； wherein and the direction of arrival of the i-th signal path is denoted by is the component of the receive antenna gain, is the relative phase of the far-zone electric field component, is the component of the receive antenna gain, is the relative phase of the far-zone electric field component; set up To transmit signals frequency band and receiver The overlapping portion of the received frequency bands is: ； in, and These are the center frequency and bandwidth of the transmitted waveform, respectively. and The calculation method is as follows: ； in, B is the center frequency, and B is the bandwidth. Calculate the average power of the i-th path : ； in, It's the wavelength. It is the impedance of free space. and It is the i-th path of the receiving point and electric field components, and Indicates the direction of arrival for the i-th path; The received carrier-to-noise ratio at the receiving point R is calculated as follows: ； in, It is the received signal power. Where is the noise power spectral density, and N is the total number of arrival paths at the receiving point; The arrival time for each propagation path is: ； in, It is the total geometric path length. It is the speed of light in free space; Using carrier phase positioning method to calculate pseudo-satellites With receiver The distance between them is: ； in, It's the wavelength. and These are the carrier phases at the transmitting and receiving ends, respectively. Let the carrier phase difference at any two receiving points be... The arrival time difference is Then the distance difference between the pseudo-satellite s and the two points is: ； The carrier phase difference is: ； Step 3: Perform data quality analysis on the simulation observations generated in Step 2, and statistically analyze the total number of epochs, completeness rate, mean signal-to-noise ratio, mean and standard deviation of pseudorange residuals, and mean and standard deviation of carrier residuals. Substitute these values ​​into the positioning algorithm to calculate the location and simulate the indoor positioning effect.

2. The pseudo-satellite indoor positioning modeling and simulation method according to claim 1, characterized in that, The specific method for step 1 is as follows: Step 1.1: Read or construct 3D map data containing configuration and material information. Divide the service area into unit grids according to the actual spatial dimensions. If it is a multi-story building, first divide the interior building into layers, set horizontal and vertical intervals on each layer, and then grid each layer of the service area according to the intervals to obtain the area grid points. Step 1.2: According to the pseudo-satellite positioning algorithm, the number of positioning base stations is greater than 4; the positioning accuracy is optimal, which is reflected in the minimum GDOP value. The optimal network deployment model is as follows: Solve the optimization model to obtain the optimal conditions for the base station observation azimuth and elevation angles: ； The receiver at the observation station is located at the origin of the coordinate system, and the horizontal azimuth and elevation angles of the observed pseudo-satellite are respectively... and Each base station follows an orthogonal uniform distribution. Based on the principle of minimizing the number of base stations, the grid points are traversed to generate a possible set of base station distribution coordinates. Step 1.3: Based on the possible pseudo-satellite base station distribution coordinate set generated in Step 1.2, the ray tracing algorithm is used to calculate the distribution of received signal field strength in the service area one by one, and the set of base station coordinates with the widest signal field strength coverage is selected as the optimal deployment scheme for pseudo-satellite base stations.

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