A Vehicle-to-Everything Cooperative RTK Localization Method and System Based on Observation Correlation Modeling

Abstract

This invention provides a vehicle-mounted cooperative RTK positioning method and system based on observation correlation modeling, belonging to the field of satellite navigation and positioning technology. This invention establishes a unified observation equation including in-vehicle and inter-vehicle baselines at the double-difference observation level, and explicitly derives the observation noise covariance matrix introduced by the shared antenna, theoretically guaranteeing the optimality of least squares estimation. Simultaneously, a joint ambiguity resolution strategy is proposed, significantly improving the success rate of ambiguity fixation for obstructed vehicles in harsh environments by introducing high-quality observation data from cooperative vehicles. In extreme scenarios with severe obstruction, traditional single-vehicle resolution errors diverge significantly, while the cooperative model proposed in this invention can stabilize positioning errors at the centimeter level, effectively solving the problem of positioning continuity in complex urban environments.

Description

Technical Field

[0001] This invention belongs to the field of satellite navigation and positioning technology, specifically relating to a vehicle-mounted cooperative RTK positioning method and system based on observation correlation modeling. Background Technology

[0002] With the rapid development of autonomous driving and intelligent transportation systems (ITS), extremely stringent requirements have been placed on the accuracy, continuity, and reliability of vehicle positioning. In open environments, real-time dynamic differential positioning (RTK) technology based on the Global Navigation Satellite System (GNSS) can provide centimeter-level positioning services. However, when vehicles are driving in urban canyons, tree-lined roads, or under overpasses, satellite signals are frequently interfered with by building obstruction and multipath effects, resulting in a sharp drop in the number of visible satellites and a deterioration of the device geometry orientation (PDOP).

[0003] Under such harsh observation conditions, the integer ambiguity of carrier phase observations is often difficult to fix, and the system has to degenerate into a floating-point solution. The positioning error may diverge instantaneously to the decimeter or even meter level, seriously threatening the safety of autonomous driving.

[0004] To maintain high-precision positioning in satellite-constrained environments, the academic community has proposed various auxiliary enhancement methods. Among them, utilizing the physical constraints of the carrier itself is a low-cost and effective approach. For example, the BaselineLength Constraint method uses prior length information fixed to the vehicle body to improve the condition number of the normal equations by adding virtual observation equations.

[0005] However, traditional single-vehicle length constraint methods have significant limitations. Length constraints only provide scalar-level information. When the satellite geometry is extremely ill-conditioned (e.g., PDOP > 10), simple length constraints are insufficient to completely reverse the rank deficiency problem in the observation equations, resulting in limited improvement in geometric strength. In single-vehicle independent solution mode, if the number of satellites falls below four due to occlusion, even with length constraints applied, the least squares solution will still experience severe oscillations due to a lack of sufficient observation redundancy, failing to cope with severe occlusion.

[0006] In recent years, vehicle-to-vehicle cooperative localization has become a popular approach to solving these problems. Through Dedicated Short Range Communication (DSRC) or C-V2X technology, neighboring vehicles can share raw observation data and build a joint solution network. Theoretically, vehicles in good observation environments can act as mobile base stations, providing additional geometric constraints for vehicles with obstructed views.

[0007] Despite the immense potential of cooperative localization, existing research largely focuses on loosely coupled data fusion, often neglecting the fine-grained modeling of stochastic models of observations. Particularly when vehicles share certain observation antennas (such as the endpoints of the cooperative baseline), significant physical correlations exist between double-difference observations. Ignoring these correlations and using a diagonal matrix to approximate the covariance matrix leads to improper weight allocation, consequently affecting the success rate of ambiguity fixation. Summary of the Invention

[0008] The purpose of this section is to outline some aspects of the embodiments of the present invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this section, the abstract and title of the invention. Such simplifications or omissions shall not be used to limit the scope of the present invention.

[0009] In view of the aforementioned existing problems, this invention is proposed. Unlike traditional loose combinations, this invention constructs a rigorous collaborative observation mathematical model, establishing a unified observation equation at the double-difference observation level that includes both in-vehicle and out-of-vehicle baselines. Furthermore, it explicitly derives the observation noise covariance matrix introduced by the shared antenna, theoretically guaranteeing the optimality of least-squares estimation. Simultaneously, this invention proposes a joint ambiguity resolution strategy, significantly improving the success rate of ambiguity fixation for obstructed vehicles in harsh environments by incorporating high-quality observation data from collaborating vehicles.

[0010] To address the aforementioned technical problems, the present invention provides the following technical solution: A vehicle-mounted cooperative RTK positioning method based on observation correlation modeling includes: acquiring dual-antenna GNSS observation data of a master vehicle and a cooperating vehicle, wherein the master vehicle is equipped with a first antenna and a second antenna, and the cooperating vehicle is equipped with a third antenna and a fourth antenna; constructing a unified double-difference observation model, wherein the unified double-difference observation model includes double-difference observation values ​​of baseline AB, baseline BC, and baseline CD; deriving the covariance matrix of the double-difference observation values ​​of the baseline AB, the baseline BC, and the baseline CD based on the independence assumption of the original carrier phase observation values ​​and the double-difference operation rules, and establishing a joint observation equation based on the unified double-difference observation model and the covariance matrix; performing least squares solution to obtain ambiguity floating-point solution and baseline floating-point solution based on the joint observation equation, and constructing a joint variance-covariance matrix based on the correlation matrix corresponding to the covariance matrix; and performing joint ambiguity resolution based on the joint variance-covariance matrix, the prior length truth value of the baseline AB, and the prior length truth value of the baseline CD.

[0011] In a preferred embodiment of the present invention, the baseline AB is the baseline between the first antenna and the second antenna, the baseline CD is the baseline between the third antenna and the fourth antenna, and the baseline BC is the baseline between the second antenna and the third antenna.

[0012] In a preferred embodiment of the present invention, the correlation matrix is ​​a 3×3 matrix, with diagonal elements being 1, the correlation element corresponding to the relationship between baseline AB and baseline BC being -1 / 2, the correlation element corresponding to the relationship between baseline BC and baseline CD being -1 / 2, and the remaining off-diagonal elements being 0.

[0013] As a preferred embodiment of the present invention, the joint variance-covariance matrix is ​​constructed by performing a Kronecker product between the correlation matrix and the single baseline variance-covariance matrix.

[0014] In a preferred embodiment of the present invention, the joint fuzziness resolution includes constructing an objective function, wherein the objective function includes a joint fuzziness search term, a first length constraint term, and a second length constraint term.

[0015] In a preferred embodiment of the present invention, an integer least squares search is performed on the objective function to obtain a fixed ambiguity.

[0016] As a preferred embodiment of the present invention, the integer least squares search includes performing a decorrelation transformation on the joint variance-covariance matrix, performing an integer combination search in the decorrelation space, and determining the candidate combination with the smallest objective function value as the fixed ambiguity.

[0017] In a preferred embodiment of the present invention, the first length constraint term and the second length constraint term are calculated using a projection method; the projection method includes calculating an unconstrained baseline vector after fixing the ambiguity.

[0018] The constrained solution is obtained by projecting the unconstrained baseline vector onto a sphere with a length equal to the true value of the prior length.

[0019] In a preferred embodiment of the present invention, a fixed solution for baseline AB is calculated based on the fixed ambiguity of baseline AB and the prior length truth value of baseline AB; and a fixed solution for baseline CD is calculated based on the fixed ambiguity of baseline CD and the prior length truth value of baseline CD.

[0020] On the other hand, the present invention also provides a vehicle-mounted cooperative RTK positioning system based on observation correlation modeling, comprising: a data acquisition module for acquiring dual-antenna GNSS observation data of a master vehicle and a cooperating vehicle, wherein the master vehicle is equipped with a first antenna and a second antenna, and the cooperating vehicle is equipped with a third antenna and a fourth antenna; a model building module for constructing a unified double-difference observation model, wherein the unified double-difference observation model includes double-difference observation values ​​of baseline AB, baseline BC, and baseline CD; a covariance derivation module for deriving the covariance matrix of the double-difference observation values ​​of baseline AB, baseline BC, and baseline CD based on the independence assumption of the original carrier phase observation values ​​and the double-difference operation rules, and establishing a joint observation equation based on the unified double-difference observation model and the covariance matrix; a floating-point solution module for performing least squares solution to obtain ambiguity floating-point solution and baseline floating-point solution based on the joint observation equation, and constructing a joint variance-covariance matrix based on the correlation matrix corresponding to the covariance matrix; and a joint ambiguity solution module for performing joint ambiguity solution based on the joint variance-covariance matrix, the prior length truth value of baseline AB, and the prior length truth value of baseline CD.

[0021] The beneficial effects of this invention are as follows: Compared with the prior art, the technical effects of this invention are as follows: The rigorous collaborative observation mathematical model constructed by this invention establishes a unified observation equation including the in-vehicle baseline and the vehicle-to-vehicle baseline at the level of double-difference observations, and explicitly derives the observation noise covariance matrix introduced by the shared antenna, theoretically guaranteeing the optimality of least squares estimation. Simultaneously, a joint ambiguity resolution strategy is proposed, which significantly improves the success rate of ambiguity fixation for obstructed vehicles in harsh environments by introducing high-quality observation data from collaborative vehicles. Simulation and field experiments show that in extreme scenarios with severe obstruction (less than 4 satellites), the traditional single-vehicle resolution error diverges significantly (RMS > 40 cm), while the collaborative model proposed in this invention can stabilize the positioning error at the centimeter level (RMS < 1 cm), effectively solving the problem of positioning continuity in complex urban environments. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the geometric configuration of the cooperative positioning system in this invention.

[0023] Figure 2 This is a comparison chart of the success rate of ambiguity resolution in this invention.

[0024] Figure 3 This is a time series plot of baseline error in this invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0026] This invention discloses a vehicle-mounted cooperative RTK positioning method based on observation correlation modeling, applicable to dual-vehicle platooning scenarios, wherein the lead vehicle and the cooperating vehicle are each equipped with dual antennas. The lead vehicle has a first antenna A and a second antenna B mounted along its longitudinal axis, while the cooperating vehicle has a third antenna C and a fourth antenna D. The four antenna nodes A, B, C, and D together constitute a dynamic observation network.

[0027] S1: Acquire dual-antenna GNSS observation data from the main vehicle and the cooperating vehicle. The main vehicle is equipped with a first antenna and a second antenna, and the cooperating vehicle is equipped with a third antenna and a fourth antenna.

[0028] Specifically, to overcome the problem of insufficient satellite visibility for a single vehicle in complex urban environments, this invention constructs a tightly coupled cooperative positioning system for two vehicles based on vehicle-to-vehicle (V2V) communication. The system's geometric configuration is as follows: Figure 1 As shown.

[0029] The system comprises two vehicles operating in tandem, designated as vehicle U (the master vehicle) and vehicle V (the collaborating vehicle). Vehicle U has two GNSS antennas mounted on its roof along its longitudinal axis, denoted as A (front) and B (rear); similarly, vehicle V has antennas C (front) and D (rear). These four antenna nodes (A, B, C, D) together form a dynamic observation network.

[0030] In this network, the baseline vectors that need to be solved include the in-vehicle baseline vector b. AB and b CD Because the antenna is rigidly fixed to the vehicle body, the lengths of these two baselines remain constant during movement, with baseline b... AB and b CD The length of is known prior information, satisfying . and .in and This is the true value of the baseline length pre-calibrated for high precision. The workshop collaboration baseline is vector b. BC This baseline connects the rear antenna B of vehicle U to the front antenna C of vehicle V. It is not only the geometric link between the two vehicles, but also the logical link for transmitting differential corrections and observation data.

[0031] It should be noted that antenna B plays a dual role in the configuration: it is both the baseline b and the antenna B.AB The mobile station is also the collaborative baseline b. BC The reference station. This antenna-sharing characteristic leads to issues involving b AB and b BC There is a physical correlation between the double-difference observations.

[0032] S2: Construct a unified double-difference observation model, which includes double-difference observations of baselines AB, BC, and CD.

[0033] Specifically, in a setup where each vehicle (or drone) is equipped with dual antennas, the carrier phase double-difference observation equation is expressed as: ; in, The carrier phase double difference is denoted by the covariance matrix. This can be derived from the original observations using the error propagation law. Specifically, the assumption is that the original carrier phase observations are independent of each other, and the standard deviation of the observation noise is... (Corresponding to typical accuracy for L1 carrier), double-difference operations involve inter-satellite and inter-station differences. For an observation epoch containing n satellites, after selecting a reference satellite, (n-1) double-difference observations can be formed, with a covariance matrix... It is an (n-1)×(n-1) dimensional symmetric matrix with the following characteristics: diagonal elements (Because double difference operations involve a linear combination of four original observations), off-diagonal elements (Reflecting the correlation introduced by the shared reference satellite) Its function is to convert integer ambiguity (dimensionless integer) into distance units (meters).

[0034] For single-frequency observations (such as the L1 band). It is a diagonal matrix, represented as: ,in, For the carrier wavelength (frequency band of L1) ≈0.1903m; wavelength of L2 band ≈0.2442m). It is an identity matrix. The matrix is ​​the design matrix for the double-difference observation equation, with dimensions (n-1)×3, reflecting the geometric relationship between the baseline vector and the observations. Each row corresponds to a double-difference observation, which is composed of the double-difference combination of the unit line-of-sight vectors. For ambiguity vectors, For the baseline vector, The carrier phase observation noise is assumed to follow a zero-mean Gaussian distribution, i.e. After double-difference operation, systematic errors such as receiver clock error, satellite clock error, and atmospheric delay are significantly reduced under short baseline conditions, and the residual error can be approximated as zero-mean random noise.

[0035] Furthermore, performing least-squares calculations on the above formula yields floating-point solutions for the ambiguity and baseline vector, denoted as follows: and The corresponding variance-covariance matrix is ​​denoted as , Covariance Matrix .

[0036] Once the ambiguity is correctly fixed as an integer, the conditional solution of the baseline vector... Covariance Matrix This can be expressed as: ; The corresponding covariance matrix is ​​expressed as: .

[0037] Furthermore, taking two cooperating vehicles U and V as an example, assume vehicle U is equipped with antennas A and B, and vehicle V is equipped with antennas C and D. These four nodes constitute six possible baselines, but only three of them are linearly independent baselines, denoted as . , and cross-vehicle collaboration baseline The overall observation equation and stochastic model of the collaborative model can then be expressed as: ;

[0038] in, Let be the carrier phase double-difference observation vector of baseline AB, with dimensions (n-1)×1. Let be the carrier phase double-difference observation vector of the cooperative baseline BC, with dimensions (n ​​- 1) × 1. The carrier phase double-difference observation vector of the baseline CD has a dimension of (n - 1)×1; , and These are integer ambiguity vectors for baselines AB, BC, and CD, respectively, each with a dimension of (n-1)×1; Let AB be a three-dimensional vector of baseline AB, with dimensions 3×1. , These are the coordinates on the three coordinate axes; The three-dimensional vector of the collaborative baseline BC has a dimension of 3×1; is a three-dimensional vector of the baseline CD, with dimensions of 3×1. This indicates the use of Kronecker product to construct a block diagonal matrix, the physical meaning of which is: ;

[0039] in, This represents the expected value sign. It is a 3×3 identity matrix.

[0040] S3: Based on the independence assumption of the original carrier phase observations and the double-difference operation rule, derive the covariance matrix of the double-difference observations of the baseline AB, the baseline BC and the baseline CD, and establish a joint observation equation based on the unified double-difference observation model and the covariance matrix.

[0041] Since antenna B serves as a shared antenna in both baselines AB and BC, and antenna C serves as a shared antenna in both baselines BC and CD, there is a physical correlation between the double-difference observations. Taking satellite 2 relative to reference satellite 1 as an example, the double-difference observations of baselines AB and BC are as follows: ; ; in, and These are double-difference observations. This is the carrier phase observation value of antenna A corresponding to satellite 1. Antenna A corresponds to the carrier phase observation value of satellite 2. Antenna B corresponds to the carrier phase observation value of satellite 1. Antenna B corresponds to the carrier phase observation value of satellite 2. This is the carrier phase observation value of antenna C corresponding to satellite 1. Antenna C corresponds to the carrier phase observation value of satellite 2.

[0042] Since the observations of antenna B have opposite signs in the two double-differences, the covariance is: ; The variance of the double difference is: ; Therefore, the correlation coefficient is: ; Similarly, baselines BC and CD share antenna C, and their correlation coefficient is -0.5. Baselines AB and CD do not share an antenna, and their correlation coefficient is 0.

[0043] Therefore, the correlation matrix P is defined as: ; Finally, the overall stochastic model of the collaborative model is expressed as: ; in, This represents the covariance matrix. The matrix reflects the correlation matrix introduced by the shared antenna. Before adjustment. Using a uniform template (with a very short baseline), the adjusted covariance matrix becomes a non-diagonal structure due to the shared antenna.

[0044] S4: Perform least squares calculations based on the joint observation equation to obtain the ambiguity floating-point solution and the baseline floating-point solution, and construct the joint variance-covariance matrix based on the correlation matrix corresponding to the covariance matrix.

[0045] Specifically, the floating-point solution matrix for the ambiguity of the three baselines is as follows: ,in, The ambiguity vector containing three baselines, i.e. The baseline floating-point solution is This contains a three-dimensional coordinate vector of three baselines, with dimensions 9×1. Combined with a single baseline... and and covariance matrix This allows us to obtain the joint floating-point solution of all ambiguities and three baselines in the collaborative system, along with their variance-covariance matrix (i.e., from a single baseline). and and covariance matrix (Extended to the joint matrix of three-day baselines) ; in, It is the Kronecker product. Expanded, it becomes: ;

[0046] S5: Perform joint ambiguity resolution based on the joint variance-covariance matrix, the prior length truth value of the baseline AB, and the prior length truth value of the baseline CD.

[0047] It should be noted that, due to and It is known. The prior length of the baseline AB on vehicle U is the true value, which is the fixed distance between the front and rear antennas A and B (unit: meters). The prior true length of the baseline CD on vehicle V, i.e., the fixed distance (in meters) between the front and rear antennas C and D, is pre-calibrated using high-precision measuring equipment and introduced as known constants into the constraint equations. The calibration accuracy is ≤2 mm (millimeter level). The constraint solution for the length constraint can then be obtained. and : ; ; The above calculations represent a nonlinear least squares problem with equality constraints. This invention employs the projection method for solution. and It is the baseline vector after obtaining a fixed ambiguity.

[0048] The baseline length is calculated as follows: ; Unconstrained solution Projected onto a length of On the surface of a sphere: ; This is a first-order approximation of the optimal solution, which is usually accurate enough. For higher precision, the Lagrange multiplier method can be used for iterative refinement.

[0049] Furthermore, in order to in the integer field The goal is to find the combination of ambiguities that minimizes the objective function. This invention employs an improved LAMBDA algorithm to obtain a fixed solution for integer ambiguities. , and : ; in: ; The improved LAMBDA algorithm described above consists of three parts: ; in, As the primary search term, the weighted sum of squared residuals based on the collaborative observation model is expressed as: ; The length constraint term for baseline AB is expressed as follows: ; The length constraint term for the baseline CD is expressed as follows: ; For example, the steps for integer search using the improved LAMBDA algorithm are as follows: (1) Decorrelation transformation.

[0050] Calculate the Z-transform matrix and the ambiguity covariance matrix. Perform decorrelation processing to obtain the transformed ambiguity vector. and the diagonalized covariance matrix ∑= diagonal( ) .

[0051] (2) Initialize the search.

[0052] Calculate the total ambiguity. The candidate list is initialized to empty, and the minimum objective function value is... .

[0053] (3) Integer search.

[0054] Starting from the last ambiguity component, process each component sequentially. (from Decrease to 1) and iterate: Centered on, within radius Enumerate candidate integer values ​​within the range.

[0055] When the search reaches the first component (i=1), through = The inverse transform yields the candidate ambiguities, where the subscripts are... This represents candidate vectors in the relevant space, extracted by grouping according to the baseline. Calculate the ambiguity objective function respectively. Baseline length constraint cost and , accumulated to obtain .

[0056] like If the optimal solution is found, then update the search; otherwise, continue searching for the previous recursive call, accumulating the remaining cost budget. .

[0057] Where, accumulated_cost is the cost from the first... The weighted sum of squared residuals accumulated up to the current layer i in the recursive process. The remaining search budget passed to the next recursive layer is used to implement branch-bound pruning to reduce the search range of invalid candidate solutions.

[0058] (4) Return the optimal solution .

[0059] Furthermore, regarding the length constraint term... and The projection method is used to solve the problem quickly. Step 1: Based on the calculated baseline with fixed ambiguity: ; Step 2: Project onto the constrained sphere: ; ; Step 3: Calculate the constraint cost: ; ; The length constraint method is used again, utilizing the obtained ambiguity. and Solving for a fixed solution of the baseline vector and ,get: ; ;

[0060] The solution method and constraint solution for the above calculations The calculation is similar and will not be repeated here.

[0061] It should be noted that the above operations are essentially optimizations of the calculation results, i.e., obtaining the ambiguity. and Then the baseline vector is also obtained, and we can further optimize it using length. This is equivalent to the observation equation having two variables, a and b, which are mutually constraining; if one is accurate, the other will also be accurate. First, we constrain b with length, which improves b's accuracy. This makes the search for a more accurate. After a is accurate, we calculate b and then constrain it again with length, so both a and b are accurate.

[0062] Fixed solution of baseline vector and Constraint solutions for length constraints and In comparison, accuracy will be improved, especially in situations where there are fewer satellites or the signal quality is poor.

[0063] It is worth noting that, compared with independent baseline methods with length constraints, the cooperative model proposed in this invention can theoretically improve the in-vehicle baseline b. AB b CD The accuracy of the estimation.

[0064] From the perspective of stochastic models, independent methods ignore the correlation between baselines. However, shared antennas (such as antenna B) introduce a theoretical correlation coefficient of -0.5 between double-differenced observations. Collaborative models explicitly model this correlation through matrix P, strictly adhering to the best linear unbiased estimator (BLUE) criterion, thereby minimizing the variance of the floating-point solution.

[0065] Furthermore, the joint ambiguity resolution strategy utilizes the collaborative baseline b. BC The coupling relationship with the in-vehicle baseline. In scenarios where satellite visibility is limited for a single vehicle, high-quality observation data from collaborative vehicles can significantly improve the success rate of ambiguity fixation and the reliability of the final fixed solution by using rigorous covariance propagation constraints on the ambiguity search space.

[0066] like Figure 1 As shown, the present invention also provides a vehicle-mounted cooperative RTK positioning system based on observation correlation modeling, comprising: a data acquisition module for acquiring dual-antenna GNSS observation data of a master vehicle and a cooperating vehicle, wherein the master vehicle is equipped with a first antenna and a second antenna, and the cooperating vehicle is equipped with a third antenna and a fourth antenna; a model building module for constructing a unified double-difference observation model, wherein the unified double-difference observation model includes double-difference observation values ​​of baseline AB, baseline BC, and baseline CD; a covariance derivation module for deriving the covariance matrix of the double-difference observation values ​​of baseline AB, baseline BC, and baseline CD based on the independence assumption of the original carrier phase observation values ​​and the double-difference operation rules, and establishing a joint observation equation based on the unified double-difference observation model and the covariance matrix; a floating-point solution module for performing least-squares solution to obtain ambiguity floating-point solution and baseline floating-point solution based on the joint observation equation, and constructing a joint variance-covariance matrix based on the correlation matrix corresponding to the covariance matrix; and a joint ambiguity solution module for performing joint ambiguity solution based on the joint variance-covariance matrix, the prior length truth value of baseline AB, and the prior length truth value of baseline CD.

[0067] To fully verify the advantages of the proposed cooperative model in terms of accuracy improvement and ambiguity fixation reliability, this invention conducts simulation experiments using high-fidelity GNSS observation data. The experiments focus on comparing the performance of traditional single-vehicle solutions and the proposed cooperative solutions under different noise levels and satellite obstruction scenarios.

[0068] The simulation platform was developed based on the MATLAB environment. To simulate the real satellite geometry, the experiment used real GPS / BDS broadcast ephemeris to calculate satellite positions, and artificially generated noise and errors were superimposed on the observations. Independent noise sequences were generated for each satellite s and each receiver antenna (A, B, C, D).

[0069] Specifically, for shared nodes (such as antenna B of vehicle U), observation noise Used simultaneously to generate baselines and collaboration baseline The observation data. This processing method ensures that the simulation data strictly meets the physical correlation described by the P matrix (i.e., the -0.5 correlation coefficient introduced by the shared antenna).

[0070] To evaluate the algorithm's performance, three solution schemes were designed for comparison:

[0071] Scheme 1 (Standard): Traditional single-baseline independent solution. No geometric constraints are imposed, and each baseline is estimated independently.

[0072] Scheme 2 (Constraint): Solving the single-vehicle length constraint. Using... Constraints are imposed, but the collaborative information between vehicles is not utilized.

[0073] Scheme 3 (Proposed): The inter-vehicle cooperation model proposed in this invention. It utilizes joint processing of all observations and applies dual-vehicle geometric constraints and a correlation matrix P. Details are shown in the table below: Table 1 Simulation Experiment Parameter Settings

[0074]

[0075] Furthermore, the impact of observation noise on the AR success rate was first examined. This was achieved by gradually increasing the noise figures m and n (from 1 mm to 10 mm) to simulate a severe multipath environment.

[0076] As shown in Figure 2, the fixation rates of all three schemes decreased with increasing noise levels, but exhibited significant differences: Scheme 1's fixation rate rapidly dropped below 80% when noise reached 5mm, indicating that the unconstrained model was extremely unstable in harsh environments. Scheme 2 benefited from length constraints, improving its robustness, but still struggled to maintain a high success rate in the high-noise range (>6mm). Scheme 3 (Proposed) performed best. Thanks to the correct stochastic model (matrix P) and the joint geometric strength of multiple baselines, its AR fixation rate remained above 90% even with noise levels as high as 8mm.

[0077] To simulate the dynamic scenario of a vehicle traversing an urban canyon or tree-lined road, a simulation was designed to simulate a sudden drop in the number of visible satellites due to environmental occlusion. The experiment lasted for 600 epochs. During epochs t = 200-400s, some visible satellites of vehicle U were artificially removed, reducing its visible satellite count to 4 (at the critical state for solution); while vehicle V maintained good satellite observations (more than 8).

[0078] Figure 3 Baseline was shown The length calculation error sequence is as follows: Phase I (0-200s): The environment is good, and the errors of the three are comparable, all within the range of 1-2cm. Phase II (200-400s, occlusion period): Scheme 1 (red curve): Due to insufficient satellites, the geometric configuration (PDOP) deteriorates sharply, the ambiguity cannot be fixed, and the calculation error diverges to the decimeter or even meter level. Scheme 2 (blue curve): Although the length constraint limits the magnitude of the error, due to the lack of sufficient satellite observations, its direction estimation still has a large deviation, resulting in large fluctuations in the overall three-dimensional error. Scheme 3 (green curve): The error remains at the centimeter level (< 5cm). This is because the collaborative model crosses the vehicle baseline. The system transmits high-quality observation data from vehicle V to vehicle U. Vehicle V acts as a temporary mobile base station, compensating for the geometric deficiencies of U.

[0079] Table 2 below summarizes the root mean square error of the three-dimensional position (3D-RMS) and the average ambiguity fixation rate throughout the simulation process.

[0080] Table 2 Performance Statistical Comparison of Different Solutions

[0081]

[0082] Data shows that, in occlusion environments, the collaborative method proposed in this invention improves accuracy by approximately 86.6% (from 15.65cm to 2.10cm) compared to traditional length constraint methods, while maintaining extremely high solution availability.

[0083] The system also includes one or more processors and memory.

[0084] The memory is used to store operable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, including the flow of the observation-correlation-based cooperative RTK localization method of the foregoing embodiments, especially... Figure 1 The flowchart of the method is shown.

[0085] Other aspects disclosed in the embodiments of the present invention also propose a computer-readable medium for storing software including instructions executable by one or more computers, which, upon execution, cause the one or more computers to perform operations including the flow of the observation-correlation-based cooperative RTK localization method of the foregoing embodiments, particularly... Figure 1 The flowchart of the method is shown.

[0086] It should be recognized that embodiments of the present invention may be implemented or carried out by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable storage medium.

[0087] The method can be implemented using standard programming techniques, including a non-transitory computer-readable storage medium configured with a computer program in the computer program, wherein the storage medium is configured such that the computer operates in a specific and predefined manner.

[0088] Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system; however, if required, the program can be implemented in assembly or machine language.

[0089] In any case, the language can be either compiled or interpreted.

[0090] Furthermore, for this purpose, the program can run on programmed application-specific integrated circuits.

[0091] The processes described herein (or variations and / or combinations thereof) can be executed under the control of one or more computer systems configured with executable instructions, and can be implemented by hardware or a combination thereof as code (e.g., executable instructions, one or more computer programs, or one or more applications) that commonly executes on one or more processors. The computer program includes a plurality of instructions executable by one or more processors.

[0092] Furthermore, the method can be implemented in any suitable computing platform, including but not limited to personal computers, minicomputers, mainframes, workstations, networked or distributed computing environments, standalone or integrated computer platforms, or in communication with charged particle tools or other imaging devices.

[0093] Various aspects of the present invention can be implemented in machine-readable code stored on a non-transitory storage medium or device, whether portable or integrated into a computing platform, such as a hard disk, optical read and / or write storage medium, RAM, ROM, etc., such that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the processes described herein.

[0094] Furthermore, machine-readable code, or parts thereof, can be transmitted via wired or wireless networks.

[0095] When such media includes instructions or programs that combine with a microprocessor or other data processor to implement the steps described above, the invention described herein includes these and other different types of non-transitory computer-readable storage media.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A vehicle-mounted cooperative RTK localization method based on observation correlation modeling, characterized in that, include: Acquire dual-antenna GNSS observation data from the main vehicle and the cooperating vehicle, wherein the main vehicle is equipped with a first antenna and a second antenna, and the cooperating vehicle is equipped with a third antenna and a fourth antenna; A unified double-difference observation model is constructed, which includes double-difference observations of baseline AB, baseline BC, and baseline CD; Based on the independence assumption of the original carrier phase observations and the double difference operation rule, the covariance matrix of the double difference observations of the baseline AB, the baseline BC and the baseline CD is derived, and a joint observation equation is established based on the unified double difference observation model and the covariance matrix. The ambiguity floating-point solution and the baseline floating-point solution are obtained by least squares calculation based on the joint observation equation, and the joint variance-covariance matrix is ​​constructed based on the correlation matrix corresponding to the covariance matrix. Joint ambiguity resolution is performed based on the joint variance-covariance matrix, the prior length truth value of the baseline AB, and the prior length truth value of the baseline CD.

2. The vehicle-mounted cooperative RTK positioning method based on observation correlation modeling according to claim 1, characterized in that, The baseline AB is the baseline between the first antenna and the second antenna, the baseline CD is the baseline between the third antenna and the fourth antenna, and the baseline BC is the baseline between the second antenna and the third antenna.

3. The vehicle-mounted cooperative RTK positioning method based on observation correlation modeling according to claim 1, characterized in that, The correlation matrix is ​​a 3×3 matrix, with diagonal elements being 1, the correlation element corresponding to the relationship between baseline AB and baseline BC being -1 / 2, the correlation element corresponding to the relationship between baseline BC and baseline CD being -1 / 2, and the remaining off-diagonal elements being 0.

4. The vehicle-mounted cooperative RTK positioning method based on observation correlation modeling according to claim 1, characterized in that, The joint variance-covariance matrix is ​​constructed by performing a Kronecker product between the correlation matrix and the single baseline variance-covariance matrix.

5. The vehicle-mounted cooperative RTK positioning method based on observation correlation modeling according to claim 1, characterized in that, The joint fuzziness resolution includes constructing an objective function, which includes a joint fuzziness search term, a first length constraint term, and a second length constraint term.

6. The vehicle-mounted cooperative RTK positioning method based on observation correlation modeling according to claim 5, characterized in that, An integer least squares search is performed on the objective function to obtain a fixed ambiguity.

7. The vehicle-mounted cooperative RTK positioning method based on observation correlation modeling according to claim 6, characterized in that, The integer least squares search includes performing a decorrelation transformation on the joint variance-covariance matrix, performing an integer combination search in the decorrelation space, and determining the candidate combination with the smallest objective function value as the fixed ambiguity.

8. The vehicle-mounted cooperative RTK positioning method based on observation correlation modeling according to claim 7, characterized in that, The first length constraint term and the second length constraint term are calculated using the projection method; The projection method includes calculating the unconstrained baseline vector after fixing the ambiguity; The constrained solution is obtained by projecting the unconstrained baseline vector onto a sphere with a length equal to the true value of the prior length.

9. The vehicle-mounted cooperative RTK positioning method based on observation correlation modeling according to claim 1, characterized in that, The fixed solution of the baseline AB is calculated based on the fixed ambiguity of the baseline AB and the prior length truth value of the baseline AB; The fixed solution of the baseline CD is calculated based on the fixed ambiguity of the baseline CD and the prior length truth value of the baseline CD.

10. A vehicle-mounted cooperative RTK positioning system based on observation correlation modeling, based on the vehicle-mounted cooperative RTK positioning method based on observation correlation modeling as described in any one of claims 1 to 9, characterized in that: The data acquisition module acquires dual-antenna GNSS observation data from the main vehicle and the cooperating vehicle. The main vehicle is equipped with a first antenna and a second antenna, and the cooperating vehicle is equipped with a third antenna and a fourth antenna. The model building module constructs a unified double-difference observation model, which includes double-difference observations of baselines AB, BC, and CD. The covariance derivation module derives the covariance matrix of the double-difference observations of the baselines AB, BC, and CD based on the independence assumption of the original carrier phase observations and the double-difference operation rules, and establishes a joint observation equation based on the unified double-difference observation model and the covariance matrix. The floating-point solution module performs least-squares solution to obtain the ambiguity floating-point solution and the baseline floating-point solution based on the joint observation equation, and constructs the joint variance-covariance matrix based on the correlation matrix corresponding to the covariance matrix. The joint fuzzy resolution module performs joint fuzziness resolution based on the joint variance-covariance matrix, the prior length truth value of the baseline AB, and the prior length truth value of the baseline CD.

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