Moving target recognition model algorithm and system based on Beidou smart map

Abstract

The invention discloses a moving target recognition model algorithm and system based on a Beidou smart map, and the model algorithm comprises the following steps: S1, positioning a moving target based on a pseudo-range positioning method in combination with a Kalman filtering method; the method comprises the following steps: 1, calculating satellite ascending node angular distance and position; 2, according to the category of the pseudo-range signal, judging whether noise eta j contained in the pseudo-range signal in a moving target pseudo-range positioning equation is added or not; s2, time calibration and distance correction are carried out on the latitude and longitude coordinate data after Kalman filtering processing, and the accuracy of target identification is improved; s3, unifying coordinates of the moving target and the Beidou satellite signal, and determining troposphere delay and ionosphere propagation delay; s4, establishing a moving target real-time observation equation according to the steps S1 to S3; the method has the beneficial effects that the non-line-of-sight deviation pseudo-range signal is introduced on the basis of Kalman filtering, and the target recognition precision is improved.

Description

Technical Field

[0001] The present invention relates to the field of satellite positioning technology, and in particular to a mobile target recognition model algorithm and system based on Beidou smart map. Background Art

[0002] Satellite navigation is a technology that can be used to locate users on the ground, sea and in space. It is composed of navigation satellites, ground stations and positioning equipment. Satellite navigation usually includes Beidou navigation. This type of navigation technology has the characteristics of continuity, persistence and real-time, and can meet two-dimensional and three-dimensional positioning needs. When satellite navigation is performing positioning navigation, the signal will be affected by various error factors from the time it is output from the satellite to the time it is received by the user, resulting in random errors in the observation quantity. This error will directly affect the positioning accuracy of the satellite navigation. Therefore, in the application process of satellite navigation, how to reduce its positioning error and improve its positioning accuracy has become a key issue of concern in its application.

[0003] At present, the positioning algorithm is usually based on the least squares method, combined with the nonlinear filtering algorithm and its extended algorithm to establish an observation model, but it is only a local observation of nonlinearity, usually only considering the clock problem, without introducing other factors, such as offset angle, transmission process noise, etc., and it does not completely and systematically solve the nonlinear filtering problem. In addition, due to the algorithm analysis logic problem, insufficient analysis occurs, which directly reduces the signal resolution and reduces the target recognition accuracy. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a mobile target recognition model algorithm and system based on Beidou smart map, which is used to solve the technical problem that the prior art only considers the clock problem without introducing other factors.

[0005] To achieve the above object, the present invention provides a mobile target recognition model algorithm based on Beidou smart map, comprising the following steps:

[0006] S1: Positioning mobile targets based on pseudo-range positioning method combined with Kalman filter method;

[0007] Step 1: Calculate the angular distance and position of the satellite ascending node;

[0008] Step 2: According to the pseudorange signal category, determine whether to add the noise η contained in the pseudorange signal to the pseudorange positioning equation of the mobile target j ;

[0009] S2: Perform time calibration and distance correction on the longitude and latitude coordinate data processed by Kalman filtering to improve the accuracy of target recognition;

[0010] S3: Unify the coordinates of the moving target and BeiDou satellite signals, and determine the tropospheric delay and ionospheric propagation delay;

[0011] S4: According to the above S1-S3, a real-time observation equation for a moving target is established, which is:

[0012] p′=p n +c*Δt-I n -T n +ε i NLOS +η j +η;

[0013] Among them, p n′ is the corrected pseudorange positioning equation: (x′ j , y′ j , z′ j ) is the three-dimensional space coordinate after satellite correction, (x′, y′, z′) is the updated moving target coordinate;

[0014] c is the speed of light;

[0015] Δt is the clock deviation between the mobile target and the satellite;

[0016] I n It is the correction value actually corresponding to the propagation delay of satellite signal in the ionosphere;

[0017] T n is the correction value actually corresponding to the propagation delay of satellite signals in the troposphere;

[0018] is the non-path delay in signal propagation;

[0019] η j Indicates the noise contained in the obtained pseudorange signal;

[0020] η is the updated moving target transfer noise.

[0021] Further: the specific method of step one is:

[0022] Get the satellite signal transmission time t s , t s =t r -p / c;

[0023] Among them, t r is the time when the mobile target receives the signal, p is the pseudo-range observation value, and c is the speed of light;

[0024] The satellite signal transmission time t s Normalized processing to get t k ;

[0025] According to t k Calculate the mean anomaly angle M k and the eccentric anomaly angle E k , and further obtain the true anomaly angle f k Angular distance from ascending node

[0026] Among them, M k The value of must be between 0 and 2π. If it is not in this range, it will be processed by ±2π to ensure that it is in the range of 0 to 2π.

[0027] According to the angular distance of the ascending node The vector radius and ascending node angular distance of the Beidou satellite are corrected, as shown in the following formula:

[0028]

[0029] r k ′=rk+δ r ;

[0030] Among them, δ u is the angular distance correction term of the ascending node of the Beidou satellite,

[0031] C uc is the cosine correction of the satellite latitude argument;

[0032] r k is the vector radius of the satellite, r k =λ(1-ecosE k ), λ is the square root of the semi-major axis of the satellite orbit, and e is the eccentricity of the satellite orbit;

[0033] δr is the vector radius correction term of the Beidou satellite, C rc is the cosine correction of the satellite orbit radius;

[0034] Finally get t k The satellite's position coordinates on the plane at this moment: This can directly obtain the three-dimensional spatial coordinates of the satellite in the geodetic coordinate system (x j ,y j , z j ).

[0035] Further: the specific method of step 2 is:

[0036] The pseudorange positioning equation of the non-line-of-sight deviation pseudorange signal is established as follows:

[0037]

[0038] Among them, (x, y, z) represents the three-dimensional space coordinates of the moving target;

[0039] (x j ,y j , z j ) represents the three-dimensional spatial coordinates of the satellite;

[0040] t r The time at which the signal is received for a moving target;

[0041] η j Indicates the noise contained in the pseudorange signal obtained. In special cases, it is negligible, that is, 0. The special case is defined as follows:

[0042] The support vector machine algorithm is used to classify the pseudo-range signal into line-of-sight signal LOS and non-line-of-sight signal NLOS;

[0043] According to the judgment of the decision function f(x), the signal is classified. Specifically, if f(x)>0, it means that the signal is a line-of-sight signal LOS. At this time, η j Take 0. If f(x)<0, it means the signal is a non-line-of-sight signal NLOS.

[0044] Further: S2 specifically includes:

[0045] 1. Update noise: The clock deviation Δt between the mobile target and the satellite is obtained by the following formula:

[0046] Δt=(t i - oe )+a(t j -t oe )+a1(t j -t oe ) 2 ;

[0047] Among them, t i is the satellite clock time;

[0048] t oe is the moving target clock time;

[0049] a is the satellite clock speed;

[0050] a1 is the rate of change of the satellite clock speed;

[0051] Then, the recursive formula of the moving target coordinates (x, y, z) at any time v is constructed by combining Δt, frequency drift Δf and the state transfer matrix of the satellite, and the observation time interval T is solved by the recursive formula;

[0052] Then, according to the observation time interval T, the satellite state transfer noise covariance matrix is ​​established to calculate the moving target transfer noise η;

[0053] 2. Update coordinates: Construct the positioning equations between the mobile target and the N satellites received:

[0054] Calculate the measurement variance σ of the positioning equations 2 The pseudo-range measurement covariance matrix R space is established as shown in the following formula:

[0055]

[0056] Among them, θ N is the satellite altitude angle corresponding to the N satellites received;

[0057] Embed the inner product formula of Lorentz space distance in R space, set the result of the inner product formula to 0, and combine the pseudo-range equations of the N satellites received to establish a matrix W consisting of the mobile target coordinate vector, the mobile target receiving signal clock difference and the satellite clock difference;

[0058] Then establish an n*N dimensional B matrix, where n is the total number of observation equations, that is, the number of mobile target observation equations plus the number of satellite observation equations; N is the number of satellites;

[0059] Setting up the equation

[0060] Among them, Z is the N-dimensional matrix constructed by the moving target coordinate vector and the moving target observation equation;

[0061] M is the third-order diagonal matrix of the inner product formula of the Lorentz space distance;

[0062] τ is the N*1-dimensional lateral quantity;

[0063] By solving this equation, we can find that there are two different solutions for W. The two solutions are respectively substituted into the pseudorange positioning equation. If only one of the solutions satisfies the pseudorange positioning equation, the coordinates of the moving target can be updated to (x′, y′, z′).

[0064] Further: S3 specifically includes:

[0065] According to the earth's rotation angular velocity ω, the satellite signal transmission time t s and the time t at which the mobile target receives the signal r Determine the Earth's rotation angle and make corrections for the Earth's rotation, as shown below:

[0066]

[0067] Among them, (x j ,y j , z j ) represents the three-dimensional spatial coordinates of the satellite before correction;

[0068] (x′ j , y′ j , z′ j ) is the three-dimensional space coordinate after satellite correction;

[0069] α is the angle of the earth's rotation, that is, α=ω*(t r -t s );

[0070] The Klobuchar model is used to correct the ionospheric delay according to the Holt exponential smoothing model and the dual-frequency observation model. The Klobuchar model and the dual-frequency observation model respectively calculate the total electron content of the ionosphere in the previous 6 days, where d is the time period of 4 hours, and 6 days is 24 hours. The Holt exponential smoothing model predicts the total electron content of the ionosphere in the previous 6 days calculated by the two models and makes a difference. The value of the Klobuchar model in the current period is predicted based on the difference, thereby determining the ionospheric propagation delay I corresponding to the free electron density in the ionosphere in the current period n ;

[0071] Then, according to the GMF tropospheric delay mapping function, the data provided by the Numerical Weather Forecast Center are analyzed to obtain the tropospheric delay T n .

[0072] The present invention also provides a mobile target identification system based on Beidou smart map, comprising: a terminal device;

[0073] The terminal device comprises: a memory, a processor, and a program stored in the memory and executable on the processor;

[0074] When the processor executes the program, the above-mentioned method and steps are implemented.

[0075] The invention has the beneficial effect of introducing a non-line-of-sight deviation pseudo-range signal on the basis of Kalman filtering, thereby improving the target recognition accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the embodiments of the present invention and the technical solutions in the prior art, the drawings required for use in the embodiments and the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some implementation methods recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0077] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0079] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can be applied to other similar scenarios based on these drawings without any creative work.

[0080] In addition, it can be understood that although the efforts made in this development process may be complex and lengthy, for ordinary technicians in the field related to the contents disclosed in this application, some design, manufacturing or production changes based on the technical contents disclosed in this application are just conventional technical means and should not be understood as the contents disclosed in this application are insufficient.

[0081] In order to better understand the solutions of the embodiments of the present application, some relevant terms and concepts that may be involved in the embodiments of the present application are first introduced below.

[0082] (1) Holt exponential smoothing model: The Holt exponential smoothing model uses two smoothing parameters (α and β) to control the smoothness of the level term and the trend term respectively. Specifically, the model consists of two main parts: the level term (Level) represents the current level of the time series, that is, the current average value; the trend term (Trend) represents the current trend of the time series, that is, the current slope.

[0083] (2) Dual-frequency observation model: Receive satellite signals of multiple frequencies at the same time, for example, receive signals from two frequency bands at the same time. By collecting the carrier phase and pseudorange observations of these different frequencies and combining them with precise ephemeris and satellite clock error data, the precise three-dimensional coordinates of any location in the world can be calculated.

[0084] (3) Klobuchar model: The Klobuchar model is an ionospheric delay model widely used in satellite navigation and positioning. The model assumes that free electrons are concentrated in a thin layer at an altitude of 350 kilometers and that the vertical ionospheric delay is a constant over a certain period of time. The daily variation of the model is represented by a half-sine wave plus a constant deviation.

[0085] Unless otherwise specified, all embodiments and optional embodiments of the present application can be combined with each other to form a new technical solution.

[0086] See also Figure 1 , the system flow chart of the present invention comprises the following steps:

[0087] S1: Positioning mobile targets based on pseudo-range positioning method combined with Kalman filter method;

[0088] Step 1: Calculate the angular distance and position of the satellite ascending node;

[0089] Step 2: According to the pseudorange signal category, determine whether to add the noise η contained in the pseudorange signal to the pseudorange positioning equation of the mobile target j ;

[0090] S2: Perform time calibration and distance correction on the longitude and latitude coordinate data processed by Kalman filtering to improve the accuracy of target recognition;

[0091] S3: Unify the coordinates of the moving target and BeiDou satellite signals, and determine the tropospheric delay and ionospheric propagation delay;

[0092] S4: Establish the real-time observation equation of the moving target according to the above S1-S3.

[0093] Specifically:

[0094] In step 1, in order to obtain the angular distance and position of the satellite ascending node, an initial pseudorange observation equation p is established n Provide to step 2; first, obtain the satellite signal transmission time t s , t s =t r -p / c; where t r The time p when the mobile target receives the signal is the pseudo-range observation value, c is the speed of light; the satellite signal transmission time t s Normalized processing to get t k According to t k Calculate the mean anomaly angle M k and the eccentric anomaly angle E k , and further obtain the true anomaly angle f k Angular distance from ascending node Among them, M k The value must be between 0 and 2π. If it is not in this range, it will be processed by ±2π to ensure that it is in the range of 0 to 2π. The vector radius and ascending node angular distance of the Beidou satellite are corrected, as shown in the following formula:

[0095]

[0096] r k ′=r k +δ r ;

[0097] Among them, δu is the angular distance correction term of the ascending node of the Beidou satellite,

[0098] C uc is the cosine correction of the satellite latitude argument;

[0099] r k is the vector radius of the satellite, r k =λ(1-ecosE k ), λ is the square root of the semi-major axis of the satellite orbit, and e is the eccentricity of the satellite orbit;

[0100] δ r is the vector radius correction term of the Beidou satellite,

[0101] C rc is the cosine correction of the satellite orbit radius;

[0102] Finally get t k Satellite position at this moment: This can be brought into the pseudorange observation equation of step 2: n .

[0103] Step 2: Introduce the deviation, that is, the non-path delay and noise in signal transmission, and combine the pseudo-range correction formula to establish the pseudo-range equation and carrier phase equation group as follows:

[0104]

[0105] Among them, p n Uncorrected pseudorange observation equation;

[0106] I is the correction corresponding to the propagation delay of the satellite signal in the ionosphere;

[0107] T is the correction corresponding to the propagation delay of the satellite signal in the troposphere;

[0108] is the noise in the pseudo-range measurement process;

[0109] is the noise in the carrier phase measurement process;

[0110] The above equations are processed by adding them to eliminate I and T, and the pseudorange signal noise η is further obtained. j , The pseudo-range positioning equation of the moving target is corrected according to the signal, as shown below:

[0111]

[0112] Among them, (x, y, z) represents the three-dimensional space coordinates of the moving target;

[0113] (x j ,y j , z j ) represents the three-dimensional space coordinates of the satellite;

[0114] t r The time at which the signal is received for a moving target;

[0115] η j Indicates the noise contained in the obtained pseudorange signal;

[0116] To obtain the noise η contained in the pseudorange signal j , according to the vector machine algorithm, the line-of-sight signal LOS and the non-line-of-sight signal NLOS are classified, and the classification is shown in the following formula:

[0117]

[0118] Where sgn represents the sign function;

[0119] α′ represents the decision bias;

[0120] y i represents the decision vector;

[0121] b′ represents the bias;

[0122] K(X,X j ) represents the kernel function, and the kernel function uses the radial basis function as the kernel function, as shown in the following formula:

[0123]

[0124] Where X is the three-dimensional space coordinate of the moving target;

[0125] X j is the three-dimensional space coordinate of the satellite;

[0126] exp is the exponential function;

[0127] σ is the kernel width;

[0128] Since the line-of-sight signal is ultra-short wave or microwave communication, the influence of the line-of-sight signal can be ignored in satellite target positioning, while the change of the moving target position may bring the influence of non-line-of-sight signal. Therefore, according to the judgment of the decision function f(x), the signal is classified. Specifically, when f(x)>0, it means that the signal is a line-of-sight signal LOS, η j =0, if f(x)<0, it means the signal is a non-line-of-sight signal NLOS.

[0129] Then, the longitude and latitude coordinate data processed by Kalman filtering are time-calibrated and distance-corrected to improve the accuracy of target recognition.

[0130] The first step is to update the moving target transfer noise with time, as follows:

[0131] The clock deviations Δt and Δt between the mobile target and the satellite are obtained by the following formula:

[0132] Δt=(t i -t oe )+a(t j -t oe )+a1(t j -t oe ) 2 ;

[0133] Among them, t i is the satellite clock time; t oe is the clock time of the mobile target; a is the satellite clock speed; a1 is the rate of change of the satellite clock speed; then, combining Δt, frequency drift Δf and the state transfer matrix of the satellite, the recursive formula of the mobile target coordinates (x, y, z) at any time v is constructed;

[0134] The frequency drift Δf is obtained by the following method:

[0135] Define an initial time t, sample the frequency value f0 at the initial time, and sample the frequency value f of the subsequent N satellite times i (i∈N) and N moving target moments f′ i , sample all the frequency values ​​and record the interval time τ of each sampling i , organized into the following matrix form:

[0136]

[0137] Δf=|Δf1+Δf2|;

[0138] The recursive formula is to reflect the corresponding continuous observation time interval of a mobile target v in the state of continuous action, which is constructed by two adjacent mobile target state matrices as shown below:

[0139]

[0140] Where T represents the observation time interval; then the satellite state transition noise covariance matrix is ​​established according to the observation time interval T, as shown in the following formula:

[0141]

[0142] Where X is the three-dimensional space coordinate of the moving target; X f is the moving target clock frequency; η is the updated moving target transfer noise;

[0143] Step 2: Update the moving target coordinates with the distance, as follows:

[0144] Update coordinates: Measure the distance between the mobile target and the N satellites received to form a positioning equation group as shown below:

[0145]

[0146] Among them, (x, y, z) represents the three-dimensional space coordinates of the moving target;

[0147] (x N ,y N , z N ) represents the three-dimensional space coordinates corresponding to the received N satellites;

[0148] c represents the speed of light;

[0149] Calculate the measurement variance σ of the positioning equations 2 The pseudo-range measurement covariance matrix is ​​established as shown in the following formula:

[0150]

[0151] Among them, θ N is the satellite altitude angle corresponding to the N satellites received;

[0152] Embed the inner product formula of Lorentz space distance in R space, set the result of the inner product formula to 0, and combine the pseudo-range equations of the N satellites received to establish a matrix W consisting of the mobile target coordinate vector, the mobile target receiving signal clock difference and the satellite clock difference;

[0153] Then establish an n*N dimensional B matrix, where n is the total number of observation equations, that is, the number of mobile target observation equations plus the number of satellite observation equations;

[0154] N is the number of satellites;

[0155] Setting up the equation

[0156] Among them, Z is the N-dimensional matrix constructed by the moving target coordinate vector and the moving target observation equation;

[0157] M is the third-order diagonal matrix of the inner product formula of the Lorentz space distance;

[0158] τ is the N*1-dimensional lateral quantity;

[0159] By solving this equation, we can find that there are two different solutions for W. The two solutions are respectively substituted into the pseudorange positioning equation. If only one of the solutions satisfies the pseudorange positioning equation, the coordinates of the moving target can be updated to (x′, y′, z′).

[0160] Then, the coordinates of the mobile target and BeiDou satellite signals are unified to determine the tropospheric delay and ionospheric propagation delay as follows:

[0161] Correct the satellite's three-dimensional spatial coordinates: according to the earth's rotation angular velocity ω and the satellite signal transmission time t s and the time t at which the mobile target receives the signal r Determine the Earth's rotation angle and make corrections for the Earth's rotation, as shown below:

[0162]

[0163] Among them, (x j ,y j , z j ) represents the three-dimensional spatial coordinates of the satellite before correction;

[0164] (x′ j , y′ j , z′ j ) is the three-dimensional space coordinate after satellite correction;

[0165] α is the angle of the earth's rotation, that is, α=ω*(t r -t s );

[0166] Determine the ionospheric propagation delay: The Klobuchar model is used to correct the ionospheric delay according to the Holt exponential smoothing model and the dual-frequency observation model. The Klobuchar model and the dual-frequency observation model respectively calculate the total electron content of the ionosphere in the previous 6 days, where d is the time period of 4 hours, and 6 days is 24 hours. The Holt exponential smoothing model predicts the total electron content of the ionosphere in the previous 6 days calculated by the two models and makes a difference. The value of the Klobuchar model in the current period is predicted based on the difference, thereby determining the ionospheric propagation delay I corresponding to the free electron density in the ionosphere in the current period n ;

[0167] Determine the tropospheric delay: Analyze the data provided by the Numerical Weather Forecast Center according to the GMF tropospheric delay mapping function to obtain the tropospheric delay T n .

[0168] Finally, each correction data is imported into the real-time observation equation of the moving target, which is:

[0169] p′=p n′ +c*Δt-I n -T n +ε i NLOS +η j +η;

[0170] Among them, p n′ is the corrected pseudorange positioning equation:

[0171] (x′ j , y′ j , z′ j ) is the three-dimensional space coordinate after satellite correction, (x′, y′, z′) is the updated moving target coordinate;

[0172] c is the speed of light;

[0173] Δt is the clock deviation between the mobile target and the satellite;

[0174] I n It is the correction value actually corresponding to the propagation delay of satellite signal in the ionosphere;

[0175] T n is the correction value actually corresponding to the propagation delay of satellite signals in the troposphere;

[0176] is the non-path delay in signal propagation;

[0177] η j Indicates the noise contained in the obtained pseudorange signal;

[0178] η is the updated moving target transfer noise.

[0179] This allows the moving target to be identified.

[0180] The above is a very detailed description of one or more embodiments of the present invention, but the content is only a specific example of the present invention and cannot be considered to limit the scope of application of the present invention. Any other methods and changes proposed based on the content of the present invention should fall within the scope of patent protection of the present invention.

Claims

1. A mobile target recognition model algorithm based on Beidou smart map, characterized in that: The steps include: S1: Positioning mobile targets based on pseudo-range positioning method combined with Kalman filter method; Step 1: Calculate the angular distance and position of the satellite ascending node; Step 2: According to the pseudorange signal category, determine whether to add the noise η contained in the pseudorange signal to the pseudorange positioning equation of the mobile target j ; S2: Perform time calibration and distance correction on the longitude and latitude coordinate data processed by Kalman filtering to improve the accuracy of target recognition; S3: Unify the coordinates of the moving target and BeiDou satellite signals, and determine the tropospheric delay and ionospheric propagation delay; S4: According to the above S1-S3, a real-time observation equation for a moving target is established, which is: p′=p n′ +c*Δt-I n -T n +e i NLOS +n j +n; Among them, p n′ is the corrected pseudorange positioning equation: (x′ j , y′ j , z′ j ) is the three-dimensional space coordinate after satellite correction, (x′, y′, z′) is the updated moving target coordinate; c is the speed of light; Δt is the clock deviation between the mobile target and the satellite; I n It is the correction value actually corresponding to the propagation delay of satellite signal in the ionosphere; T n is the correction value actually corresponding to the propagation delay of satellite signals in the troposphere; is the non-path delay in signal propagation; η j Indicates the noise contained in the obtained pseudorange signal; η is the updated moving target transfer noise.

2. According to claim 1, a mobile target recognition model algorithm based on Beidou smart map is characterized in that: The specific method of step one is: Get the satellite signal transmission time t s , t s =t r -p / c; Among them, t r is the time when the mobile target receives the signal, p is the pseudo-range observation value, and c is the speed of light; The satellite signal transmission time t s Normalized processing to get t k ; According to t k Calculate the mean anomaly angle M k and the eccentric anomaly angle E k , and further obtain the true anomaly angle f k Angular distance from ascending node Among them, M k The value of must be between 0 and 2π. If it is not in this range, it will be processed by ±2π to ensure that it is in the range of 0 to 2π. According to the angular distance of the ascending node The vector radius and ascending node angular distance of the Beidou satellite are corrected, as shown in the following formula: r k =r k +d r ; Among them, δ u is the angular distance correction term of the ascending node of the Beidou satellite, C uc is the cosine correction of the satellite latitude argument; r k is the vector radius of the satellite, r k =λ(1-ecosE k ), λ is the square root of the semi-major axis of the satellite orbit, and c is the eccentricity of the satellite orbit; δ r is the vector radius correction term of Beidou satellite, C rc is the cosine correction of the satellite orbit radius; Finally get t k The satellite's position coordinates on the plane at this moment: This can directly obtain the three-dimensional spatial coordinates of the satellite in the geodetic coordinate system (x j ,y j , z j ).

3. The mobile target recognition model algorithm based on Beidou smart map according to claim 1 is characterized in that: The specific method of step 2 is: The pseudorange positioning equation of the non-line-of-sight deviation pseudorange signal is established as follows: Among them, (x, y, z) represents the three-dimensional space coordinates of the moving target; (x j ,y j , z j ) represents the three-dimensional space coordinates of the satellite; t r The time at which the signal is received for a moving target; η j Indicates the noise contained in the pseudorange signal obtained. In special cases, it is negligible, that is, 0. The special case is defined as follows: The support vector machine algorithm is used to classify the pseudo-range signal into line-of-sight signal LOS and non-line-of-sight signal NLOS; According to the judgment of the decision function f(x), the signal is classified. Specifically, if f(x)>0, it means that the signal is a line-of-sight signal LOS. At this time, η j Take 0. If f(x)<0, it means the signal is a non-line-of-sight signal NLOS.

4. The mobile target recognition model algorithm based on Beidou smart map according to claim 1 is characterized in that: The S2 specifically includes:

1. Update noise: The clock deviation Δt between the mobile target and the satellite is obtained by the following formula: Δt=(t i -t oe )+a(t j -t oe) +a1(t j -t oe ) 2 ; Among them, t i is the satellite clock time; t oe is the moving target clock time; a is the satellite clock speed; a1 is the rate of change of the satellite clock speed; Then, the recursive formula of the moving target coordinates (x, y, z) at any time v is constructed by combining Δt, frequency drift Δf and the state transfer matrix of the satellite, and the observation time interval T is solved by the recursive formula; Then, according to the observation time interval T, the satellite state transfer noise covariance matrix is ​​established to calculate the moving target transfer noise η; 2. Update coordinates: Construct the positioning equations between the mobile target and the N satellites received: Calculate the measurement variance σ of the positioning equations 2 The pseudo-range measurement covariance matrix R space is established as shown in the following formula: Among them, θ N is the satellite altitude angle corresponding to the N satellites received; Embed the inner product formula of Lorentz space distance in R space, set the result of the inner product formula to 0, and combine the pseudo-range equations of the N satellites received to establish a matrix W consisting of the mobile target coordinate vector, the mobile target receiving signal clock difference and the satellite clock difference; Then establish an n*N dimensional B matrix, where n is the total number of observation equations, that is, the number of mobile target observation equations plus the number of satellite observation equations; N is the number of satellites; Setting up the equation Among them, Z is the N-dimensional matrix constructed by the moving target coordinate vector and the moving target observation equation; M is the third-order diagonal matrix of the inner product formula of the Lorentz space distance; τ is the N*1-dimensional lateral quantity; By solving this equation, we can find that there are two different solutions for W. The two solutions are respectively substituted into the pseudorange positioning equation. If only one of the solutions satisfies the pseudorange positioning equation, the coordinates of the moving target can be updated to (x′, y′, z′).

5. The mobile target recognition model algorithm based on Beidou smart map according to claim 1 is characterized in that: The S3 specifically includes: According to the earth's rotation angular velocity ω, the satellite signal transmission time t s and the time t at which the mobile target receives the signal r Determine the Earth's rotation angle and make corrections for the Earth's rotation, as shown below: Among them, (x j ,y j , z j ) represents the three-dimensional spatial coordinates of the satellite before correction; (x′ j , y′ j , z′ j ) is the three-dimensional space coordinate after satellite correction; α is the angle of the earth's rotation, that is, α=ω*(t r -t s ); The Klobuchar model is used to correct the ionospheric delay according to the Holt exponential smoothing model and the dual-frequency observation model. The Klobuchar model and the dual-frequency observation model respectively calculate the total electron content of the ionosphere in the previous 6 days, where d is the time period of 4 hours, and 6 days is 24 hours. The Holt exponential smoothing model predicts the total electron content of the ionosphere in the previous 6 days calculated by the two models and makes a difference. The value of the Klobuchar model in the current period is predicted based on the difference, thereby determining the ionospheric propagation delay I corresponding to the free electron density in the ionosphere in the current period n ; Then, according to the GMF tropospheric delay mapping function, the data provided by the Numerical Weather Forecast Center are analyzed to obtain the tropospheric delay T n .

6. A mobile target recognition system based on Beidou smart map, characterized in that: include: A terminal device; The terminal device includes: a memory, a processor, and a program stored in the memory and executable on the processor; When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

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