Ambiguity resolving and positioning method based on double-difference phase observation

Abstract

The invention relates to an ambiguity resolving and positioning method based on double-difference phase observation, which belongs to the technical field of wireless positioning, and comprises the following steps: S1, selecting a frequency range of signal emission, and calculating a wavelength corresponding to each frequency point; s2, noise is introduced, and a simulation observation value is generated; s3, constructing a double-difference phase observation equation; s4, calculating a floating point solution by using a weighted least square method; s5, a certificate ambiguity solution is fixed, and position parameters are optimized; and S6, comparing the phase change of the target object at the continuous time points, and calculating the actual value of the displacement. According to the method, the ambiguity resolving complexity is reduced, and the calculation efficiency is improved; the noise robustness is enhanced, and the positioning precision in a complex environment is improved; a double-difference design matrix is optimized, and the system stability is improved; the positioning precision is improved, and the centimeter-level error range is realized; the method has the potential of expanding and adapting to 5G NR scenes.

Description

Technical Field

[0001] The invention belongs to the technical field of wireless positioning and relates to an ambiguity resolution and positioning method based on double-difference phase observation. Background Art

[0002] Wireless positioning technology is an important part of modern communication and navigation. It is widely used in the Global Navigation Satellite System (GNSS), Indoor Positioning System (IPS) and high-precision positioning systems based on the fifth generation new radio (5G NR) signal. With the continuous advancement of technology, positioning methods are evolving from traditional signal arrival time (TOF) and signal strength (RSS) measurements to high-precision ranging based on signal carrier phase observation. In GNSS, carrier phase observation has become the core technology for achieving centimeter-level accuracy due to its short wavelength and high accuracy. For example, real-time kinematic positioning (RTK) eliminates system errors through double difference observation to achieve centimeter-level accuracy. For indoor positioning, due to the significant multipath effect, traditional TOF and RSS methods are difficult to meet high-precision requirements. Multi-frequency carrier phase observation has become a research hotspot for achieving sub-meter or centimeter-level positioning. However, the high complexity of ambiguity resolution in complex environments and the susceptibility of the observation matrix to noise remain challenges, and more efficient and robust solutions are urgently needed.

[0003] At present, in high-precision positioning technology, ambiguity resolution of carrier phase observation is the key link to achieve high precision. Existing technical solutions mainly include the following methods. Teunissen PJG proposed an integer ambiguity resolution method in the conference paper "Least-square sestimation of the integer GPS ambiguities". By decorrelating the ambiguity parameters, the success rate of integer ambiguity fixation was improved. After being widely promoted, this method was named LAMBDA method. Vollath U et al. proposed a three-carrier ambiguity resolution (TCAR) method in the journal "Analysis of Three-Carrier Ambiguity Resolution Technique for Precise Relative Positioning in GNSS-2". It uses a combination of multi-frequency observations to gradually fix the ultra-wide lane, wide lane and narrow lane ambiguities to achieve fast resolution. With the evolution of GNSS integer ambiguity resolution technology, the Cascade Integer Resolution (CIR) method is also widely used in multi-frequency GNSS relative positioning technology. It is based on the integer guided estimation criterion, fixes the ambiguity step by step, and has a simple algorithm implementation and a high resolution success rate. In the following years, researchers gradually realized the limitations of the LAMBDA algorithm in some complex situations. In order to overcome these shortcomings, the MLAMBDA method came into being. It further improved the accuracy and reliability of ambiguity resolution by introducing technologies such as maximum likelihood estimation. Although these methods have achieved remarkable results in certain scenarios, there is still room for improvement in terms of ambiguity resolution complexity, noise robustness, and matrix rank optimization. First, the complexity of traditional ambiguity resolution methods is high in high-dimensional and multi-frequency scenarios, making it difficult to meet real-time requirements. Second, the double-difference design matrix is ​​prone to rank deficiency when the frequency or location points are improperly distributed, resulting in unstable solution results. In addition, existing technologies are not fully adapted to complex scenarios (such as 5G positioning and indoor positioning), resulting in unstable positioning performance or insufficient accuracy. The above problems seriously restrict the practical application of high-precision positioning technology in complex scenarios. Summary of the invention

[0004] In view of this, the purpose of the present invention is to address the problems of high complexity of ambiguity resolution, insufficient rank of double-difference design matrix and significant influence of noise interference on positioning accuracy in existing high-precision positioning technology, and propose an efficient ambiguity resolution and positioning method based on double-difference phase observation. Combining weighted least squares method, optimized MLAMBDA method and weighted processing of noise covariance matrix, high-precision positioning in complex scenarios is achieved.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] An ambiguity resolution and positioning method based on double-difference phase observation comprises the following steps:

[0007] S1: Select the frequency range of signal transmission and calculate the wavelength corresponding to each frequency point;

[0008] S2: introduce noise and generate simulated observations;

[0009] S3: Construct double difference phase observation equation;

[0010] S4: Calculate floating point solution using weighted least squares method;

[0011] S5: fix the certificate ambiguity solution and optimize the position parameters;

[0012] S6: Compare the phase changes of the target object at consecutive time points and calculate the actual value of the displacement.

[0013] Further, in step S1, 2.8 GHz to 2.89 GHz is selected as the frequency range of signal transmission, the transmission frequency step is set, and the frequency points are distributed at equal intervals to generate a frequency list; the wavelength corresponding to each frequency point is calculated one by one using the following formula, and the results are stored in the wavelength list;

[0014]

[0015] where f j is the frequency of the jth frequency point, c is the speed of light, λ j is the wavelength corresponding to the jth frequency.

[0016] Further, step S2 specifically includes the following steps:

[0017] Define the initial distance and the distance after each move, and generate a distance list;

[0018] Adding the initial distance to the beginning of the distance list;

[0019] Read the number of positions num_positions and the number of frequencies num_frequencies;

[0020] Initialize the phase matrix and define the noise level;

[0021] Each target position and frequency point is traversed, and for position i and frequency j, the theoretical phase value between the target and the receiver is calculated by the carrier phase formula, which is:

[0022]

[0023] where d i represents the actual distance between the target position i and the receiver, φ ij Represents the theoretical phase value corresponding to frequency j at target position i;

[0024] Gaussian noise is then superimposed to simulate the actual observed value, and finally the result is limited to the range of [-π, π] to form a winding phase.

[0025] Further, the step S3 of constructing the double difference phase observation equation includes the following steps:

[0026] Select the reference frequency and initialize the double difference observation value y dd and the design matrix A dd With G dd ; Traverse all pairs of adjacent positions and first calculate the phase difference between adjacent positions i and i+1 at different frequencies, that is:

[0027] Δφ i,j =φ i+1,j -φ i,j

[0028] φ i,j represents the theoretical phase value corresponding to frequency j at position i, φ i+1,j Indicates the theoretical phase value corresponding to frequency j at position i+1, Δφ i,j Represents the phase difference between position i and i+1 at frequency j, wraps the phase difference and limits it to the range [-π,π]; then calculates the reference frequency phase difference between adjacent positions i and i+1:

[0029] Δφ i,ref =φ i+1,ref -φ i,ref

[0030] φ i,ref represents the phase value of the reference frequency at position i, φ i+1,ref Indicates the phase value of the reference frequency at position i+1; Δφ ref Represents the phase difference between position i and i+1 at the reference frequency; taking the reference frequency as the reference frequency, traverse all frequency points except the reference frequency one by one, and calculate the double difference phase difference y corresponding to the reference frequency dd :

[0031] y dd =Δφ i,j -Δφ i,ref

[0032] Δφ i,j represents the phase difference between position i and i+1 at frequency j, Δφ refIt represents the phase difference between position i and i+1 at the reference frequency, ydd is the double difference phase difference of position i;

[0033] Construct double difference design matrix A dd , used to record the wave number difference information of each frequency; construct the integer design matrix G dd Used to record the integer ambiguity between each frequency and position; where each G dd The elements in the matrix are assigned a value of 2π, representing the unit contribution of integer ambiguity, and (j>ref_freq_idx) is used to skip the reference frequency to ensure that only the ambiguity contribution of non-reference frequency is recorded in the matrix, which is expressed as follows:

[0034]

[0035] G dd =2π

[0036] The double difference observation value y calculated each time dd is used to populate the vector of double-differenced observations and to update both the double-differenced design matrix and the integer design matrix.

[0037] Further, step S4 specifically includes the following steps:

[0038] Construct the noise covariance matrix Q y_dd With the total design matrix D dd According to the noise superposition principle:

[0039]

[0040] D dd =[A dd ,G dd ]

[0041] σ 单差 represents the standard deviation of the single-difference observation noise, represents the variance of the double-difference observation noise, and I is the unit matrix;

[0042] Check the condition number and rank of the design matrix. If the condition number is greater than 10, 12 , it means that the matrix is ​​close to singularity, which may lead to numerical instability; if the rank is less than the number of columns of the matrix, it means that the design matrix is ​​not full rank and inverse operation or pseudo-inverse calculation cannot be performed, which may lead to non-unique solution or inability to solve;

[0043] Compute floating-point solutions to double-difference equations using pseudoinverse, including the position change parameter Δd float and the fuzziness parameter n float A preliminary estimate of is given by:

[0044]

[0045] D dd is the total design matrix, containing the linear relationship between the double-difference observations and the parameters, Q y_dd is the noise covariance matrix, y dd is the double difference phase difference, is the estimated parameter vector, including position change parameters and ambiguity parameters; Δd float is the position change parameter, n float is the fuzziness parameter;

[0046] Extract the covariance matrix Q of the integer ambiguities z_dd For subsequent accuracy analysis:

[0047]

[0048] Q z_dd =Q x_dd ((num_positions-1)+1:end,(num_positions-1)+1:end)

[0049] Q x_dd is the complete parameter covariance matrix, including the covariance of the location parameters and integer ambiguities; Q z_dd is the covariance matrix of the integer ambiguity part.

[0050] Further, step S5 specifically includes the following steps:

[0051] Based on floating point ambiguity resolution float , the improved MLAMBDA method is used to fix the integer ambiguity z fixed , for the ambiguity covariance matrix Q z_dd LDL T Decomposition, get the decorrelation transformation matrix Z, the lower triangular matrix L and the diagonal matrix D, through the transformation to solve the floating point ambiguity n float Transform to the new coordinate system z float , as shown in the following formula:

[0052] Q z_dd =ZLDL T z T

[0053] z float =Z T n float

[0054] Based on the decorrelation ambiguity parameter z float , using the least squares criterion to perform integer search and finally determine the fixed integer ambiguity solution z fixed :

[0055]

[0056] Resolve the fixed integer ambiguity z fixed Mapping back to the original coordinate system, we get the final integer ambiguity solution n fixed :

[0057] n fixed =Zz fixed

[0058] Re-estimate the position change parameter Δd using the weighted least squares method fixed :

[0059] y dd,corrected =y dd -G dd n fixed

[0060]

[0061] The beneficial effects of the present invention are:

[0062] 1. Reduce the complexity of ambiguity resolution and improve computational efficiency

[0063] The invention adopts the weighted least square method combined with the pseudo-inverse matrix solution, which greatly reduces the computational complexity of ambiguity resolution. Compared with the traditional direct inversion method, it can maintain the stability of computational efficiency when the number of frequency points and position points increases.

[0064] 2. Enhance noise robustness and improve positioning accuracy in complex environments

[0065] By constructing a noise covariance matrix to weight the observations, the ambiguity resolution results in high-noise scenes are made more stable. Experiments show that this method can effectively suppress the interference of noise on positioning results and improve positioning accuracy, especially for complex indoor scenes.

[0066] 3. Optimize the double-difference design matrix to improve system stability

[0067] By rationally constructing the double-difference observation equation and optimizing the distribution of frequency points and position points, the problem of unstable solution caused by insufficient rank of the design matrix is ​​overcome.

[0068] 4. Improve positioning accuracy and achieve centimeter-level error range

[0069] By combining the optimization and fixation of floating-point solutions with integer ambiguities, the present invention achieves a centimeter-level error range in the estimation of relative distance differences, which significantly improves the accuracy compared to the sub-meter-level error of traditional methods.

[0070] 5. Potential to expand and adapt to 5G NR scenarios

[0071] The double-difference observation equation and ambiguity resolution process designed by the present invention are stable in multi-frequency signal scenarios and are suitable for the 5G NR mid-frequency band. Due to the high tolerance of double-difference observation and weighted least squares method to multi-frequency signals and the high efficiency of ambiguity resolution, they can theoretically adapt to high-frequency signals and have the potential to further adapt to high-frequency band signals.

[0072] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0074] Figure 1 It is a flow chart of the ambiguity resolution and positioning method based on double difference phase observation;

[0075] Figure 2 This is a comparison chart between the estimated value and the true value of the relative distance difference;

[0076] Figure 3 It is the relative distance difference error analysis diagram;

[0077] Figure 4 The following is an example diagram of an application scenario. DETAILED DESCRIPTION

[0078] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and the features in the embodiments can be combined with each other without conflict.

[0079] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0080] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0081] This embodiment provides a high-precision displacement monitoring solution based on double-difference phase observation, which is suitable for outdoor long-distance scenarios. The solution is designed to accurately monitor small displacements of buildings or large structures, especially when close-range monitoring is not possible. The frequency range of 2.8 GHz to 2.89 GHz is selected to provide more stable signal quality and higher measurement accuracy through precise phase measurement of high-frequency signals.

[0082] A high-precision displacement monitoring solution based on double-difference phase observation is set as follows: monitoring the displacement changes of large structures such as high-rise buildings and bridges under wind loads or other external forces. The distance between the monitoring equipment and the target structure is set to 50 to 100 meters to optimize the quality of signal propagation and reflection.

[0083] like Figure 1 As shown, the steps of the ambiguity resolution and positioning method based on double difference phase observation of the present invention are as follows:

[0084] The first step is frequency selection.

[0085] Select 2.8GHz to 2.89GHz as the frequency range for signal transmission. This frequency band has good penetration and small atmospheric attenuation, and is suitable for outdoor long-distance transmission and monitoring target structures in the environment. As needed, the transmission frequency step size (for example, 10MHz) can be set to generate a frequency list with equal intervals to ensure that the signal coverage and frequency resolution meet the requirements. The frequency points are distributed at equal intervals to generate a frequency list with a total of 10 frequency points. Use formula 1 to calculate the wavelength corresponding to each frequency point one by one, and store the results in the wavelength list.

[0086]

[0087] where f j is the frequency of the jth frequency point, c is the speed of light, λ j is the wavelength corresponding to the jth frequency.

[0088] Step 2: Equipment configuration and installation.

[0089] Transmitter: Equipped with a microwave signal transmitter that supports transmission in the 2.8GHz to 2.89GHz frequency band, with the ability to transmit continuously or by frequency hopping. The directional antenna of the transmitter should point to the monitored target object to ensure that the signal strength covers the target area.

[0090] Receiver: Equipped with a high-sensitivity receiver, the receiver must include a signal demodulation and processing unit that can receive and demodulate the phase information in the reflected signal in real time. At the same time, the receiver should have anti-interference ability and stability to avoid the impact of environmental changes on measurement accuracy.

[0091] The third step is signal debugging and calibration.

[0092] Adjust the transmitter's transmission power and frequency range to ensure that the signal can cover the target monitoring area. Adjust the receiver's sensitivity and directionality to ensure that the signal reflected from the target can be clearly received.

[0093] Perform preliminary signal calibration: The transmitting and receiving equipment must operate in a stable reference environment to collect initial observation data, which are then used to calibrate system parameters (such as initial phase deviation, background noise level, etc.) to provide a benchmark for subsequent data processing.

[0094] Step 4: Double-difference phase observation and data collection.

[0095] First, double-difference phase observation is performed. The transmitting device continuously transmits signals according to the preset frequency list (2.8 GHz to 2.89 GHz, step size 10 MHz); the receiver records the signal reflected from the target object and continuously collects the phase data of the target object at different frequency points and different positions. ij Specifically, according to the frequency list, the phase information of ten different frequencies is a group, and 10 groups are taken in succession (which can be determined by yourself). When the position changes, the phase information of the group corresponding to the changed position will change. The data is stored locally or transmitted to a remote data processing center via a wireless network for subsequent analysis.

[0096] Step 5: Data processing and displacement calculation.

[0097] 1. Use MATLAB and other data processing software to demodulate the collected phase data and extract the phase information of the target object at different positions and frequencies, including:

[0098] Define the initial distance and the distance after each move, and generate a distance list. Add the initial distance to the beginning of the list. Read the number of positions num_positions and the number of frequencies num_frequencies. Initialize the phase matrix and define the noise level as 0.03 radians. Traverse each target position and frequency point, and for each position i and each frequency j, calculate the theoretical phase value between the target and the receiver using the carrier phase formula (Equation 2):

[0099]

[0100] where d i represents the actual distance between the target position i and the receiver, φ ij It represents the theoretical phase value corresponding to frequency j at the target position i. Then Gaussian noise is superimposed to simulate the actual observed value, and finally the result is limited to the range of [-π, π] to form a winding phase.

[0101] 2. Construct the double-difference observation equation, record the displacement information of the target object by designing the matrix, and calculate the floating-point ambiguity solution by weighted least squares method based on the observation value and the noise covariance matrix; specifically include:

[0102] Select the base frequency (the first frequency in the frequency list) and initialize the double difference observation y dd and the design matrix A dd With G dd Traverse all pairs of adjacent positions and first calculate the phase difference between adjacent positions i and i+1 at different frequencies, that is,

[0103] Δφ i,j =φ i+1,j -φ i,j (3)

[0104] φ i,j represents the theoretical phase value corresponding to frequency j at position i, φ i+1,j Indicates the theoretical phase value corresponding to frequency j at position i+1, Δφ i,j Represents the phase difference between position i and i+1 at frequency j, wraps the phase difference and limits it to the range [-π,π]. Then calculate the reference frequency phase difference between adjacent positions i and i+1:

[0105] Δφ i,ref =φ i+1,ref -φ i,ref (4)

[0106] φ i,ref represents the phase value of the reference frequency at position i, φ i+1,ref Indicates the phase value of the reference frequency at position i+1. Δφ refRepresents the phase difference between position i and i+1 at the reference frequency. Taking the reference frequency as the reference frequency, traverse all frequency points except the reference frequency one by one, and calculate the double difference phase difference y corresponding to the reference frequency dd :

[0107] y dd =Δφ i,j -Δφ i,ref (5)

[0108] Δφ i,j represents the phase difference between position i and i+1 at frequency j, Δφ ref Represents the phase difference between position i and i+1 at the reference frequency, y dd That is, the double difference phase difference at position i. At the same time, the double difference design matrix A is constructed dd (records the wave number difference information of each frequency) and the integer design matrix G dd (records the integer ambiguity between each frequency and position), where each G dd The elements in the matrix are assigned a value of 2π, representing the unit contribution of integer ambiguity, and (j>ref_freq_idx) is used to skip the reference frequency to ensure that only the ambiguity contribution of non-reference frequency is recorded in the matrix. As shown in equations 6 and 7:

[0109]

[0110] G dd =2π (7)

[0111] The double difference observation value y calculated each time dd It will be used to fill the double-difference observation vector and update the double-difference design matrix and integer design matrix to provide support for parameter estimation of the subsequent positioning algorithm.

[0112] Construct the noise covariance matrix Q y_dd With the total design matrix D dd According to the noise superposition principle:

[0113]

[0114] D dd =[A dd ,G dd ] (10)

[0115] σ 单差 represents the standard deviation of the single-difference observation noise, represents the variance of the double-difference observation noise, and I is the unit matrix.

[0116] Check the condition number and rank of the design matrix. If the condition number is large (greater than 10 12) indicates that the matrix is ​​close to being singular, which may lead to numerical instability and affect the accuracy of subsequent calculations. If the rank is less than the number of columns of the matrix, it means that the design matrix is ​​not full rank and inverse operations or pseudo-inverse calculations cannot be performed, which may result in non-unique solutions or inability to find a solution.

[0117] Compute floating-point solutions to double-difference equations using pseudoinverse, including the position change parameter Δd float and the fuzziness parameter n float The initial estimate of . Equation 12 can be derived from Equation 11, and the derivation process is not detailed:

[0118]

[0119] D dd is the total design matrix, containing the linear relationship between the double-difference observations and the parameters, Q y_dd is the noise covariance matrix, y dd is the double difference phase difference, is the estimated parameter vector (including position change parameters and ambiguity parameters), Δd float is the position change parameter, n float is the fuzziness parameter.

[0120] Extract the covariance matrix Q of the integer ambiguities z_dd For subsequent accuracy analysis:

[0121]

[0122] Q z_dd =Q x_dd ((num_positions-1)+1:end,(num_positions-1)+1:end) (16)

[0123] Q x_dd is the complete parameter covariance matrix, containing the covariances of the location parameters and integer ambiguities. z_dd is the covariance matrix of the integer ambiguity part.

[0124] 3. Use the improved MLAMBDA method to fix the ambiguity and obtain an accurate integer ambiguity solution; finally, perform displacement calculation and optimize the displacement parameters based on the fixed ambiguity solution and the double difference observation equation; specifically, include:

[0125] Based on floating point ambiguity resolution float , the improved MLAMBDA method is used to fix the integer ambiguity z fixed Specifically, the ambiguity covariance matrix Q z_dd LDL TDecomposition, get the decorrelation transformation matrix Z, the lower triangular matrix L and the diagonal matrix D, through the transformation to solve the floating point ambiguity n float Transform to the new coordinate system z float , as shown in Equations 17 and 18:

[0126] Q z_dd =ZLDL T z T (17)

[0127] z float =Z T n float (18)

[0128] Based on the decorrelation ambiguity parameter z float , using the least squares criterion to perform integer search and finally determine the fixed integer ambiguity solution z fixed :

[0129]

[0130] Resolve the fixed integer ambiguity z fixed Mapping back to the original coordinate system, we get the final integer ambiguity solution n fixed :

[0131] n fixed =Zz fixed (20)

[0132] Re-estimate the position change parameter Δd using the weighted least squares method fixed :

[0133] y dd,corrected =y dd -G dd n fixed (twenty one)

[0134]

[0135] Compare the phase changes of the target object at consecutive time points and calculate the actual value of the displacement.

[0136] Step 6: Data output and application.

[0137] like Figure 2-3As shown in the figure, a graphical user interface is used to display real-time monitoring data, including the displacement change trend and current status of the target object. Important time points or abnormal change points are marked in the chart to facilitate monitoring personnel to identify key data. The historical displacement data during the monitoring period is sorted out and a trend analysis report is generated. The report should include displacement change curves, amplitude statistics, and analysis of the impact of environmental factors (such as wind speed, temperature changes, etc.). The displacement threshold is set according to actual needs. When the target displacement value exceeds the set threshold, the alarm system is automatically triggered, and the monitoring personnel are notified through sound and light signals or text messages to take timely measures.

[0138] Step 7: Monitoring and maintenance.

[0139] Regularly check the operating status of the transmitting and receiving equipment to ensure stable signal transmission and reception. Calibrate equipment parameters to adapt to external environmental changes (such as the impact of temperature, humidity, etc. on the signal). If abnormal data is found, check and replace possible faulty equipment components (such as antennas or signal processing units); regularly clean the surface of the equipment to prevent dust or debris from blocking the signal propagation path. Application scenarios such as Figure 4 shown.

[0140] In the above embodiments, the description's reference to "this embodiment" indicates that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily all refer to the same embodiment.

[0141] In the above-described embodiments, although the invention has been described in conjunction with specific embodiments of the invention, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other storage structures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed. Embodiments of the invention are intended to encompass all such substitutions, modifications, and variations that fall within the broad scope of the appended claims.

[0142] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented.

[0143] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0144] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0145] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0146] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0147] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0148] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0149] The present invention can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0150] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. An ambiguity resolution and positioning method based on double-difference phase observation, characterized in that: The following steps are involved: S1: Select the frequency range of signal transmission and calculate the wavelength corresponding to each frequency point; S2: introduce noise and generate simulated observations; S3: Construct double difference phase observation equation; S4: Calculate floating point solution using weighted least squares method; S5: fix the certificate ambiguity solution and optimize the position parameters; S6: Compare the phase changes of the target object at consecutive time points and calculate the actual value of the displacement.

2. The ambiguity resolution and positioning method based on double-difference phase observation according to claim 1, characterized in that: In step S1, 2.8 GHz to 2.89 GHz is selected as the frequency range of signal transmission, the transmission frequency step is set, and the frequency points are distributed at equal intervals to generate a frequency list; the wavelength corresponding to each frequency point is calculated one by one using the following formula, and the results are stored in the wavelength list; where f j is the frequency of the jth frequency point, c is the speed of light, λ j is the wavelength corresponding to the jth frequency.

3. The ambiguity resolution and positioning method based on double difference phase observation according to claim 1, characterized in that: Step S2 specifically includes the following steps: Define the initial distance and the distance after each move, and generate a distance list; Adding the initial distance to the beginning of the distance list; Read the number of positions num_positions and the number of frequencies num_frequencies; Initialize the phase matrix and define the noise level; Each target position and frequency point is traversed, and for position i and frequency j, the theoretical phase value between the target and the receiver is calculated by the carrier phase formula, which is: where d i represents the actual distance between the target position i and the receiver, φ ij Represents the theoretical phase value corresponding to frequency j at target position i; Gaussian noise is then superimposed to simulate the actual observed value, and finally the result is limited to the range of [-π, π] to form a winding phase.

4. The ambiguity resolution and positioning method based on double-difference phase observation according to claim 1, characterized in that: The step S3 constructs the double difference phase observation equation, comprising the following steps: Select the reference frequency, initialize the double difference observations ydd and the design matrix A dd With G dd ; Traverse all pairs of adjacent positions and first calculate the phase difference between adjacent positions i and i+1 at different frequencies, that is: Df i,j =φ i+1,j -f i,j φ i,j represents the theoretical phase value corresponding to frequency j at position i, φ i+1,j Indicates the theoretical phase value corresponding to frequency j at position i+1, Δφ i,j Represents the phase difference between position i and i+1 at frequency j, wraps the phase difference and limits it to the range [-π,π]; then calculates the reference frequency phase difference between adjacent positions i and i+1: Df i,ref =φ i+1,ref -f i,ref φ i,ref represents the phase value of the reference frequency at position i, φ i+1,ref Indicates the phase value of the reference frequency at position i+1; Δφ ref Represents the phase difference between position i and i+1 at the reference frequency; taking the reference frequency as the reference frequency, traverse all frequency points except the reference frequency one by one, and calculate the double difference phase difference y corresponding to the reference frequency dd : y dd =Df i,j -Df i,ref Δφ i,j represents the phase difference between position i and i+1 at frequency j, Δφ ref It represents the phase difference between position i and i+1 at the reference frequency, ydd is the double difference phase difference of position i; Construct double difference design matrix A dd , used to record the wave number difference information of each frequency; construct the integer design matrix G dd Used to record the integer ambiguity between each frequency and position; where each G dd The elements in the matrix are assigned a value of 2π, representing the unit contribution of integer ambiguity, and (j>ref_freq_idx) is used to skip the reference frequency to ensure that only the ambiguity contribution of non-reference frequency is recorded in the matrix, which is expressed as follows: G dd ＝2π The double difference observation value y calculated each time dd is used to populate the vector of double-differenced observations and to update both the double-differenced design matrix and the integer design matrix.

5. The ambiguity resolution and positioning method based on double-difference phase observation according to claim 1, characterized in that: Step S4 The specific steps include: Construct the noise covariance matrix Q y_dd With the total design matrix D dd According to the noise superposition principle: D dd =[A dd ,G dd ] σ 单差 represents the standard deviation of the single-difference observation noise, represents the variance of the double-difference observation noise, and I is the unit matrix; Check the condition number and rank of the design matrix. If the condition number is greater than 10, 12 , then the matrix is ​​close to being singular, which may lead to numerical instability; If the rank is less than the number of columns of the matrix, it means that the design matrix is ​​not full of rank and cannot be inverted or pseudo-inversely calculated, which may result in a non-unique solution or an inability to solve the problem; Compute floating-point solutions to double-difference equations using pseudoinverse, including the position change parameter Δd float and the fuzziness parameter n float A preliminary estimate of is given by: D dd is the total design matrix, containing the linear relationship between the double-difference observations and the parameters, Q y_dd is the noise covariance matrix, y dd is the double difference phase difference, is the estimated parameter vector, including position change parameters and ambiguity parameters; Δd float is the position change parameter, nfl oat is the fuzziness parameter; Extract the covariance matrix Q of the integer ambiguities z_dd For subsequent accuracy analysis: Q z_dd =Q x_dd ((num_positions-1)+1:end,(num_positions-1)+1:end) Q x_dd is the complete parameter covariance matrix, including the covariance of the location parameters and integer ambiguities; Q z_dd is the covariance matrix of the integer ambiguity part.

6. The ambiguity resolution and positioning method based on double-difference phase observation according to claim 1, characterized in that: Step S5 specifically includes the following steps: Solving NFL based on floating point ambiguity oat , the improved MLAMBDA method is used to fix the integer ambiguity z fixed , for the ambiguity covariance matrix Q z_dd LDL T Decomposition, get the decorrelation transformation matrix Z, the lower triangular matrix L and the diagonal matrix D, through the transformation to solve the floating point ambiguity n float Transform to the new coordinate system z float , as shown in the following formula: Q z_dd =ZLDL T z T With float =Z T n float Based on the decorrelation ambiguity parameter z float , using the least squares criterion to perform integer search and finally determine the fixed integer ambiguity solution z fixed : Resolve the fixed integer ambiguity z fixed Mapping back to the original coordinate system, we get the final integer ambiguity solution n fixed : n fixed =Zz fixed Re-estimate the position change parameter Δd using the weighted least squares method fixed : and dd,corrected =and dd -G dd n fixed

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